{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eAWwICVEM_3x"
   },
   "source": [
    "# TableMage Demonstration\n",
    "\n",
    "TableMage is a Python package for low-code/conversational data science. In this notebook, we provide a few examples of how the package can be used.\n",
    "\n",
    "## Notebook Contents\n",
    "\n",
    "1. Introduction\n",
    "2. Exploratory Data Analysis\n",
    "3. Regression Analysis\n",
    "4. Causal Inference\n",
    "5. Machine Learning\n",
    "6. Conversational Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_n9HUVQ1Nwye"
   },
   "source": [
    "# Section 1: Introduction\n",
    "\n",
    "## 1.1: Installation\n",
    "\n",
    "Let's first install the package.\n",
    "On a local machine, you simply need to copy-and-paste the following code into your terminal:\n",
    "```{bash}\n",
    "git clone https://github.com/ajy25/TableMage.git\n",
    "cd TableMage\n",
    "pip install .\n",
    "```\n",
    "If you want to use the conversational data analysis mode, you should replace the last line with the following line:\n",
    "```\n",
    "pip install '.[agents]'\n",
    "```\n",
    "NOTE: If you are a MacOS user, you'll need to install [libomp](https://formulae.brew.sh/formula/libomp). It's a dependency for using XGBoost, a TableMage dependency.\n",
    "\n",
    "Okay! Let's run the cell below. On Google Colab, you'll be prompted to restart the session—this is normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using_google_colab = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If using Colab, copy and paste the following code block into a cell below and run it.\n",
    "```\n",
    "%%capture\n",
    "!pip uninstall -y tablemage\n",
    "!rm -rf TableMage\n",
    "!git clone https://github.com/ajy25/TableMage.git\n",
    "%cd TableMage\n",
    "!pip install '.[agents]'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python package for low-code/conversational clinical data science.\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "project_dir = Path.cwd().parent.resolve()\n",
    "import sys\n",
    "\n",
    "sys.path.append(str(project_dir))\n",
    "import tablemage as tm\n",
    "\n",
    "print(tm.__description__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZI5H04BPPYAg"
   },
   "source": [
    "## 1.2 The Analyzer Class\n",
    "\n",
    "Now that TableMage is installed, let's try importing the package. We'll use a toy dataset from scikit-learn for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "id": "TpU2XO20NvxC",
    "outputId": "7f2886ff-ebaa-4516-9ae0-1a7b671335d4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "import pandas as pd\n",
    "\n",
    "brca_dataset = load_breast_cancer()\n",
    "df = pd.DataFrame(data=brca_dataset.data, columns=brca_dataset.feature_names)\n",
    "df[\"target\"] = brca_dataset.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fNY3i9A2QTu2"
   },
   "source": [
    "The Analyzer is the bridge between the data and the analysis methods. Most likely, you'll want to do some modeling of some sort, such as linear regression or some type of machine learning regression/classification. As such, the Analyzer splits the data into a train dataset and a withheld test dataset upon initialization.\n",
    "\n",
    "Be careful! The Analyzer will rename variables to make them easily formula-compatible (i.e., replace spaces and other prohibited characters with underscores). It is recommended that you remove spaces and special characters from variable names *before* you initialize an Analyzer, just to make sure you have full control over the names. A good rule-of-thumb is to avoid punctuation and spaces, with the exceptions of \"_\" and \".\", which are totally fine. We'll let Analyzer handle the renaming for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jl3xiFhJM8zw",
    "outputId": "282d433e-56e5-4369-9991-de2b7cead761"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mUPDT: \u001b[0mRenamed variables \u001b[95m'area error', \u001b[0m\u001b[95m'compactness error', \u001b[0m\u001b[95m'concave points error', \n",
      "      \u001b[0m\u001b[95m'concavity error', \u001b[0m\u001b[95m'fractal dimension error', \u001b[0m\u001b[95m'mean area', \u001b[0m\u001b[95m'mean compactness', \n",
      "      \u001b[0m\u001b[95m'mean concave points', \u001b[0m\u001b[95m'mean concavity', \u001b[0m\u001b[95m'mean fractal dimension', \u001b[0m\u001b[95m'mean \n",
      "      perimeter', \u001b[0m\u001b[95m'mean radius', \u001b[0m\u001b[95m'mean smoothness', \u001b[0m\u001b[95m'mean symmetry', \u001b[0m\u001b[95m'mean texture', \n",
      "      \u001b[0m\u001b[95m'perimeter error', \u001b[0m\u001b[95m'radius error', \u001b[0m\u001b[95m'smoothness error', \u001b[0m\u001b[95m'symmetry error', \u001b[0m\u001b[95m'texture \n",
      "      error', \u001b[0m\u001b[95m'worst area', \u001b[0m\u001b[95m'worst compactness', \u001b[0m\u001b[95m'worst concave points', \u001b[0m\u001b[95m'worst \n",
      "      concavity', \u001b[0m\u001b[95m'worst fractal dimension', \u001b[0m\u001b[95m'worst perimeter', \u001b[0m\u001b[95m'worst radius', \u001b[0m\u001b[95m'worst \n",
      "      smoothness', \u001b[0m\u001b[95m'worst symmetry', \u001b[0m\u001b[95m'worst texture'\u001b[0m to \u001b[95m'area_error', \n",
      "      \u001b[0m\u001b[95m'compactness_error', \u001b[0m\u001b[95m'concave_points_error', \u001b[0m\u001b[95m'concavity_error', \n",
      "      \u001b[0m\u001b[95m'fractal_dimension_error', \u001b[0m\u001b[95m'mean_area', \u001b[0m\u001b[95m'mean_compactness', \u001b[0m\u001b[95m'mean_concave_points', \n",
      "      \u001b[0m\u001b[95m'mean_concavity', \u001b[0m\u001b[95m'mean_fractal_dimension', \u001b[0m\u001b[95m'mean_perimeter', \u001b[0m\u001b[95m'mean_radius', \n",
      "      \u001b[0m\u001b[95m'mean_smoothness', \u001b[0m\u001b[95m'mean_symmetry', \u001b[0m\u001b[95m'mean_texture', \u001b[0m\u001b[95m'perimeter_error', \n",
      "      \u001b[0m\u001b[95m'radius_error', \u001b[0m\u001b[95m'smoothness_error', \u001b[0m\u001b[95m'symmetry_error', \u001b[0m\u001b[95m'texture_error', \n",
      "      \u001b[0m\u001b[95m'worst_area', \u001b[0m\u001b[95m'worst_compactness', \u001b[0m\u001b[95m'worst_concave_points', \u001b[0m\u001b[95m'worst_concavity', \n",
      "      \u001b[0m\u001b[95m'worst_fractal_dimension', \u001b[0m\u001b[95m'worst_perimeter', \u001b[0m\u001b[95m'worst_radius', \u001b[0m\u001b[95m'worst_smoothness', \n",
      "      \u001b[0m\u001b[95m'worst_symmetry', \u001b[0m\u001b[95m'worst_texture'\u001b[0m. \n",
      "\u001b[92mUPDT: \u001b[0mAnalyzer initialized for dataset \u001b[93m'Breast Cancer'\u001b[0m. \n"
     ]
    }
   ],
   "source": [
    "analyzer = tm.Analyzer(\n",
    "    df, test_size=0.2, split_seed=42, verbose=True, name=\"Breast Cancer\"\n",
    ")\n",
    "\n",
    "# You can also split the dataset yourself, e.g. ...\n",
    "# df_train, df_test = sklearn.train_test_split(df, random_state=42)\n",
    "# analyzer = tm.Analyzer(df_train, df_test=df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NctfBOyBTDGO"
   },
   "source": [
    "TableMage is designed for notebooks. Many objects are display-friendly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gc49sk8xS_3f",
    "outputId": "f34d1e9a-1811-4b99-b6d3-bdc7514c1267"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mBreast Cancer\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTrain shape: \u001b[0m\u001b[93m(455, 31)\u001b[0m                      \u001b[1mTest shape: \u001b[0m\u001b[93m(114, 31)\u001b[0m                       \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mCategorical variables:\u001b[0m\n",
       "  \u001b[93mNone\u001b[0m \n",
       "                                                                                        \n",
       "\u001b[1mNumeric variables:\u001b[0m\n",
       "  \u001b[95m'area_error', \u001b[0m\u001b[95m'compactness_error', \u001b[0m\u001b[95m'concave_points_error', \u001b[0m\u001b[95m'concavity_error', \n",
       "  \u001b[0m\u001b[95m'fractal_dimension_error', \u001b[0m\u001b[95m'mean_area', \u001b[0m\u001b[95m'mean_compactness', \u001b[0m\u001b[95m'mean_concave_points', \n",
       "  \u001b[0m\u001b[95m'mean_concavity', \u001b[0m\u001b[95m'mean_fractal_dimension', \u001b[0m\u001b[95m'mean_perimeter', \u001b[0m\u001b[95m'mean_radius', \n",
       "  \u001b[0m\u001b[95m'mean_smoothness', \u001b[0m\u001b[95m'mean_symmetry', \u001b[0m\u001b[95m'mean_texture', \u001b[0m\u001b[95m'perimeter_error', \u001b[0m\u001b[95m'radius_error', \n",
       "  \u001b[0m\u001b[95m'smoothness_error', \u001b[0m\u001b[95m'symmetry_error', \u001b[0m\u001b[95m'target', \u001b[0m\u001b[95m'texture_error', \u001b[0m\u001b[95m'worst_area', \n",
       "  \u001b[0m\u001b[95m'worst_compactness', \u001b[0m\u001b[95m'worst_concave_points', \u001b[0m\u001b[95m'worst_concavity', \n",
       "  \u001b[0m\u001b[95m'worst_fractal_dimension', \u001b[0m\u001b[95m'worst_perimeter', \u001b[0m\u001b[95m'worst_radius', \u001b[0m\u001b[95m'worst_smoothness', \n",
       "  \u001b[0m\u001b[95m'worst_symmetry', \u001b[0m\u001b[95m'worst_texture'\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UcMRobVMRPlF"
   },
   "source": [
    "Before we proceed, let's discuss why TableMage requires train-test splitting upon initialization. TableMage aims to accelerate data science on tabular data. Often, the end goal is to train a model to predict a target, such as whether or not a patient has breast cancer based on geometrical features, or a patient's billing amoung given the health insurer and disease type. In these cases, it is incredibly important to think in terms of pipelines, especially when transformations must be made to the data. Transformations—including missing data imputation and feature scaling—must be \"fit\" on the train data only. Performing data transformations based on the entire dataset is a common mistake, even for experienced data scientists.\n",
    "\n",
    "TableMage handles all of this for you. You can explore the dataset (looking at the entire dataset or only the train or test dataset—your choice), make transformations such as imputation, feature engineering, and scaling, and immediately train a model to predict a target variable, without needing to worry about all the intermediate details! Hopefully, this will be more clear in section 5, when we discuss machine learning.\n",
    "\n",
    "If you don't plan on doing any modeling, simply set test_size to 0.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wAtZltXuT2g1"
   },
   "source": [
    "## 1.3 Submodules\n",
    "\n",
    "TableMage has two submodules:\n",
    "1. ml: Machine learning models\n",
    "2. fs: Feature selection models\n",
    "\n",
    "Let's print an object from each. These will be discussed in greater detail in section 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "oZeIB5TwT1_Y",
    "outputId": "15251e55-c69d-4808-c74a-7d7ab1696441"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearC(l2)\n",
      "BorutaFSR\n"
     ]
    }
   ],
   "source": [
    "print(tm.ml.LinearC())\n",
    "print(tm.fs.BorutaFSR())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GntK4oMpWL_R"
   },
   "source": [
    "# Section 2: Exploratory Data Analysis\n",
    "\n",
    "Let's explore a dataset. We'll use the Kaggle House Prices dataset as an example since it contains a good amount of categorical and numeric features with varying levels of missingness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BB6_W0PgRPSR",
    "outputId": "1495dd27-6cfd-4e36-cd8f-c662a5327c2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mUPDT: \u001b[0mAnalyzer initialized for dataset \u001b[93m'House Prices'\u001b[0m. \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mHouse Prices\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTrain shape: \u001b[0m\u001b[93m(1168, 80)\u001b[0m                      \u001b[1mTest shape: \u001b[0m\u001b[93m(292, 80)\u001b[0m                      \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mCategorical variables:\u001b[0m\n",
       "  \u001b[95m'Alley', \u001b[0m\u001b[95m'BldgType', \u001b[0m\u001b[95m'BsmtCond', \u001b[0m\u001b[95m'BsmtExposure', \u001b[0m\u001b[95m'BsmtFinType1', \u001b[0m\u001b[95m'BsmtFinType2', \n",
       "  \u001b[0m\u001b[95m'BsmtQual', \u001b[0m\u001b[95m'CentralAir', \u001b[0m\u001b[95m'Condition1', \u001b[0m\u001b[95m'Condition2', \u001b[0m\u001b[95m'Electrical', \u001b[0m\u001b[95m'ExterCond', \n",
       "  \u001b[0m\u001b[95m'ExterQual', \u001b[0m\u001b[95m'Exterior1st', \u001b[0m\u001b[95m'Exterior2nd', \u001b[0m\u001b[95m'Fence', \u001b[0m\u001b[95m'FireplaceQu', \u001b[0m\u001b[95m'Foundation', \n",
       "  \u001b[0m\u001b[95m'Functional', \u001b[0m\u001b[95m'GarageCond', \u001b[0m\u001b[95m'GarageFinish', \u001b[0m\u001b[95m'GarageQual', \u001b[0m\u001b[95m'GarageType', \u001b[0m\u001b[95m'Heating', \n",
       "  \u001b[0m\u001b[95m'HeatingQC', \u001b[0m\u001b[95m'HouseStyle', \u001b[0m\u001b[95m'KitchenQual', \u001b[0m\u001b[95m'LandContour', \u001b[0m\u001b[95m'LandSlope', \u001b[0m\u001b[95m'LotConfig', \n",
       "  \u001b[0m\u001b[95m'LotShape', \u001b[0m\u001b[95m'MSZoning', \u001b[0m\u001b[95m'MasVnrType', \u001b[0m\u001b[95m'MiscFeature', \u001b[0m\u001b[95m'Neighborhood', \u001b[0m\u001b[95m'PavedDrive', \n",
       "  \u001b[0m\u001b[95m'PoolQC', \u001b[0m\u001b[95m'RoofMatl', \u001b[0m\u001b[95m'RoofStyle', \u001b[0m\u001b[95m'SaleCondition', \u001b[0m\u001b[95m'SaleType', \u001b[0m\u001b[95m'Street', \u001b[0m\u001b[95m'Utilities'\u001b[0m \n",
       "                                                                                        \n",
       "\u001b[1mNumeric variables:\u001b[0m\n",
       "  \u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'3SsnPorch', \u001b[0m\u001b[95m'BedroomAbvGr', \u001b[0m\u001b[95m'BsmtFinSF1', \u001b[0m\u001b[95m'BsmtFinSF2', \n",
       "  \u001b[0m\u001b[95m'BsmtFullBath', \u001b[0m\u001b[95m'BsmtHalfBath', \u001b[0m\u001b[95m'BsmtUnfSF', \u001b[0m\u001b[95m'EnclosedPorch', \u001b[0m\u001b[95m'Fireplaces', \n",
       "  \u001b[0m\u001b[95m'FullBath', \u001b[0m\u001b[95m'GarageArea', \u001b[0m\u001b[95m'GarageCars', \u001b[0m\u001b[95m'GarageYrBlt', \u001b[0m\u001b[95m'GrLivArea', \u001b[0m\u001b[95m'HalfBath', \n",
       "  \u001b[0m\u001b[95m'KitchenAbvGr', \u001b[0m\u001b[95m'LotArea', \u001b[0m\u001b[95m'LotFrontage', \u001b[0m\u001b[95m'LowQualFinSF', \u001b[0m\u001b[95m'MSSubClass', \u001b[0m\u001b[95m'MasVnrArea', \n",
       "  \u001b[0m\u001b[95m'MiscVal', \u001b[0m\u001b[95m'MoSold', \u001b[0m\u001b[95m'OpenPorchSF', \u001b[0m\u001b[95m'OverallCond', \u001b[0m\u001b[95m'OverallQual', \u001b[0m\u001b[95m'PoolArea', \n",
       "  \u001b[0m\u001b[95m'SalePrice', \u001b[0m\u001b[95m'ScreenPorch', \u001b[0m\u001b[95m'TotRmsAbvGrd', \u001b[0m\u001b[95m'TotalBsmtSF', \u001b[0m\u001b[95m'WoodDeckSF', \u001b[0m\u001b[95m'YearBuilt', \n",
       "  \u001b[0m\u001b[95m'YearRemodAdd', \u001b[0m\u001b[95m'YrSold'\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.dpi\"] = 150\n",
    "\n",
    "if using_google_colab:\n",
    "    curr_dir = Path.cwd()\n",
    "else:\n",
    "    curr_dir = Path(\"__notebook__\").resolve().parent\n",
    "\n",
    "data_path = curr_dir.parent / \"demo\" / \"regression\" / \"house_price_data\" / \"data.csv\"\n",
    "df = pd.read_csv(data_path, index_col=0)\n",
    "analyzer = tm.Analyzer(\n",
    "    df, test_size=0.2, split_seed=42, verbose=True, name=\"House Prices\"\n",
    ")\n",
    "analyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9wO41RUMXKGR"
   },
   "source": [
    "## 2.1 Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MJwmBmdflS2L"
   },
   "source": [
    "We can begin our analysis by using TableMage to make plots of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "id": "VXE_iNJtQQR4",
    "outputId": "263ead1f-7fdb-490e-e1a2-cc71eb4edc96"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda().plot(\"SalePrice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "imuGgltYXURD"
   },
   "source": [
    "By default, Analyzer considers the entire dataset (train and test) for exploratory analysis. You can change this by specifying which dataset you would like to consider in the `analyzer.eda()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "id": "Bfgg7icFXP5_",
    "outputId": "e0da3188-f934-4a28-efad-dc6c763cbc6d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda(\"train\").plot(\"SalePrice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_BHAQYidXvuY"
   },
   "source": [
    "Let's plot the sale price versus 1st floor square footage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 479
    },
    "id": "_CD5f8t-XIgA",
    "outputId": "b4e83082-c33f-4588-d3d8-38c5747be9d6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda().plot(\"SalePrice\", \"1stFlrSF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bFcoVptrYAG1"
   },
   "source": [
    "You can plot any two variables agains each other, regardless of whether they are numeric or categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "id": "538MldzwX4kI",
    "outputId": "80fcb125-850e-4ccf-b2f4-66667cb4a752"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1650x750 with 2 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, figsize=(11, 5))\n",
    "analyzer.eda().plot(\"SalePrice\", \"Alley\", ax=axs[0])\n",
    "analyzer.eda().plot(\"Alley\", \"Heating\", ax=axs[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bAmyeUuCYZ07"
   },
   "source": [
    "You can change colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "id": "bLn1dTDWX9wp",
    "outputId": "a7cdbf59-5438-4ab9-e69b-f2af8e3e82d2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1650x750 with 2 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.options.plot_options.set_color_map(\"viridis\")\n",
    "tm.options.plot_options.set_bar_color(\"red\")\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(11, 5))\n",
    "analyzer.eda().plot(\"SalePrice\", \"Alley\", ax=axs[0])\n",
    "analyzer.eda().plot(\"Alley\", \"Heating\", ax=axs[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jco5f1-BZ4jK"
   },
   "source": [
    "You can make pair plots, like in R. Hypothesis tests are automatically selected based on data normality and homoskedasticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "69em5vTXYnnq",
    "outputId": "dab3c02d-1e7b-473a-932c-4316ba5325d4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 25 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.options.plot_options.set_to_defaults()\n",
    "tm.options.plot_options.set_font_sizes(\n",
    "    axis_title=8, major_ticklabel=6, minor_ticklabel=5\n",
    ")\n",
    "analyzer.eda().plot_pairs(\n",
    "    [\"SalePrice\", \"Alley\", \"Heating\", \"1stFlrSF\", \"2ndFlrSF\"],\n",
    "    htest=True,\n",
    "    figsize=(10, 10),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aW9NF4obbWfL"
   },
   "source": [
    "## 2.2 Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j4AeBOtNlS2M"
   },
   "source": [
    "We can display all the basic statistics of each numerical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "UchAMgzVZFnK",
    "outputId": "02a7dcb0-1962-46b4-a5b5-9f0e96ed6d6a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Statistic</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>variance</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "      <th>q1</th>\n",
       "      <th>median</th>\n",
       "      <th>q3</th>\n",
       "      <th>n_missing</th>\n",
       "      <th>missing_rate</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>334.0</td>\n",
       "      <td>4692.0</td>\n",
       "      <td>1162.627</td>\n",
       "      <td>386.588</td>\n",
       "      <td>1.494501e+05</td>\n",
       "      <td>1.375</td>\n",
       "      <td>5.722</td>\n",
       "      <td>882.00</td>\n",
       "      <td>1087.0</td>\n",
       "      <td>1391.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>346.992</td>\n",
       "      <td>436.528</td>\n",
       "      <td>1.905571e+05</td>\n",
       "      <td>0.812</td>\n",
       "      <td>-0.556</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>728.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>0.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>3.410</td>\n",
       "      <td>29.317</td>\n",
       "      <td>8.595060e+02</td>\n",
       "      <td>10.294</td>\n",
       "      <td>123.235</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.866</td>\n",
       "      <td>0.816</td>\n",
       "      <td>6.650000e-01</td>\n",
       "      <td>0.212</td>\n",
       "      <td>2.219</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5644.0</td>\n",
       "      <td>443.640</td>\n",
       "      <td>456.098</td>\n",
       "      <td>2.080255e+05</td>\n",
       "      <td>1.684</td>\n",
       "      <td>11.076</td>\n",
       "      <td>0.00</td>\n",
       "      <td>383.5</td>\n",
       "      <td>712.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>46.549</td>\n",
       "      <td>161.319</td>\n",
       "      <td>2.602391e+04</td>\n",
       "      <td>4.251</td>\n",
       "      <td>20.040</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.519</td>\n",
       "      <td>2.690000e-01</td>\n",
       "      <td>0.595</td>\n",
       "      <td>-0.840</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.239</td>\n",
       "      <td>5.700000e-02</td>\n",
       "      <td>4.099</td>\n",
       "      <td>16.336</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>567.240</td>\n",
       "      <td>441.867</td>\n",
       "      <td>1.952464e+05</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.469</td>\n",
       "      <td>223.00</td>\n",
       "      <td>477.5</td>\n",
       "      <td>808.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>0.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>21.954</td>\n",
       "      <td>61.119</td>\n",
       "      <td>3.735550e+03</td>\n",
       "      <td>3.087</td>\n",
       "      <td>10.391</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.645</td>\n",
       "      <td>4.160000e-01</td>\n",
       "      <td>0.649</td>\n",
       "      <td>-0.221</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.565</td>\n",
       "      <td>0.551</td>\n",
       "      <td>3.040000e-01</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-0.858</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>472.980</td>\n",
       "      <td>213.805</td>\n",
       "      <td>4.571251e+04</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.910</td>\n",
       "      <td>334.50</td>\n",
       "      <td>480.0</td>\n",
       "      <td>576.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.767</td>\n",
       "      <td>0.747</td>\n",
       "      <td>5.580000e-01</td>\n",
       "      <td>-0.342</td>\n",
       "      <td>0.216</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>1900.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1978.506</td>\n",
       "      <td>24.690</td>\n",
       "      <td>6.095830e+02</td>\n",
       "      <td>-0.649</td>\n",
       "      <td>-0.421</td>\n",
       "      <td>1961.00</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>2002.00</td>\n",
       "      <td>81</td>\n",
       "      <td>0.055</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>334.0</td>\n",
       "      <td>5642.0</td>\n",
       "      <td>1515.464</td>\n",
       "      <td>525.480</td>\n",
       "      <td>2.761296e+05</td>\n",
       "      <td>1.365</td>\n",
       "      <td>4.874</td>\n",
       "      <td>1129.50</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>1776.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.503</td>\n",
       "      <td>2.530000e-01</td>\n",
       "      <td>0.675</td>\n",
       "      <td>-1.077</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.220</td>\n",
       "      <td>4.900000e-02</td>\n",
       "      <td>4.484</td>\n",
       "      <td>21.455</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>1300.0</td>\n",
       "      <td>215245.0</td>\n",
       "      <td>10516.828</td>\n",
       "      <td>9981.265</td>\n",
       "      <td>9.962565e+07</td>\n",
       "      <td>12.195</td>\n",
       "      <td>202.544</td>\n",
       "      <td>7553.50</td>\n",
       "      <td>9478.5</td>\n",
       "      <td>11601.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>21.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>70.050</td>\n",
       "      <td>24.285</td>\n",
       "      <td>5.897490e+02</td>\n",
       "      <td>2.161</td>\n",
       "      <td>17.375</td>\n",
       "      <td>59.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>259</td>\n",
       "      <td>0.177</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>0.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>5.845</td>\n",
       "      <td>48.623</td>\n",
       "      <td>2.364204e+03</td>\n",
       "      <td>9.002</td>\n",
       "      <td>82.946</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>20.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>56.897</td>\n",
       "      <td>42.301</td>\n",
       "      <td>1.789338e+03</td>\n",
       "      <td>1.406</td>\n",
       "      <td>1.571</td>\n",
       "      <td>20.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>103.685</td>\n",
       "      <td>181.066</td>\n",
       "      <td>3.278497e+04</td>\n",
       "      <td>2.666</td>\n",
       "      <td>10.044</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.00</td>\n",
       "      <td>8</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15500.0</td>\n",
       "      <td>43.489</td>\n",
       "      <td>496.123</td>\n",
       "      <td>2.461381e+05</td>\n",
       "      <td>24.452</td>\n",
       "      <td>698.601</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.322</td>\n",
       "      <td>2.704</td>\n",
       "      <td>7.310000e+00</td>\n",
       "      <td>0.212</td>\n",
       "      <td>-0.407</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>0.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>46.660</td>\n",
       "      <td>66.256</td>\n",
       "      <td>4.389861e+03</td>\n",
       "      <td>2.362</td>\n",
       "      <td>8.457</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.575</td>\n",
       "      <td>1.113</td>\n",
       "      <td>1.238000e+00</td>\n",
       "      <td>0.692</td>\n",
       "      <td>1.099</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.099</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.913000e+00</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.092</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>0.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>2.759</td>\n",
       "      <td>40.177</td>\n",
       "      <td>1.614216e+03</td>\n",
       "      <td>14.813</td>\n",
       "      <td>222.501</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>34900.0</td>\n",
       "      <td>755000.0</td>\n",
       "      <td>180921.196</td>\n",
       "      <td>79442.503</td>\n",
       "      <td>6.311111e+09</td>\n",
       "      <td>1.881</td>\n",
       "      <td>6.510</td>\n",
       "      <td>129975.00</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>214000.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>0.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>15.061</td>\n",
       "      <td>55.757</td>\n",
       "      <td>3.108889e+03</td>\n",
       "      <td>4.118</td>\n",
       "      <td>18.372</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.518</td>\n",
       "      <td>1.625</td>\n",
       "      <td>2.642000e+00</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.874</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6110.0</td>\n",
       "      <td>1057.429</td>\n",
       "      <td>438.705</td>\n",
       "      <td>1.924624e+05</td>\n",
       "      <td>1.523</td>\n",
       "      <td>13.201</td>\n",
       "      <td>795.75</td>\n",
       "      <td>991.5</td>\n",
       "      <td>1298.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>0.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>94.245</td>\n",
       "      <td>125.339</td>\n",
       "      <td>1.570981e+04</td>\n",
       "      <td>1.540</td>\n",
       "      <td>2.979</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>1872.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1971.268</td>\n",
       "      <td>30.203</td>\n",
       "      <td>9.122150e+02</td>\n",
       "      <td>-0.613</td>\n",
       "      <td>-0.442</td>\n",
       "      <td>1954.00</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1984.866</td>\n",
       "      <td>20.645</td>\n",
       "      <td>4.262330e+02</td>\n",
       "      <td>-0.503</td>\n",
       "      <td>-1.272</td>\n",
       "      <td>1967.00</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>2004.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2007.816</td>\n",
       "      <td>1.328</td>\n",
       "      <td>1.764000e+00</td>\n",
       "      <td>0.096</td>\n",
       "      <td>-1.191</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Statistic          min       max        mean        std      variance    skew  \\\n",
       "Variable                                                                        \n",
       "1stFlrSF         334.0    4692.0    1162.627    386.588  1.494501e+05   1.375   \n",
       "2ndFlrSF           0.0    2065.0     346.992    436.528  1.905571e+05   0.812   \n",
       "3SsnPorch          0.0     508.0       3.410     29.317  8.595060e+02  10.294   \n",
       "BedroomAbvGr       0.0       8.0       2.866      0.816  6.650000e-01   0.212   \n",
       "BsmtFinSF1         0.0    5644.0     443.640    456.098  2.080255e+05   1.684   \n",
       "BsmtFinSF2         0.0    1474.0      46.549    161.319  2.602391e+04   4.251   \n",
       "BsmtFullBath       0.0       3.0       0.425      0.519  2.690000e-01   0.595   \n",
       "BsmtHalfBath       0.0       2.0       0.058      0.239  5.700000e-02   4.099   \n",
       "BsmtUnfSF          0.0    2336.0     567.240    441.867  1.952464e+05   0.919   \n",
       "EnclosedPorch      0.0     552.0      21.954     61.119  3.735550e+03   3.087   \n",
       "Fireplaces         0.0       3.0       0.613      0.645  4.160000e-01   0.649   \n",
       "FullBath           0.0       3.0       1.565      0.551  3.040000e-01   0.037   \n",
       "GarageArea         0.0    1418.0     472.980    213.805  4.571251e+04   0.180   \n",
       "GarageCars         0.0       4.0       1.767      0.747  5.580000e-01  -0.342   \n",
       "GarageYrBlt     1900.0    2010.0    1978.506     24.690  6.095830e+02  -0.649   \n",
       "GrLivArea        334.0    5642.0    1515.464    525.480  2.761296e+05   1.365   \n",
       "HalfBath           0.0       2.0       0.383      0.503  2.530000e-01   0.675   \n",
       "KitchenAbvGr       0.0       3.0       1.047      0.220  4.900000e-02   4.484   \n",
       "LotArea         1300.0  215245.0   10516.828   9981.265  9.962565e+07  12.195   \n",
       "LotFrontage       21.0     313.0      70.050     24.285  5.897490e+02   2.161   \n",
       "LowQualFinSF       0.0     572.0       5.845     48.623  2.364204e+03   9.002   \n",
       "MSSubClass        20.0     190.0      56.897     42.301  1.789338e+03   1.406   \n",
       "MasVnrArea         0.0    1600.0     103.685    181.066  3.278497e+04   2.666   \n",
       "MiscVal            0.0   15500.0      43.489    496.123  2.461381e+05  24.452   \n",
       "MoSold             1.0      12.0       6.322      2.704  7.310000e+00   0.212   \n",
       "OpenPorchSF        0.0     547.0      46.660     66.256  4.389861e+03   2.362   \n",
       "OverallCond        1.0       9.0       5.575      1.113  1.238000e+00   0.692   \n",
       "OverallQual        1.0      10.0       6.099      1.383  1.913000e+00   0.217   \n",
       "PoolArea           0.0     738.0       2.759     40.177  1.614216e+03  14.813   \n",
       "SalePrice      34900.0  755000.0  180921.196  79442.503  6.311111e+09   1.881   \n",
       "ScreenPorch        0.0     480.0      15.061     55.757  3.108889e+03   4.118   \n",
       "TotRmsAbvGrd       2.0      14.0       6.518      1.625  2.642000e+00   0.676   \n",
       "TotalBsmtSF        0.0    6110.0    1057.429    438.705  1.924624e+05   1.523   \n",
       "WoodDeckSF         0.0     857.0      94.245    125.339  1.570981e+04   1.540   \n",
       "YearBuilt       1872.0    2010.0    1971.268     30.203  9.122150e+02  -0.613   \n",
       "YearRemodAdd    1950.0    2010.0    1984.866     20.645  4.262330e+02  -0.503   \n",
       "YrSold          2006.0    2010.0    2007.816      1.328  1.764000e+00   0.096   \n",
       "\n",
       "Statistic      kurtosis         q1    median         q3  n_missing  \\\n",
       "Variable                                                             \n",
       "1stFlrSF          5.722     882.00    1087.0    1391.25          0   \n",
       "2ndFlrSF         -0.556       0.00       0.0     728.00          0   \n",
       "3SsnPorch       123.235       0.00       0.0       0.00          0   \n",
       "BedroomAbvGr      2.219       2.00       3.0       3.00          0   \n",
       "BsmtFinSF1       11.076       0.00     383.5     712.25          0   \n",
       "BsmtFinSF2       20.040       0.00       0.0       0.00          0   \n",
       "BsmtFullBath     -0.840       0.00       0.0       1.00          0   \n",
       "BsmtHalfBath     16.336       0.00       0.0       0.00          0   \n",
       "BsmtUnfSF         0.469     223.00     477.5     808.00          0   \n",
       "EnclosedPorch    10.391       0.00       0.0       0.00          0   \n",
       "Fireplaces       -0.221       0.00       1.0       1.00          0   \n",
       "FullBath         -0.858       1.00       2.0       2.00          0   \n",
       "GarageArea        0.910     334.50     480.0     576.00          0   \n",
       "GarageCars        0.216       1.00       2.0       2.00          0   \n",
       "GarageYrBlt      -0.421    1961.00    1980.0    2002.00         81   \n",
       "GrLivArea         4.874    1129.50    1464.0    1776.75          0   \n",
       "HalfBath         -1.077       0.00       0.0       1.00          0   \n",
       "KitchenAbvGr     21.455       1.00       1.0       1.00          0   \n",
       "LotArea         202.544    7553.50    9478.5   11601.50          0   \n",
       "LotFrontage      17.375      59.00      69.0      80.00        259   \n",
       "LowQualFinSF     82.946       0.00       0.0       0.00          0   \n",
       "MSSubClass        1.571      20.00      50.0      70.00          0   \n",
       "MasVnrArea       10.044       0.00       0.0     166.00          8   \n",
       "MiscVal         698.601       0.00       0.0       0.00          0   \n",
       "MoSold           -0.407       5.00       6.0       8.00          0   \n",
       "OpenPorchSF       8.457       0.00      25.0      68.00          0   \n",
       "OverallCond       1.099       5.00       5.0       6.00          0   \n",
       "OverallQual       0.092       5.00       6.0       7.00          0   \n",
       "PoolArea        222.501       0.00       0.0       0.00          0   \n",
       "SalePrice         6.510  129975.00  163000.0  214000.00          0   \n",
       "ScreenPorch      18.372       0.00       0.0       0.00          0   \n",
       "TotRmsAbvGrd      0.874       5.00       6.0       7.00          0   \n",
       "TotalBsmtSF      13.201     795.75     991.5    1298.25          0   \n",
       "WoodDeckSF        2.979       0.00       0.0     168.00          0   \n",
       "YearBuilt        -0.442    1954.00    1973.0    2000.00          0   \n",
       "YearRemodAdd     -1.272    1967.00    1994.0    2004.00          0   \n",
       "YrSold           -1.191    2007.00    2008.0    2009.00          0   \n",
       "\n",
       "Statistic      missing_rate     n  \n",
       "Variable                           \n",
       "1stFlrSF              0.000  1460  \n",
       "2ndFlrSF              0.000  1460  \n",
       "3SsnPorch             0.000  1460  \n",
       "BedroomAbvGr          0.000  1460  \n",
       "BsmtFinSF1            0.000  1460  \n",
       "BsmtFinSF2            0.000  1460  \n",
       "BsmtFullBath          0.000  1460  \n",
       "BsmtHalfBath          0.000  1460  \n",
       "BsmtUnfSF             0.000  1460  \n",
       "EnclosedPorch         0.000  1460  \n",
       "Fireplaces            0.000  1460  \n",
       "FullBath              0.000  1460  \n",
       "GarageArea            0.000  1460  \n",
       "GarageCars            0.000  1460  \n",
       "GarageYrBlt           0.055  1460  \n",
       "GrLivArea             0.000  1460  \n",
       "HalfBath              0.000  1460  \n",
       "KitchenAbvGr          0.000  1460  \n",
       "LotArea               0.000  1460  \n",
       "LotFrontage           0.177  1460  \n",
       "LowQualFinSF          0.000  1460  \n",
       "MSSubClass            0.000  1460  \n",
       "MasVnrArea            0.005  1460  \n",
       "MiscVal               0.000  1460  \n",
       "MoSold                0.000  1460  \n",
       "OpenPorchSF           0.000  1460  \n",
       "OverallCond           0.000  1460  \n",
       "OverallQual           0.000  1460  \n",
       "PoolArea              0.000  1460  \n",
       "SalePrice             0.000  1460  \n",
       "ScreenPorch           0.000  1460  \n",
       "TotRmsAbvGrd          0.000  1460  \n",
       "TotalBsmtSF           0.000  1460  \n",
       "WoodDeckSF            0.000  1460  \n",
       "YearBuilt             0.000  1460  \n",
       "YearRemodAdd          0.000  1460  \n",
       "YrSold                0.000  1460  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda().numeric_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_oSEujaKlS2M"
   },
   "source": [
    "We can also list out all the statistics for categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "WpAGrDfmactN",
    "outputId": "2efbdbb1-bcf1-4963-b058-96946dbf1db9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Statistic</th>\n",
       "      <th>n_unique</th>\n",
       "      <th>most_common</th>\n",
       "      <th>least_common</th>\n",
       "      <th>n_missing</th>\n",
       "      <th>missing_rate</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>2</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Pave</td>\n",
       "      <td>1369</td>\n",
       "      <td>0.937671</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType</th>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2fmCon</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>Po</td>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Mn</td>\n",
       "      <td>38</td>\n",
       "      <td>0.026027</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>6</td>\n",
       "      <td>Unf</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>6</td>\n",
       "      <td>Unf</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>38</td>\n",
       "      <td>0.026027</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>Fa</td>\n",
       "      <td>37</td>\n",
       "      <td>0.025342</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CentralAir</th>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1</th>\n",
       "      <td>9</td>\n",
       "      <td>Norm</td>\n",
       "      <td>RRNe</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2</th>\n",
       "      <td>8</td>\n",
       "      <td>Norm</td>\n",
       "      <td>PosA</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>5</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>Mix</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>Po</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>Fa</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>15</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>ImStucc</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>16</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>4</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>MnWw</td>\n",
       "      <td>1179</td>\n",
       "      <td>0.807534</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>5</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Po</td>\n",
       "      <td>690</td>\n",
       "      <td>0.472603</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation</th>\n",
       "      <td>6</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Wood</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>Sev</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>3</td>\n",
       "      <td>Unf</td>\n",
       "      <td>Fin</td>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>Po</td>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>6</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2Types</td>\n",
       "      <td>81</td>\n",
       "      <td>0.055479</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating</th>\n",
       "      <td>6</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Floor</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeatingQC</th>\n",
       "      <td>5</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Po</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle</th>\n",
       "      <td>8</td>\n",
       "      <td>1Story</td>\n",
       "      <td>2.5Fin</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual</th>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>Fa</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour</th>\n",
       "      <td>4</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Low</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope</th>\n",
       "      <td>3</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Sev</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig</th>\n",
       "      <td>5</td>\n",
       "      <td>Inside</td>\n",
       "      <td>FR3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape</th>\n",
       "      <td>4</td>\n",
       "      <td>Reg</td>\n",
       "      <td>IR3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>5</td>\n",
       "      <td>RL</td>\n",
       "      <td>C (all)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>3</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>BrkCmn</td>\n",
       "      <td>872</td>\n",
       "      <td>0.59726</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>4</td>\n",
       "      <td>Shed</td>\n",
       "      <td>TenC</td>\n",
       "      <td>1406</td>\n",
       "      <td>0.963014</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <td>25</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Blueste</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PavedDrive</th>\n",
       "      <td>3</td>\n",
       "      <td>Y</td>\n",
       "      <td>P</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>3</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Fa</td>\n",
       "      <td>1453</td>\n",
       "      <td>0.995205</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl</th>\n",
       "      <td>8</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Metal</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle</th>\n",
       "      <td>6</td>\n",
       "      <td>Gable</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition</th>\n",
       "      <td>6</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AdjLand</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>9</td>\n",
       "      <td>WD</td>\n",
       "      <td>Con</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street</th>\n",
       "      <td>2</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>2</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>NoSeWa</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Statistic     n_unique most_common least_common  n_missing missing_rate     n\n",
       "Variable                                                                     \n",
       "Alley                2        Grvl         Pave       1369     0.937671  1460\n",
       "BldgType             5        1Fam       2fmCon          0          0.0  1460\n",
       "BsmtCond             4          TA           Po         37     0.025342  1460\n",
       "BsmtExposure         4          No           Mn         38     0.026027  1460\n",
       "BsmtFinType1         6         Unf          LwQ         37     0.025342  1460\n",
       "BsmtFinType2         6         Unf          GLQ         38     0.026027  1460\n",
       "BsmtQual             4          TA           Fa         37     0.025342  1460\n",
       "CentralAir           2           Y            N          0          0.0  1460\n",
       "Condition1           9        Norm         RRNe          0          0.0  1460\n",
       "Condition2           8        Norm         PosA          0          0.0  1460\n",
       "Electrical           5       SBrkr          Mix          1     0.000685  1460\n",
       "ExterCond            5          TA           Po          0          0.0  1460\n",
       "ExterQual            4          TA           Fa          0          0.0  1460\n",
       "Exterior1st         15     VinylSd      ImStucc          0          0.0  1460\n",
       "Exterior2nd         16     VinylSd        Other          0          0.0  1460\n",
       "Fence                4       MnPrv         MnWw       1179     0.807534  1460\n",
       "FireplaceQu          5          Gd           Po        690     0.472603  1460\n",
       "Foundation           6       PConc         Wood          0          0.0  1460\n",
       "Functional           7         Typ          Sev          0          0.0  1460\n",
       "GarageCond           5          TA           Ex         81     0.055479  1460\n",
       "GarageFinish         3         Unf          Fin         81     0.055479  1460\n",
       "GarageQual           5          TA           Po         81     0.055479  1460\n",
       "GarageType           6      Attchd       2Types         81     0.055479  1460\n",
       "Heating              6        GasA        Floor          0          0.0  1460\n",
       "HeatingQC            5          Ex           Po          0          0.0  1460\n",
       "HouseStyle           8      1Story       2.5Fin          0          0.0  1460\n",
       "KitchenQual          4          TA           Fa          0          0.0  1460\n",
       "LandContour          4         Lvl          Low          0          0.0  1460\n",
       "LandSlope            3         Gtl          Sev          0          0.0  1460\n",
       "LotConfig            5      Inside          FR3          0          0.0  1460\n",
       "LotShape             4         Reg          IR3          0          0.0  1460\n",
       "MSZoning             5          RL      C (all)          0          0.0  1460\n",
       "MasVnrType           3     BrkFace       BrkCmn        872      0.59726  1460\n",
       "MiscFeature          4        Shed         TenC       1406     0.963014  1460\n",
       "Neighborhood        25       NAmes      Blueste          0          0.0  1460\n",
       "PavedDrive           3           Y            P          0          0.0  1460\n",
       "PoolQC               3          Gd           Fa       1453     0.995205  1460\n",
       "RoofMatl             8     CompShg        Metal          0          0.0  1460\n",
       "RoofStyle            6       Gable         Shed          0          0.0  1460\n",
       "SaleCondition        6      Normal      AdjLand          0          0.0  1460\n",
       "SaleType             9          WD          Con          0          0.0  1460\n",
       "Street               2        Pave         Grvl          0          0.0  1460\n",
       "Utilities            2      AllPub       NoSeWa          0          0.0  1460"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda().categorical_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ihEmQQN1lS2N"
   },
   "source": [
    "We can compute the correlation matrix for a set of numeric variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "l4M6LpZUafVT",
    "outputId": "016eaa52-971a-44be-ca48-9241ef7534d5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>1.000 (1.000)</td>\n",
       "      <td>0.606 (0.000)</td>\n",
       "      <td>0.319 (0.000)</td>\n",
       "      <td>-0.029 (0.269)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>0.606 (0.000)</td>\n",
       "      <td>1.000 (1.000)</td>\n",
       "      <td>-0.203 (0.000)</td>\n",
       "      <td>-0.014 (0.603)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>0.319 (0.000)</td>\n",
       "      <td>-0.203 (0.000)</td>\n",
       "      <td>1.000 (1.000)</td>\n",
       "      <td>-0.029 (0.273)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>-0.029 (0.269)</td>\n",
       "      <td>-0.014 (0.603)</td>\n",
       "      <td>-0.029 (0.273)</td>\n",
       "      <td>1.000 (1.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                SalePrice        1stFlrSF        2ndFlrSF          YrSold\n",
       "SalePrice   1.000 (1.000)   0.606 (0.000)   0.319 (0.000)  -0.029 (0.269)\n",
       "1stFlrSF    0.606 (0.000)   1.000 (1.000)  -0.203 (0.000)  -0.014 (0.603)\n",
       "2ndFlrSF    0.319 (0.000)  -0.203 (0.000)   1.000 (1.000)  -0.029 (0.273)\n",
       "YrSold     -0.029 (0.269)  -0.014 (0.603)  -0.029 (0.273)   1.000 (1.000)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda().tabulate_correlation_matrix(\n",
    "    [\"SalePrice\", \"1stFlrSF\", \"2ndFlrSF\", \"YrSold\"], htest=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m0ldajawlS2N"
   },
   "source": [
    "Here's the same thing as a figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 603
    },
    "id": "QMtH4r8Zaomi",
    "outputId": "9ad11f0b-498c-4562-fe42-f5848f12bd8d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x900 with 2 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda().plot_correlation_heatmap(\n",
    "    [\"SalePrice\", \"1stFlrSF\", \"2ndFlrSF\", \"YrSold\"], htest=True, figsize=(5, 6)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qz-6Gv-alS2N"
   },
   "source": [
    "Correlations can be compared between a set of numeric variables and a specific numeric variable of interest. Here, we are interested in how square footage and year sold correlate with sale price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "mVIWmF7kawjm",
    "outputId": "d1c1360e-373b-4994-f76d-0d841502e59b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Corr. w SalePrice</th>\n",
       "      <th>p-value (Bonferroni corrected)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>0.606</td>\n",
       "      <td>1.618e-146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>0.319</td>\n",
       "      <td>1.729e-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>-0.029</td>\n",
       "      <td>8.082e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Corr. w SalePrice p-value (Bonferroni corrected)\n",
       "1stFlrSF             0.606                     1.618e-146\n",
       "2ndFlrSF             0.319                      1.729e-35\n",
       "YrSold              -0.029                      8.082e-01"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda().tabulate_correlation_comparison(\n",
    "    numeric_vars=[\"1stFlrSF\", \"2ndFlrSF\", \"YrSold\"],\n",
    "    target=\"SalePrice\",\n",
    "    bonferroni_correction=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uKWcv6rhlS2T"
   },
   "source": [
    "We leverage the Python `tableone` package to make descriptive exploratory tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 537
    },
    "id": "7e2o6vIkbN6y",
    "outputId": "8d74f765-f31e-4b76-cd9a-36a0a0d91825"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">Grouped by GarageFinish</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Fin</th>\n",
       "      <th>RFn</th>\n",
       "      <th>Unf</th>\n",
       "      <th>P-Value</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>1460</td>\n",
       "      <td>352</td>\n",
       "      <td>422</td>\n",
       "      <td>605</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>180921.2 (79442.5)</td>\n",
       "      <td>240052.7 (96960.6)</td>\n",
       "      <td>202068.9 (63536.2)</td>\n",
       "      <td>142156.4 (46498.5)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>One-way ANOVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>1162.6 (386.6)</td>\n",
       "      <td>1326.5 (479.3)</td>\n",
       "      <td>1240.6 (351.5)</td>\n",
       "      <td>1046.0 (298.4)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>One-way ANOVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>347.0 (436.5)</td>\n",
       "      <td>452.3 (503.3)</td>\n",
       "      <td>341.1 (448.2)</td>\n",
       "      <td>304.5 (381.3)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>One-way ANOVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">YrSold, n (%)</th>\n",
       "      <th>2006</th>\n",
       "      <td></td>\n",
       "      <td>314 (21.5)</td>\n",
       "      <td>74 (21.0)</td>\n",
       "      <td>92 (21.8)</td>\n",
       "      <td>133 (22.0)</td>\n",
       "      <td>0.997</td>\n",
       "      <td>Chi-squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td></td>\n",
       "      <td>329 (22.5)</td>\n",
       "      <td>79 (22.4)</td>\n",
       "      <td>93 (22.0)</td>\n",
       "      <td>140 (23.1)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td></td>\n",
       "      <td>304 (20.8)</td>\n",
       "      <td>74 (21.0)</td>\n",
       "      <td>89 (21.1)</td>\n",
       "      <td>118 (19.5)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td></td>\n",
       "      <td>338 (23.2)</td>\n",
       "      <td>85 (24.1)</td>\n",
       "      <td>95 (22.5)</td>\n",
       "      <td>143 (23.6)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td></td>\n",
       "      <td>175 (12.0)</td>\n",
       "      <td>40 (11.4)</td>\n",
       "      <td>53 (12.6)</td>\n",
       "      <td>71 (11.7)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">BldgType, n (%)</th>\n",
       "      <th>1Fam</th>\n",
       "      <td></td>\n",
       "      <td>1220 (83.6)</td>\n",
       "      <td>296 (84.1)</td>\n",
       "      <td>365 (86.5)</td>\n",
       "      <td>505 (83.5)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>Chi-squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2fmCon</th>\n",
       "      <td></td>\n",
       "      <td>31 (2.1)</td>\n",
       "      <td>3 (0.9)</td>\n",
       "      <td>2 (0.5)</td>\n",
       "      <td>17 (2.8)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duplex</th>\n",
       "      <td></td>\n",
       "      <td>52 (3.6)</td>\n",
       "      <td>2 (0.6)</td>\n",
       "      <td>1 (0.2)</td>\n",
       "      <td>37 (6.1)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twnhs</th>\n",
       "      <td></td>\n",
       "      <td>43 (2.9)</td>\n",
       "      <td>2 (0.6)</td>\n",
       "      <td>8 (1.9)</td>\n",
       "      <td>28 (4.6)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TwnhsE</th>\n",
       "      <td></td>\n",
       "      <td>114 (7.8)</td>\n",
       "      <td>49 (13.9)</td>\n",
       "      <td>46 (10.9)</td>\n",
       "      <td>18 (3.0)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />"
      ],
      "text/plain": [
       "                            Grouped by GarageFinish                                                                                                       \n",
       "                                            Missing             Overall                 Fin                 RFn                 Unf P-Value           Test\n",
       "n                                                                  1460                 352                 422                 605                       \n",
       "SalePrice, mean (SD)                              0  180921.2 (79442.5)  240052.7 (96960.6)  202068.9 (63536.2)  142156.4 (46498.5)  <0.001  One-way ANOVA\n",
       "1stFlrSF, mean (SD)                               0      1162.6 (386.6)      1326.5 (479.3)      1240.6 (351.5)      1046.0 (298.4)  <0.001  One-way ANOVA\n",
       "2ndFlrSF, mean (SD)                               0       347.0 (436.5)       452.3 (503.3)       341.1 (448.2)       304.5 (381.3)  <0.001  One-way ANOVA\n",
       "YrSold, n (%)        2006                                    314 (21.5)           74 (21.0)           92 (21.8)          133 (22.0)   0.997    Chi-squared\n",
       "                     2007                                    329 (22.5)           79 (22.4)           93 (22.0)          140 (23.1)                       \n",
       "                     2008                                    304 (20.8)           74 (21.0)           89 (21.1)          118 (19.5)                       \n",
       "                     2009                                    338 (23.2)           85 (24.1)           95 (22.5)          143 (23.6)                       \n",
       "                     2010                                    175 (12.0)           40 (11.4)           53 (12.6)           71 (11.7)                       \n",
       "BldgType, n (%)      1Fam                                   1220 (83.6)          296 (84.1)          365 (86.5)          505 (83.5)  <0.001    Chi-squared\n",
       "                     2fmCon                                    31 (2.1)             3 (0.9)             2 (0.5)            17 (2.8)                       \n",
       "                     Duplex                                    52 (3.6)             2 (0.6)             1 (0.2)            37 (6.1)                       \n",
       "                     Twnhs                                     43 (2.9)             2 (0.6)             8 (1.9)            28 (4.6)                       \n",
       "                     TwnhsE                                   114 (7.8)           49 (13.9)           46 (10.9)            18 (3.0)                       "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.eda().tabulate_tableone(\n",
    "    vars=[\"SalePrice\", \"1stFlrSF\", \"2ndFlrSF\", \"YrSold\", \"BldgType\"],\n",
    "    stratify_by=\"GarageFinish\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UPF56yb7lS2T"
   },
   "source": [
    "# Section 3: Regression Analysis\n",
    "\n",
    "TableMage accelerates regression analyses. Let's start with a basic example on the house prices dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "qZdhXxCPlS2T"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mUPDT: \u001b[0mAnalyzer initialized for dataset \u001b[93m'House Prices'\u001b[0m. \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mHouse Prices\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTrain shape: \u001b[0m\u001b[93m(1168, 80)\u001b[0m                      \u001b[1mTest shape: \u001b[0m\u001b[93m(292, 80)\u001b[0m                      \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mCategorical variables:\u001b[0m\n",
       "  \u001b[95m'Alley', \u001b[0m\u001b[95m'BldgType', \u001b[0m\u001b[95m'BsmtCond', \u001b[0m\u001b[95m'BsmtExposure', \u001b[0m\u001b[95m'BsmtFinType1', \u001b[0m\u001b[95m'BsmtFinType2', \n",
       "  \u001b[0m\u001b[95m'BsmtQual', \u001b[0m\u001b[95m'CentralAir', \u001b[0m\u001b[95m'Condition1', \u001b[0m\u001b[95m'Condition2', \u001b[0m\u001b[95m'Electrical', \u001b[0m\u001b[95m'ExterCond', \n",
       "  \u001b[0m\u001b[95m'ExterQual', \u001b[0m\u001b[95m'Exterior1st', \u001b[0m\u001b[95m'Exterior2nd', \u001b[0m\u001b[95m'Fence', \u001b[0m\u001b[95m'FireplaceQu', \u001b[0m\u001b[95m'Foundation', \n",
       "  \u001b[0m\u001b[95m'Functional', \u001b[0m\u001b[95m'GarageCond', \u001b[0m\u001b[95m'GarageFinish', \u001b[0m\u001b[95m'GarageQual', \u001b[0m\u001b[95m'GarageType', \u001b[0m\u001b[95m'Heating', \n",
       "  \u001b[0m\u001b[95m'HeatingQC', \u001b[0m\u001b[95m'HouseStyle', \u001b[0m\u001b[95m'KitchenQual', \u001b[0m\u001b[95m'LandContour', \u001b[0m\u001b[95m'LandSlope', \u001b[0m\u001b[95m'LotConfig', \n",
       "  \u001b[0m\u001b[95m'LotShape', \u001b[0m\u001b[95m'MSZoning', \u001b[0m\u001b[95m'MasVnrType', \u001b[0m\u001b[95m'MiscFeature', \u001b[0m\u001b[95m'Neighborhood', \u001b[0m\u001b[95m'PavedDrive', \n",
       "  \u001b[0m\u001b[95m'PoolQC', \u001b[0m\u001b[95m'RoofMatl', \u001b[0m\u001b[95m'RoofStyle', \u001b[0m\u001b[95m'SaleCondition', \u001b[0m\u001b[95m'SaleType', \u001b[0m\u001b[95m'Street', \u001b[0m\u001b[95m'Utilities'\u001b[0m \n",
       "                                                                                        \n",
       "\u001b[1mNumeric variables:\u001b[0m\n",
       "  \u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'3SsnPorch', \u001b[0m\u001b[95m'BedroomAbvGr', \u001b[0m\u001b[95m'BsmtFinSF1', \u001b[0m\u001b[95m'BsmtFinSF2', \n",
       "  \u001b[0m\u001b[95m'BsmtFullBath', \u001b[0m\u001b[95m'BsmtHalfBath', \u001b[0m\u001b[95m'BsmtUnfSF', \u001b[0m\u001b[95m'EnclosedPorch', \u001b[0m\u001b[95m'Fireplaces', \n",
       "  \u001b[0m\u001b[95m'FullBath', \u001b[0m\u001b[95m'GarageArea', \u001b[0m\u001b[95m'GarageCars', \u001b[0m\u001b[95m'GarageYrBlt', \u001b[0m\u001b[95m'GrLivArea', \u001b[0m\u001b[95m'HalfBath', \n",
       "  \u001b[0m\u001b[95m'KitchenAbvGr', \u001b[0m\u001b[95m'LotArea', \u001b[0m\u001b[95m'LotFrontage', \u001b[0m\u001b[95m'LowQualFinSF', \u001b[0m\u001b[95m'MSSubClass', \u001b[0m\u001b[95m'MasVnrArea', \n",
       "  \u001b[0m\u001b[95m'MiscVal', \u001b[0m\u001b[95m'MoSold', \u001b[0m\u001b[95m'OpenPorchSF', \u001b[0m\u001b[95m'OverallCond', \u001b[0m\u001b[95m'OverallQual', \u001b[0m\u001b[95m'PoolArea', \n",
       "  \u001b[0m\u001b[95m'SalePrice', \u001b[0m\u001b[95m'ScreenPorch', \u001b[0m\u001b[95m'TotRmsAbvGrd', \u001b[0m\u001b[95m'TotalBsmtSF', \u001b[0m\u001b[95m'WoodDeckSF', \u001b[0m\u001b[95m'YearBuilt', \n",
       "  \u001b[0m\u001b[95m'YearRemodAdd', \u001b[0m\u001b[95m'YrSold'\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = curr_dir.parent / \"demo\" / \"regression\" / \"house_price_data\" / \"data.csv\"\n",
    "df = pd.read_csv(data_path, index_col=0)\n",
    "analyzer = tm.Analyzer(\n",
    "    df, test_size=0.2, split_seed=42, verbose=True, name=\"House Prices\"\n",
    ")\n",
    "analyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_tQ_dc3TlS2T"
   },
   "source": [
    "Let's first preprocess the data some."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "v-jZIu1qlS2T",
    "outputId": "5f6159fd-ae23-4f7c-e3d1-a3d5fd0965bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mUPDT: \u001b[0mDropped 0 rows with missing values from train and 0 rows from test. \n",
      "\u001b[93mNOTE: \u001b[0mNumeric variables \u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'YrSold'\u001b[0m have no missing values. Imputer \n",
      "      will consider all specified variables regardless. \n",
      "\u001b[92mUPDT: \u001b[0mImputed missing values with strategy \u001b[93m'median'\u001b[0m for numeric variables \u001b[95m'1stFlrSF', \n",
      "      \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'YrSold'\u001b[0m. \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mHouse Prices\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTrain shape: \u001b[0m\u001b[93m(1168, 80)\u001b[0m                      \u001b[1mTest shape: \u001b[0m\u001b[93m(292, 80)\u001b[0m                      \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mCategorical variables:\u001b[0m\n",
       "  \u001b[95m'Alley', \u001b[0m\u001b[95m'BldgType', \u001b[0m\u001b[95m'BsmtCond', \u001b[0m\u001b[95m'BsmtExposure', \u001b[0m\u001b[95m'BsmtFinType1', \u001b[0m\u001b[95m'BsmtFinType2', \n",
       "  \u001b[0m\u001b[95m'BsmtQual', \u001b[0m\u001b[95m'CentralAir', \u001b[0m\u001b[95m'Condition1', \u001b[0m\u001b[95m'Condition2', \u001b[0m\u001b[95m'Electrical', \u001b[0m\u001b[95m'ExterCond', \n",
       "  \u001b[0m\u001b[95m'ExterQual', \u001b[0m\u001b[95m'Exterior1st', \u001b[0m\u001b[95m'Exterior2nd', \u001b[0m\u001b[95m'Fence', \u001b[0m\u001b[95m'FireplaceQu', \u001b[0m\u001b[95m'Foundation', \n",
       "  \u001b[0m\u001b[95m'Functional', \u001b[0m\u001b[95m'GarageCond', \u001b[0m\u001b[95m'GarageFinish', \u001b[0m\u001b[95m'GarageQual', \u001b[0m\u001b[95m'GarageType', \u001b[0m\u001b[95m'Heating', \n",
       "  \u001b[0m\u001b[95m'HeatingQC', \u001b[0m\u001b[95m'HouseStyle', \u001b[0m\u001b[95m'KitchenQual', \u001b[0m\u001b[95m'LandContour', \u001b[0m\u001b[95m'LandSlope', \u001b[0m\u001b[95m'LotConfig', \n",
       "  \u001b[0m\u001b[95m'LotShape', \u001b[0m\u001b[95m'MSZoning', \u001b[0m\u001b[95m'MasVnrType', \u001b[0m\u001b[95m'MiscFeature', \u001b[0m\u001b[95m'Neighborhood', \u001b[0m\u001b[95m'PavedDrive', \n",
       "  \u001b[0m\u001b[95m'PoolQC', \u001b[0m\u001b[95m'RoofMatl', \u001b[0m\u001b[95m'RoofStyle', \u001b[0m\u001b[95m'SaleCondition', \u001b[0m\u001b[95m'SaleType', \u001b[0m\u001b[95m'Street', \u001b[0m\u001b[95m'Utilities'\u001b[0m \n",
       "                                                                                        \n",
       "\u001b[1mNumeric variables:\u001b[0m\n",
       "  \u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'3SsnPorch', \u001b[0m\u001b[95m'BedroomAbvGr', \u001b[0m\u001b[95m'BsmtFinSF1', \u001b[0m\u001b[95m'BsmtFinSF2', \n",
       "  \u001b[0m\u001b[95m'BsmtFullBath', \u001b[0m\u001b[95m'BsmtHalfBath', \u001b[0m\u001b[95m'BsmtUnfSF', \u001b[0m\u001b[95m'EnclosedPorch', \u001b[0m\u001b[95m'Fireplaces', \n",
       "  \u001b[0m\u001b[95m'FullBath', \u001b[0m\u001b[95m'GarageArea', \u001b[0m\u001b[95m'GarageCars', \u001b[0m\u001b[95m'GarageYrBlt', \u001b[0m\u001b[95m'GrLivArea', \u001b[0m\u001b[95m'HalfBath', \n",
       "  \u001b[0m\u001b[95m'KitchenAbvGr', \u001b[0m\u001b[95m'LotArea', \u001b[0m\u001b[95m'LotFrontage', \u001b[0m\u001b[95m'LowQualFinSF', \u001b[0m\u001b[95m'MSSubClass', \u001b[0m\u001b[95m'MasVnrArea', \n",
       "  \u001b[0m\u001b[95m'MiscVal', \u001b[0m\u001b[95m'MoSold', \u001b[0m\u001b[95m'OpenPorchSF', \u001b[0m\u001b[95m'OverallCond', \u001b[0m\u001b[95m'OverallQual', \u001b[0m\u001b[95m'PoolArea', \n",
       "  \u001b[0m\u001b[95m'SalePrice', \u001b[0m\u001b[95m'ScreenPorch', \u001b[0m\u001b[95m'TotRmsAbvGrd', \u001b[0m\u001b[95m'TotalBsmtSF', \u001b[0m\u001b[95m'WoodDeckSF', \u001b[0m\u001b[95m'YearBuilt', \n",
       "  \u001b[0m\u001b[95m'YearRemodAdd', \u001b[0m\u001b[95m'YrSold'\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.dropna(include_vars=[\"SalePrice\"]).impute(\n",
    "    include_vars=[\"1stFlrSF\", \"2ndFlrSF\", \"YrSold\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dO_5KCs1lS2T"
   },
   "source": [
    "It's really easy to fit an OLS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "jqWZF1L3lS2T",
    "outputId": "10d4226c-e9a8-4ff8-ed09-44c516463647"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mOrdinary Least Squares Regression Report\u001b[0m\n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTarget variable:\u001b[0m\n",
       "  \u001b[95m'SalePrice'\u001b[0m \n",
       "                                                                                        \n",
       "\u001b[1mPredictor variables (3):\u001b[0m\n",
       "  \u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'YrSold'\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mMetrics:\u001b[0m\n",
       "  \u001b[1mTrain (1168)\u001b[0m                               \u001b[1mTest (292)\u001b[0m \n",
       "    R2:       \u001b[93m0.548\u001b[0m                            R2:       \u001b[93m0.636\u001b[0m \n",
       "    Adj. R2:  \u001b[93m0.547\u001b[0m                            Adj. R2:  \u001b[93m0.633\u001b[0m \n",
       "    RMSE:     \u001b[93m51925.911\u001b[0m                        RMSE:     \u001b[93m52810.739\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mCoefficients:\u001b[0m\n",
       "\u001b[1m                            Estimate         Std. Error            p-value \u001b[0m\n",
       "\u001b[1m  Predictor                                                                \u001b[0m\n",
       "  const                  -876198.651        2181846.129              0.688 \n",
       "  1stFlrSF                   137.096             15.182              0.000 \n",
       "  2ndFlrSF                    80.969              6.990              0.000 \n",
       "  YrSold                     432.707           1086.173              0.690 \n",
       "========================================================================================"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = analyzer.ols(target=\"SalePrice\", predictors=[\"1stFlrSF\", \"2ndFlrSF\", \"YrSold\"])\n",
    "report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>OLS Linear Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset</th>\n",
       "      <th>Statistic</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">train</th>\n",
       "      <th>rmse</th>\n",
       "      <td>51925.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mae</th>\n",
       "      <td>34482.403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mape</th>\n",
       "      <td>0.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pearsonr</th>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spearmanr</th>\n",
       "      <td>0.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2</th>\n",
       "      <td>0.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adjr2</th>\n",
       "      <td>0.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_obs</th>\n",
       "      <td>1168.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">test</th>\n",
       "      <th>rmse</th>\n",
       "      <td>52810.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mae</th>\n",
       "      <td>35310.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mape</th>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pearsonr</th>\n",
       "      <td>0.813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spearmanr</th>\n",
       "      <td>0.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2</th>\n",
       "      <td>0.636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adjr2</th>\n",
       "      <td>0.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_obs</th>\n",
       "      <td>292.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   OLS Linear Model\n",
       "Dataset Statistic                  \n",
       "train   rmse              51925.911\n",
       "        mae               34482.403\n",
       "        mape                  0.205\n",
       "        pearsonr              0.740\n",
       "        spearmanr             0.771\n",
       "        r2                    0.548\n",
       "        adjr2                 0.547\n",
       "        n_obs              1168.000\n",
       "test    rmse              52810.739\n",
       "        mae               35310.444\n",
       "        mape                  0.212\n",
       "        pearsonr              0.813\n",
       "        spearmanr             0.806\n",
       "        r2                    0.636\n",
       "        adjr2                 0.633\n",
       "        n_obs               292.000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.metrics(dataset=\"both\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimate (Std. Error)</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-876198.651 (2181846.129)</td>\n",
       "      <td>0.688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>137.096 (15.182)</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>80.969 (6.99)</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>432.707 (1086.173)</td>\n",
       "      <td>0.690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Estimate (Std. Error)  p-value\n",
       "const     -876198.651 (2181846.129)    0.688\n",
       "1stFlrSF           137.096 (15.182)    0.000\n",
       "2ndFlrSF              80.969 (6.99)    0.000\n",
       "YrSold           432.707 (1086.173)    0.690"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.coefs(format=\"coef(se)|pval\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GgqKASYGXDrh00Oy+dCZeJXk6QBWVWOmATHNxXImh6/DnzrIXl+5LgYeCADU3iZXdUrmgMrbffvtt1PXqzKkDQAUFClRV1ufvUOrvVqmgRoG3yt7cY1YjHAW7roNlcWjepx6LGo4EdSZVsKv5Zsrm6eDecUGOowyJDm7d3Cc1bXFNYBQYKHhTQxVlOZSZc1lavbe8r6Myusq6FhYQKOBTyaHmwepEiIIzF6gk+h7RfDq9J1wJsijAV9luYfR8KBDSWBXI6/4dPZeap6bfK9uqcbo5na4jaCok8h7RiQs9L3pdHT3nKleORa+VnhNvh0w9NnX+dKXX/pNQGovWr9Tr4H0NdFJK7yd9vmgsUth7P5X02eAdn//Le4Im6H9HZdF6/hyd9FAJcWHzqfX+VkWByyZ76f2iANX75TLc+q45rgAAIDE5nxkUZSBUUqQmL2pxrwNRBXI66FN3R12nsqNmzZpF/Z0OHIMWi9YBigIVZf9U1qZSUHXl1EGmAikdALk1zwpbJ7Cw+ygqHUSqwYca2+hnHTipNFGliCotdFkKbVtdCXVZcw41P0flegoYvAf/xaXgRpkhbS9WFkDPm7JuCoqUXVU2RGf0FWArgHcZLAVJDz30kA0cdICsBif+A2LdRs+3Om8qC6U5euqCqMyYGrYEUUZYz79KGGOVkuogXgGSmp107drVvs56bfQ6K0OhpTi05py/3FLBrEqF1QBG5XJ6bnXCQeMUPQ6dhFBgpLmEeo00d1ABpd6H+nsFUGqSoYBZQYtOMKjjp54nb+AZi/5ez6tOeLj5ekV5j+h10fOnOVs6UaKDer0Ohc2/Fdc5VK+vAnplyx09dpWH6n9SY1QgpQBIf+MCoFRI5D2i50lZSr3/9BzptXNdWmNR2aoeox6PgnzN2VR3UnXTVKmu6L2skx6aK6j3i9776pyqLKXKYzUvUe8nZYfVzVTPsZ4zzYPUa66TBPp80Xbnzp1rwkBNjtxj1POpAE/NpfR+9wZ5CrT1/+ftBqvGUhL0+amMqb901i2JomykuvMCAIDE5EUwKDog0QGbyu50AK4DW8290RlqBYJBBx3KDngzBI7KTRVM6QBdC5Hr7L8OelTeqINrbUuBgIKHwhR2H8WhNe1UvqUDbB3sal6QDr5UkqYDVVEZpw5U3cGqggsFNMrKucYVJS0V1fOqA2VlvoIouNN8IwVbyiApOFOmQX/nfe508K7HoNdOX3odlUnydo9UAKXATAfOKn9UZkZZWXV4jDXnSc0pFNwpaNXBeSw6UNfzqBMH6jKpDod6nbX2mQIAPd/+OUo6uFX3SmWj9b5Q+aPeIy6ToqBEQZCajChgUvZTmTiVibpgS+PSe0u30TwpZZGUcVPAkcicKAVjeu71musguajvEb0nNGa9VxQY6f71vCfaGEmBVVB5rcal+1HHUdc0RtlXBWHFmbObqETeI8qE6T2m+ZV6H2psWjZGz4kynEHcGqV6THof62SKAhI9Pvc/rOZSagyj5UX0uuo50Vj03leApLJHlTfqPaPmU45OGGg+pV4HlefqxJaCRwXl2U7/A3pN9VyqLF3vN30u6Ln3UsCrzsbe95UrT3YnhAAAQGrssrOo7TMBAFlNJykkVR2NAQBAbsiLOYMAAAAAgGgEgwAAAACQhygTBQAAAIA8RGYQAAAACBF/LofcDoqLYBAAACBPaEksdTz2fqnbsjpKq7O2lrBJtsGDBxe67MsXX3xhb6PvyabtagzZplOnTgVeC++XW6fVO/5t27bZLs1aHsz58ccf7ZJJyZDK1wHZKW+WlgAAAICx651q6RpH69FqCSOte6zlX9xascmi5Yy0NA4K0vPs1iD2c8vraAkorQktq1atsksgaZki5/3337dr2QLFQTAIAACQRypVqmTatGlT4PqOHTvaNW9fe+21pAaDCmRcMIOCa7IGvRZehf0eKAnKRAEAAGDKlStnypYta3bZZZfIdTt27DDDhg0zxx9/vNl///3NiSeeaEaOHBn1d0uWLDFXXnmlad++vWndurU599xzzaRJk+KWiY4aNcpu64ADDjAXXHCBWb58eUKlpf6Sz2XLltls5hFHHGFatmxpg1ldXrduXczHqczaSSedZFq1amUzlnfffbfZtGlT4G2VMW3evLl5/vnno65XCafuT9lU+eyzz8w555xjDjzwQHPIIYeYv/3tb+ann34yyeAesx7rsccea6+75ZZbbJmprh8yZEiB5yaR1y2R1wG5j8wgAABAHlGzkb/++iuqTPTnn382jz76qNm8ebM5/fTTI79ToKRMYe/evW2g8+WXX9o5a7/99pu5+uqrbdCh39WqVcs88MADZtdddzXPPfecDYbee+8906BBgwL3r8DqnnvuMRdddJHNRn7++efmjjvuKPLj2LJli7nwwgtNtWrVbNlr5cqVbbmkgqPddtvN/OMf/yjwN2PHjjWDBg0y/fv3t8HTggULzMCBA+229N1PGc127dqZd955xwZL3tJMPY+nnnqqWbp0qS31PPvss831119vn5v//ve/5oorrjAffvihKVWqVMKvhaPn0U/PsR7bNddcY5/fE044wWYWFbCOHj06qpy0sNctma8Dwo1gEAAAII8oMFBWy0vZwCZNmpiHH37YHHPMMfa6hQsXmldeecUGOApsRBk43Xbo0KGmR48eNpBRQKVgyJWWKsukoEXNToKCn8cee8yccsop5tZbb41sU5k5ZamKYtGiRTb4URC311572es6dOhgZsyYYaZNmxb4N7q+fv365vzzz7dBmgK9ChUqmA0bNsS8HwXHGquyZvXq1bPXKTg87LDDTM2aNe3Pf/zxhw28ateubX+vcX300Ufm999/t2W5sbzxxhv2y0+Bnb88VFlbZSll7733tnM/3X2Ju30ir5vmhSbrdUC4EQwCAADkEQWCAwYMiDQkeeihh8yff/5pv++zzz6R202dOtUGbypH9GavdPnxxx83X3/9tS1b3HfffW1GafLkyTagUJZJZYxBFDiuWbMmEnA6J598cpGDEAVGL774os1OKjBcvHixmT9/vr2PoGybCxYVaKlz6nHHHWcD2NNOOy2qNNZPGTg9X++++665/PLLzYoVK+xjV4ZRVBqrEtuuXbva8lM9fpXMKigujJ4Hl6nzaty4sSmuRF63Ro0aJe11QLgRDAIAAOSRihUr2vlyjoKZLl26mEsvvdSWFqr0UNavX2+/qxQyyMqVK20Q9fTTT9sgQyWRynKVKVPGBloKoHbfffeov3EZOJV2einDVhwjRowwTzzxhB1rjRo17Py48uXLm40bNwbeXpkwBY8KIpUZ0xy7Pffc0/z973+3vwuizJ4ejzKACgYVFOo+dJ0o06iSS83RU7mmymTVCVQZuL59+8YNNJWh874WyZDI6+Ze42S9DggvgsEEbd261Z5FUo25zvgk4sEHH7QfUF6qbb/ttttSNEoAADKL/WX4KIi68847zXXXXWfuu+8+85///CdqaQM1XFEA6edKJlUaqTlqmrc3Z84cO59u+PDhkbl8Xi74UFYqKIBxXACl+YylS5e2P2s+o5fW2rv//vvNjTfeaN9zLsDR4/juu+9iPt7OnTvbLwWMymZqrNrGQQcdFCnz9FOwrJJLZR8VFKrpigJCx1saq8ybso96T2sNR2Xb0imR101zBxN5HZD76CaaAE0q1geLSg+KQouAXnbZZfaDxn1pOwAA5CL2l+Gl8kZ11lSDFTff7uCDD7bf1ZlT2Sv3pU6amluowEENWzR3bubMmTaAU+lmv3797PzDoM6UDRs2NHXr1rUBo9fHH38cddnNs1NzFEdBlpcuK/BRts4FggoYdb2yf0GUqXNlmWo4o0BN8x1VTqmS2VhU/qqgWVm/WbNmRTXZUUdRlVsqENS8PnU0VWMWSXZ3ThcYe/kb1CTyuiX6OiD3EQwW4vvvv7c14N4Po0SppbDq8pVyd1/xJhEDABBW7C/DT41EVOJ577332oycum0qI6b5gE8++aSdi/bSSy/ZLJoCCwUUamKizp1azkEZsy+++MJmemfPnm2zZ34KGFWSqaDj9ttvt4G/MmrarpdrRqOM5ZQpU8yYMWNs9tGb6VI2ThkuZQd1v8oUqjHM6tWr7YmJWHMGx48fb5vOqHvmBx98YAMkPRZl8eIFYSq71DiVPdScQO82f/31VxtkakkNPSbNmVRg6J+TV1IKYEVjV6McbyZQgbw6mybyuiX6OiD3EQwWQv9s+kAKmkyrndcll1xiP4w0gVr18vrwFJ0d0j+kJugCAJDr2F+Gn5rH9OzZ08ydOzcSFPzrX/+yr51eV2XgVPqouXWaJ6gASY1T9PN+++1nS0yV4VUXTS3roNLNICrRVMA4ffp0u0SCAhL/MhB6Pyhg09p6Ks9URk7ZNi2v4Jx55pk2ANMSFr169TKPPPKIzYppW8p+Ba3z1717dxv8fPLJJ3ZtRAWbataix6BAOB5lA/W+1fi92TgFkXpe1IlTHTy19IPuX9v0NuRJBp0k0euhgFaPWY1/1OBGmb+bb77ZPPXUUwm9bom+Dsh9u+xUuyEkRGdaVFeuORCaE6GSCn1pkVGVAegDpVu3bvbDRTXz+tDQZZ1tUdtirT+jf8x4680AABB27C8BIBz4lC0mlSJoh6UJ8jp7dfjhh9uf1dVK1NZYKXit/aI1XXT2RmdCla4HACBfsL8EgOxFN9FiUumBFvU88MADI9dpsrIWHdWEXaXiNYHYdc3SWVJdrzIHtwAoAAC5jv0lAGQvgsFiUtcptSDWJOtYk3v9a7eoJl0TjFWZG2/NGQAAcgX7SwDIXpSJFpMmBC9atMi25W3QoIH90plPLV6qOQ6aqOufOP3DDz/YEhl2bACAfMH+EgCyF8FgMallr85YakFcraekLmqaEK+Wx9q5HX300WbevHnmoYceMkuWLDFvvfWWnUzfu3fvTA8dAIC0YX8JANmLbqLF7I4m6oCm1r1acFWtfrX+jNZw0boy8umnn9qWvdr5qRWy2i2fd955GX4UAACkFvtLAAgHgkEAAAAAyEOUiQIAAABAHiIYBAAAAIA8xNISAbT+kVphiya3080MANJHsxf0OSy77rqr/RxGyX3wwQd2Xt7KlStth8/bb789au2/omJfCQDh318yZzDAtm3bzHfffZfpYQBA3mvVqlWkyQiKb9myZea0004zzzzzjGndurUZM2aMeeSRR8ykSZOKvU32lQAQ/v0lmUEAAHJc/fr1zeTJk+1yDgri1q9fb6pWrZrpYQEAMoxgMIA3xaoou3Tp0mkfw+zZs+335s2bmzDjcWSPsD2G/v37m0cffTRy+eqrrzYDBw4M3eOIhccR259//mlmzZplf6ZENHkUCM6dO9ecccYZdr82dOjQEm3P+9robHSYykS3bt1qv5crV86EUZjHH+axC+PPnDCPPVXjV4GnTvCVZH9JMBjAu0PTDjMTwaAbQybuO5l4HNkjbI/h3nvvNdu3b7cLVB966KH2ssYetscRC4+jYACo+Q7+gCJMAUYYaK7gzJkz7cLuffr0MePHjzd77LFHsbblfW30c5heKzfWMI05V8Yf5rEL48+cMI89HeMv7nYJBgFkpd122802u0Du09nSF154wdSuXduccsopmR5OTitTpoz9fvbZZ9v5g19++aU58cQTS7xdZYbDdGLDZZ5btmxpwijM4w/z2IXxZ06Yx56q8euk+fTp00u0DepvAAAZDwSXLl1qm5FoLhuSb8qUKeaSSy6Juk6lRVWqVMnYmAAAmUcwCADIWCD4/PPP20BQmeALL7zQVKtWLdPDyknNmjWzZ6XfeecduxyEnnedUS7J0hIAgPAjGAQApN0ff/xhAxIteaBAsGfPnqZevXqZHlbO0rzAxx57zAwfPtx06NDBjBs3zgwbNsw+9wCA/MWcQQBARgLBn3/+OZIRrFu3bqaHlfMOPvhg88Ybb2R6GACALEIwCABIKwWBy5cvN+XLl7cZQQJBAAAyg2AQAJBWjRs3Nl27drWli3Xq1Mn0cAAAyFsEgwCAlNuyZYtdS9B1r2zRokWmhwQAQN6jgQwAIOWB4MiRI+26dhs2bMj0cAAAwP8hGAQApDwQXLFihV1KQl8AACA7UCYKAEhZIPjcc8+ZX375xVSoUMFcdNFFplatWpkeFgAA+D9kBgEASff7779HAsGKFSsSCAJIeOmZfv362fUw9V2XAaQOmUEAQEoCwZUrV0YCwZo1a2Z6WABC4JZbbjEPPfSQ/fmLL76w3x988MEMjwrIXWQGAQBJtWPHDrN9+3YCQQBF9vnnn8e9DCC5CAYBAElVqVIlc+GFF5qLL76YQBBAkRx66KFxLyMxlNsiUZSJAgBKbPPmzWbJkiWmefPm9nLlypXtFwAUxb/+9a9IRlCBoLuMoqHcFnkRDL711lvmrrvuKjBX5d///rc57bTTMjYuAMgnOuP87LPPml9//dWcffbZZv/998/0kACE1G677UbQkgSU2yIvgsEuXbrYL0cNC959911z0kknZXRcAJBPgeAnn3xifvvtN5sJrFu3bqaHBAB5T1lVlxF0l4GcCwa9li1bZgYPHmzGjBljypQpk7Ttzp492+yyyy4m3dzCzLNmzTJhxuPIHrnwGITHkV2B4MSJE82mTZtM+fLlzWGHHWaXktBXSezcuTNpYwSAfES5LfIuGFRJwbnnnmv23nvvTA8FAPJiQXllBBUIqqzrqKOOso1jAACZR7kt8ioYXL58uZkwYYIZP3580retZgilS5c26eayBS1btjRhxuPIHrnwGITHkR0ZwSeffNJs3LjRZgQVCLZv3z5p29eyFNOnT0/a9gAAQA4vLTF27Fhz5JFHmurVq2d6KACQ88qVK2eaNGlidt99dzKCAACEWE4EgypVOuGEEzI9DADIC5pHffzxx5srrriCQBBAzmGNPuST0AeDO3bssOVWrVu3zvRQACBnqVuoqjD++uuvSEBYoUKFTA8LQI7KZEDm1uhTN05912UgV4V+zuC6devs2oK1atXK9FAAICdt2LDBriOoz1t1+mQdVwC5vGg6a/Qhn4Q+M6h5gnPnzrVzWAAAqQsEq1ataudnA0AuB2T+NflYow+5LPSZQQBAegLBiy++2DaNAYBcXjSdNfqQTwgGAQAFrF+/3gaC+l6tWjVz0UUXEQgCyIuAjDX6kE8IBgEAUTQvcNSoUZFAUBnBKlWqZHpYAPIIARmQHqGfMwgASC51Cu3cubOpW7cugSAAADmMzCAAIJIRVCAo9evXN7169YpcBgAAuYfMIADANokZOnSoWbFiReQ6AkEAAHIbwSAA5Lm1a9eaZ555xqxcudK8++67NkMIAAByH8EgAOR5IKiuob/99pupUaOGOeecc8gIAgCQJwgGASBPrVmzxmYEXSCo5SMqV66c6WEhRcaPH29OPfVU07ZtW3PWWWeZr7/+OtNDQgx//PGH6devn+nQoYP9rssAkAo0kAGAPA0ElRHcuHGjqVmzprnwwgtNpUqVMj0spMjSpUtN//79zeOPP24OPvhgM3bsWHPVVVeZjz76iNc9C91yyy3moYcesj+7hddzYZkFBbUDBw40M2bMMJ06dbJrB2oJCQCZQ2YQAPLQJ598QiCYR9QYSCXA7dq1M6VKlTJdunSx1y9atCjTQ0MALbQe73KYg9yRI0eamTNn2mBXlwFkFplBAMhDWkewTJky5phjjjEVK1bM9HCQYgoC9eVMnz7dbNmyxTRs2DAp2589e3ao5ppu3brVfp81a5bJRk2aNIlkBN1l71izffyxTJgwocDlsD2GsD73uTD+MI89VeNPRsM3gkEAyBObN282FSpUsAftCgQVECL/LF682PTp08dcd911ZISzVN++fe13lVO2bt06cjns9FiUFfReBpBZBIMAkAd+/fVXO0dQzUOUDQxTFgfJowPx3r17mx49epjLLrssadtt3ry5KV26tAkLd2a+ZcuWJls999xzoR5/kGHDhtnvYZ4zGNbnPhfGH+axp2r827dvt5UeJUEwCAB5EggqMzhv3jxzxBFHmLJly2Z6WEizTz/91GaYbr75ZtOtW7dMDwd5SIGfGhmF+YAeyDUEgwCQw1atWmUzDAoE69SpY3r27EkgmIfUKObaa681999/vznxxBMzPRwAQJagmygA5HAg6DKCLhDUnEHkn5dfftk2jFFW8MADD4x8ffXVV5keGpB0rNMIJI7MIADkoJUrV9qM4O+//27q1q1rA8Hy5ctneljIEJXmufI8INfl6jqNQCqQGQSAHF1XjkAQQD7K1XUagVQgMwgAOahNmzZ2bmCjRo0IBAGUmEotlXFTYHXooYdmdSdQjc+7TqMuAwhGMAgAOVQaqnXj3CLyLVq0yPSQAORI4Bam0ks9XvE+fgDBCAYBIEfKQkeOHGkqV65sLrroIhrFAHksFYFbmEovFfhma6AKZBvmDAJADgSCahajbpEqDS1Vio92IJ+lInDzl1pSegnkBjKDABBiy5cvtxlBlYXVr1/fXHDBBaZcuXKZHhaAHJszR+klkJsIBgEgBwLBvfbay5x//vkEggBSErhRegnkJoJBAAhpIKjS0K1btxIIAohC4AYgUQSDABBCWi5CB3y1a9c2PXr0IBAEAABFRjAIACFUrVo1c/HFF9uuoWoaAwAAUFS0nANyjOaP9evXz3To0MF+12XkhqVLl5q5c+dGLletWpVAEAAAFBuZQSDHhGlhYBQtEHz++efNX3/9ZXr27GkaNmyY6SEBQEJ0UlL7JjW0adKkienbt2+R/sY1wVFpPIDkIhgEckyYFgZGYpYsWWJeeOEFs23bNhsE1qtXL9NDAoASnaRUA6yi/g0nNoHko0wUyDEsDJzbgaCaxVAaCqA40wfatWtn2rZta78HTSMoyTSDeH/rPyk5Y8aMQrfHiU0gPcgMAjmGhYFzx+LFi20g+Oeff5pGjRqZ8847z5QpUybTwwIQMt4sm/Pll18WyLaVJBt34403miFDhkT+ViXtgwcPtpe1L3Lbk9atWxe6Pf/fcGITSA2CQSDHsL5Ubli9enUkENxnn31M9+7dCQQBFEusrJq73s3PGz58eEJ/F2T06NEFLrtg0HuSMtE5g5zYBNKDYBAAslD16tVNy5YtzW+//UYgCKBE/Fk27/WxMofe3yfzJOWsWbOK/DcAUodgEACy0C677GK6dOlitm/fbnbdlY9qAMXnsmqfffaZLd8sVaqU/f7SSy+ZUaNGmZ07d0bdvmLFiqZXr17mrrvuMn369Ilk/bp27WoGDRoU2NVTv3Nlou4ygOzHEQYAZImFCxeamTNnmtNOO80erCkgJBAEUFL+LJsavARlAh0Fgrq9bucN8PSzPpMUXPqXfVCQ6C8XVfkpy0EA2Y2jDADIkkDwxRdftGfra9asaQ477LBMDwlAjgqaC1inTh3ToEGDqPl5QbfTdbEazShQ/OWXX6ICR0o9gezG0hIAkGELFiyIBIL77befbfsOAKkSNBdQc5OnTp1qgzeXzQu6na6LtexDti0HUZKlMoB8sWsurMF1xx132NKqWrVqmVtvvdUcddRRmR4WACTkp59+snN2FAiqy163bt0oDQWQUsr86TPHOxcwqFtnrNspMxi07EO2LQfBwvVA4UJ9xLFjxw5z9dVXm1NOOcWMGDHCToy+9tpr7fcKFSpkengAEJfKqXTmXE1iCAQBpIsyf1r2wS39UNTbxVr2IduWg8i2TCWQjUJ91PHtt9/alP+VV15pGy0ceeSRtjNW6dKlMz00AIhL6wfqTLUCwaZNm9pAkM8uAMng1g30BmVFbeQSbxuxln1IdDmIrVu32rJN/7aTMW6vbMtUAtko1MHgnDlzTOPGjc2dd95pPvzwQ1O3bl1z2223mXLlyiXtPmbPnm0DzXTTB2VR1uPJVjyO7JELjyGXHocqG9q2bWuWL19u1xPU51kYpeL18Le5B5D+8shUllhquyNHjiyw7WTfZ7ZlKoFsFOpgcMOGDeaTTz6xwaDmDY4bN86WjX7wwQematWqmR4eABSgTKDLAGqe81577ZXpIQHIMckojyzJNoIyfKLrJkyYYE+CBW072WWdLFwP5HgwWKZMGVOvXj3bAUs6d+5shg4dar755hvTqVOnpNxH8+bNM1K65c6yK2MQZjyO7JELjyHsj2PevHm2iuGCCy4wK1euDO3jSPXroYB5+vTpSdsekG+SUR5Zkm0EZfgk1tqGyW5Ak+xyUyCXhToYbNSokdm0aVOB0itKjABkm7lz55pXXnnFfkbpYKdhw4aZHhKAHJWM8siSbCORDF/QuobJKuukiyiQJ8GgFmVW1u6ZZ54xF154oXn77bfN6tWrTfv27TM9NAAIDARbtGhhTjrppNDOEQSQ/ZJRHhlvG/7M21133WUGDBgQuXzIIYcEZvi816mqy7/9ZJV10kUUyJNgUMtHPPfcc+buu++2bY9VMvroo4+aSpUqZXpoAGAp6Hv11VdtIKhSyrPOOsuUKlUq08NCHtNSTDpBcf/992d6KEiRVJdJ+jNvkyZNsh3e3eVrrrnG9O3bNzDDpzmDrVu3TmkzF7qIAnkSDIq6ibqOVACQrYHg/vvvb84880wCQWSM5mI+9dRTNvNy+umnZ3o4SCF/sKaF47WGqYIzZe3kyy+/LHag6M+0ff/99wVOOPTq1ctMnDgxatt677l5xqmcw0cXUSCPgkEgGzBZHX6auzxlyhQbCLZq1cqcccYZBILIqP79+5uNGzfaNS23bduW6eEghfzB2ujRo80vv/xif/ZmzIo7n86fedO6qV6bN2+OBKOZmKtHF1EgcQSDQBIwWR1+Wp+0R48eZurUqaZjx44Egsi4m266yS5nomkVP//8c1K3nak1efN1vdKg8es67YdmzJhRINhXZjAWlW1qO96/Vxnn3/72N/P4449HLqvs063j3LNnT/P888/bPg1eyj5678ttu7Cxhwnjz5wwjz2b1+UlGASSgMnqcNauXWv22GOPyNnpo48+OtNDAiwFgshd3oXcpVmzZqZs2bK2RH3atGkFAjdHgZ7/72fOnGlPbGo5HHdZZca33nqrvayg8OSTTy4wTUdrPHvvx20bQPYiGASSgMnqcGf7xowZY0444QTToUOHTA8HSJtMrcmbj+uVxhq/C9yc3Xff3c7ZU+f1+fPnR/1OQZpOVnmnNfj/fsGCBVGXleV74YUXIpeHDRtm5wq6xjGuQ6ibmxhrykQuPvdhEubxh3ns2bwuL8EgkARMVocOil577TVbsqG5OfoeprI5AOGZk57oSUn9nTdYcxSgqYQ93t8rqxivvFQOP/xws2LFCvtz165dzaBBg5gvD4QMwSCQBExWz2/fffedef31120A2KZNG3PaaacRCAJI2Zz0yy+/PKGTkrHK1JX1a9eunQ3mXPbO//ea36Q5g46CPf+4hgwZErmsjCCBIBA+BIMAkMRAsEuXLgSCAFI6Jz0oGAw6KenP9jm//vqr/dLyEqK/8/+9MpKaGxgrI8lceSA30N4OAIpJTRVcIHjggQcSCCIU+vTpw4LzIeKfg7548WIzcODASGfCeO666y772VSxYkVToUKFwNsUN4jzj4u58kA4kRkEgGLSmm0KBNu2bWs6d+5MIAgg6VxGbtSoUXY+sr5cF8/nnnsu7hxDLTERNGfQH8QFzUu88cYbI2WgbuF6LUviHxdz5YFwIxgEgGLSfJvatWubxo0bEwgCSFhQ8BVrvp0r39Rt3cLxovX/graloM07ly9IzZo1TcOGDe1nmLKH6jjqgkYFfo8++miB9cu0cL03GGSuPJAbCAYBoAjmzp1rD6Lc4sv77rtvpocEIAeawhQWWB1yyCFR8/9+/vln069fv6jgT7+PVQ7qtc8++9hlJzQOLQviDTLlzz//LHEQCyAcCAYBIEE6c/7WW2+Zvfbay/Ts2dOUKVMm00MCkOW8AZQCOhkxYkSR5+35l3lYs2aNDSj9wd/vv/8edVlzBrX8gzfgc8tOuIA0EeomWpwgFkB2IxgEgAR888035u2337Y/qzRUbdQBoDBBAZSfa74Sa01BXffkk08G/q0/+HOfUa50XaWg99xzjxkwYEBCy074y0mVRXR/07Fjx6jfv/TSS2QJgZDjaAYAihAI6sz+ySefzBxBAAmJl/VTl89evXpFBX1BgWNRMniuuZULElVCqpNXiS474ZQvX97Uq1fPBqgK+tTAxp+dXLlypf1y29HjoIwUCBeCQSCJmE+Re77++mszduxY+7MWaT7ppJMIBAEkLF7QpUDQG6Qla+0+f7ZQgZx/f+TvUuq3ZcuWSJOaRGisySojZV8KpA/BIJBEzKfILdOnT48Egu3btzcnnngigSCAIvEuweDmDGqx96DlGPyBoysfDQomS5cubbZv3x65rOxf9erVbabOT8Ge9k/e/ZHrBqoxeLuJFpfGmqxgln0pkD4Eg0ASJWtHiOygEik1Z2jVqhWBIIBiKcoSDFrmYdKkSWbevHmmSZMm9rLLiKlU0xvonXnmmebHH380P/zwg21mpbl9CxYsiLntePsjzStUk5nffvstcA6ivyFN2bJlI4GtW4Nw8uTJZvHixUlZiJ59KZA+BINAEsU6q4twqlWrlrnyyitNpUqVCAQBJN369etNp06dIsGfKhBchk7f1fRFgaS+FBi62+655542M6jgUMtA6Ov777+P2rY+s7xrBbrgLSgL512XUCfAvAGhLleuXNluTx1FBw0aFFWyqeUtguY0Kmgs7kL0/n3ptm3bbOkopaJA8hEMAikqBwoqAUL2++qrr0yNGjXsWoKigyAAKIlYc+DU0dPNy1Pwp3VMY2XEFBi6QFEBob7i8S8aH4s/61alSpWoYLBp06ZmypQpMQOxWFk7ZQ+LG7zp+VGG1BsY+8tcASQHwSCQoXIgZJ9p06aZ9957z5ZcKSO4xx57ZHpIALJASRuaxJoDpxJP//14KSPWoUMHm9V79dVXS/QYnn76aXvfyj565y36F7NX9u+zzz4rEIh5M5P77ruv/Vv9TmMMUpLKGD23Cia9KBUFUoNgEAD+7wDt/fffj3QNrVatWqaHBCBL+IM5Za3iZcv8FFz5Lyvw82fvtL0rrrjCBj4KslxAFm8JCNc8Zvfddzdr166NmRFUtk8BoL4cbbd169amTp069rIrA/WvQajxKBB041E209tpVCWhGoPmDpYqVcrs2LHDPkaVkBa3EyjTLoD0KJWm+wGArDV16tRIIHjEEUeYY489ljmCAGIGcy5blggFfYsWLYq6TkGT/t6/bp/mJ8vEiRMLZMaCaD6f296aNWsSLg31UlCnbqP6co/TH3jpcryyVI1VlRVak/XII4+0z4+CTgXQiT5Pfgoi+/btazOZ+s60CyA1yAwCWYJ1lTJDz/e4ceMigaDOfhMIAvDyB21BAWIs+lz/9ddfo65TFi2o7HHVqlU2gHr44YcT+vxP9meVC3KD5r975/D5eYPHZHUCZdoFkB4Eg0CWYF2l9FOzBhcI6mz2McccQyAI5JlETsQpeEskQAwSFAxpKYd45Z/K8GnR98KoA2lh6wOWL1/ebN261ZZuOrVr17ZNsrylqPHGKxMmTAicM+hvlkZ5JxAuBINAlmBdpfTTAU2zZs3sEhKaI0MgCOQf/4m4F1980QZjmzdvtp0033333cAmKUEBolsuQp8nbv2/xo0bFyjtVCB5zz332O9q7FLY2n5+mpd3/vnnmwceeMAccMABBTKPXkFBpeYIak1APU51T169enVU8Bbr5KTmSbrAWQGzyln9gXMYumpTiQP8D8EgkCU4m5o+OgBS4Kd1urp162Z/JhAE8pP/xJtKNR1lvmIFW64rp5+yZ665itb/++6770z16tXNunXrbHZOgZ/W9VMQuN9++xU5EBR9Xm3fvt3cd999cQNBv4oVKwZmE9VExi1orwDVn7F0z9GNN94YWZPQLTY/ePDg0JV3UokD/A/BIJAlwnA2NRdMnjzZnrk/9dRT7QGVzrADyA8uI6SSx/33398uH6MMWTxqzBLk008/tcs+eD+vtW1vl81421AQGHTbRCgQVAbTNZAJ4rp7evXq1Suw6kSBoBrIiII9dQf1cicnR48eHXX9E088Ye8nbJk1KnGA/yEYBLJEGM6mhp0O3nQQKCr/0ll5APnDm9maOXNmQn+jgNFbRum4QM5llhR4uW2XlMo4dZ+FzUuMl1X0/63mCeq6woJf+fnnn222cP78+TaTqDUGY92Hy7CFaf9FJQ7wPwSDAPLCJ598Yj7++GP7sxrFEAgCuZ8BVMdPBSzKXqms86mnnirytlTaWbNmzbjlmKNGjYqZQSwOjVnzDRNtUpOIevXqRQWryipWqVLFri0o3t+pVNaVy6qkdMCAATbY022DAt6wZdaoxAH+h2AQQM5TS3Q1OnDzedQ5FEDu8s4Jc7yLrReFFnIvjCuxLCqVqgetDaisYDLnMSuwXL58edR1rVq1smusKnBWxlS30RzHIAqaVF6v6grXOMcbqIYts0YlDvA/BIMA8iYQ1GLyWksQQG5LdA3ATIu3SHxxFpCPty3XIMYfwClwLqy8VaWlKhf1ZkeVLd1nn32KlVnTUhf9+vWjmyeQBQgGAeQsHbgoGJTjjjsusrYXgPApynIAySyvTLQ5Szbzj1VzEr2lkv6Oo5dcckmkYYyynkGZT81XVGbRvTZFCe6UtR05cmSh3TxZAgJIPYJBADlLZ641x0XlTYcddlimhwMgTcsB+NcADApwcoEWlE9kcXq/7t2726BKwZZ/DUV1HHXPq0prYz1XWqfVBYDexesTWarB30U11pxDloAAUo9gEEBOUTmUSpDc2eMWLVpkekgA0rwcgKoAvHMEEw1wEpUtWUE1tymqWrVq2WZalSpVsk1kvKWf6iCqx+aWzDjkkEOium4q+NRcRnVjVkMe/7zMwl4bBZ8DBw4sMH8x1pxDloAAUo9gEEBOBYJaOmLWrFnmoosuMrvvvnumhwQgDcsB+MsJ3VIIQd0i/dsJM534KqqNGzdGOoVu3rw56nffffdd1JIZ11xzjV1z0GX9lIXs27evDawVMMYSK7jTa+TKQ125qrKUseYcsgQEkHoEgwByJhD86KOPIo0jFixYUGDhZADhFW85gETLCRU0usoB/ZyP4pWV+jONKqlV8BiUnfMHavq8LVu2bNyGMv7MXoMGDeKWfbIEBJB6BIMAci4QPOmkkwgEgZALah4SK3BItJxQ23v88cdTMt6witcMJ6icVqWjsQK1wpq7FDXTxxIQQOoRDAIIfSA4fvx4M2XKFHv55JNPNu3atcv0sACUUFGah/iDDDU0UTDpD06Yc/a/ALBcuXJ2uQjN/XviiScCbxNvbmRxAjUFjGvWrLGlqFrzlUwfkHkEgwBCHQh++OGHkQM8AkEgNh2Aay7dokWLbGOl+++/3+y9994mWxWleYiCCjVFcfPdNMft+uuvtwGPN3OVS/MFS6J69ermvPPOiwRjCvyefvppu1yEd1F6N1fQy9uYp6gUQPbv39/+3LJly2JvB0DyEAwCCC2d/f/xxx/tz6ecckqkfAlANM2Tu/rqq83NN99sTjjhBDNs2DDbCOS1115Lyvb//PNPs3379gLXlypVKmqZB/8yBsreKUCdNm2aPZHjLTVU4PbNN99EbqvL7u/V0bJMmTKR35UuXdqsXr066roXX3wxEty4AFDbf/nll+1t4z0WR2PXfYXltnoe9JwXdlstQD948GDzySef2GV39Dxfeuml9nf6Wa/FnXfeaf7xj3+YUaNG2Y6jbrt6nX/77beorKuedzdG/T7oveB9zR9++GEzb948+5oOGDAgZnmpHrv3fuNtN9Zt/e8x3Z86qbrbap5kvAyonlN9+W/rfy/Hu21RtlvYbXUS1PuaF/W23vF7/z8L225xb+vuKxm31WvqHltht/V/Rmi8Gnc6byuaR+sdv24ba9ze2+p1itct2HvbktplZ7xHEAJqUfz8889H3kh6ckp61k8v1vTp0+3Pbdq0iXrjpYu6IebCmbOwPw43Z0UdKtVyWwdQYV3wNuyvRazHsWnTJrNw4UJ7FjtMcvX1SIZs+AzONZMmTTKDBg0yY8eOjTzH6gb50ksv2fXiSvo6vffee4EH6uoWecQRR0Quv/766zEP6JWx1O9c5uitt96KedBUrVo1c+yxx0Yuv/vuu1FZLS91znzsscfMAQccYAPEoUOH2sxYEK1J6l0uQUtS7LnnnoG3VSdOPafOxRdfbBo2bBh4Wz2Of/7zn5HLPXr0sCWasdx9992Rn7t16xb3/+u+++6LHJifccYZ9n8mlgceeCDyPOkEWrxKClVaaH1GnUjQ3zVv3jzmbY8//vhI92Z9JsyePTvmbRcvXmxGjBhhf1YgqpMTsXTs2NEuhSHz58+PvN9iLSdSt27dyHvpq6++inlbvffr169vf162bJmZOnVqzNsefPDBkdd1xYoVkbnpQfTcu/8nve8UbMeifZaW6JC1a9fa44xY9Ny798CGDRtsRUwsel/pve7eo/rfjKVx48aR+fV6nd9+++2Yt1WzH3fCVYHKG2+8EfO2+p/xzgdVI6JYivIZof9bvWeU8U/mZ0SVKlWi3ofjxo2zJzyCaDkW/e846lewbt26wNsqJunSpUvksqoXVCYdRPu5M888M3J58uTJcZfA0RrK3uW0SrK/DH1mcM6cOea///2v/SACUjlnZebMmfaDiMnsmaUPPu8Hr87whi0QBNJNJ0z22WefyGUdMOy11162625xg8FU8C9Gnkw6oVfctfnCIF72sCR04F2che1jUVCH/5+5VmChDD2QSaHPDOrMwyuvvGJ3asniPdupD8FUfcDG46J8d/YjrML+OHT2VkGg484sh1HYXwvRx5VKmHRg6z1bG0a58Hqk6nEk40wnoikzpmyJMjzO+eefb8455xxz+umnl3hfqaxFUHliYSVgygI++uijkddd69q5E25FLQHTHEFl/ZzLL7/cvneUwVAmQ2fSlclTpineshL5UCYa67Y6sebWIFSWUc+XqmGU6fEuTu+l8mOdlE+0TFSvi1trUGPo06ePrfJKVZmo9z3mxvuf//wnsEz0mGOOiZoTqUyYsnveck6txShBmdJESj/dePR7jVHBoMaTrjJRl7XV+MNWJqoEkB6by5KGrUx05syZ9raxsuzFKRNNRiVNqDOD+sBSSYdKLzTJWRPhb7311rglEkBRzyR7g0F3Zhnppw9QfeApEHSXASSmfPnyBQIgZXtUBpgMOjhK5CDEP88l3jpyRZkTo/tX4KKDSu+2VN2hMkAZMmRIpENmvANZr3gH6Nl428ICpni3VdDnnaPpni8F52eddZa9HETPtzdY9QYjQVwmzM0Z1OuUyGtd2HZj3TboPeYNgvWzu3+VzbrO1O6y9z69gUthY/Zu10vj8L7/dDnWbYPouS7JbWONv6TbjSdZt/W//kX9jMj0bUv/3/gTGbc3QE61UAeDKhXTP+oVV1xhzxK8+eabpnfv3ub999+3tcLJoOidOYP5+zg0R1ClocwZzCwFfqr3/+mnn+zlgw46yHTu3NmEWZhfj3TOGURyqERUc3G8z/GSJUtMo0aNMjquZK4jF7StoI6kKi1PZtljrgjKlg4fPtx+v+eeeyKBtrIx3i6jha0V6KcqAmXH0vXZV5T3WDoWuS/qWotAqoU6GNTE22effTZqkvVzzz1nz2x5J40CJd2JuAPesAaCYeYCQdcMIOzloUAmaC05NS5QyaSaH+jElqpp1EAil/kPvFXi50qQ/ZkOqg0KUgMSN2/eBVSusZo/YAq6Pmz7zHQscp+OgBPIm2Dw66+/NnPnzrXzuhydsQr7HBwA/58Ozt555x37vy6a21SUkgwA/zvI1Xw6tdhXa31VvXi7ZuYqd6Ct5RHUmS9WN8F8UqNGjZhLa6gSSoGx5ip55yu5DGG8wMXbcM0F4DRcy0zACeRNMKiaW80RUBtdzRNUYw8Fg6w1BuQOV1uvZgYq1XVZWgBFs//++5sxY8aYfDvwVgCjYBD/nzcQ1PPjLQ+NNd/QmyGUoKAvqCQXQPYLdTCorlc6y6mzUepy1axZM/PEE0+QGQRyhM5Qa9kYHcTWq1cv08MBEEI6Roi3Xpfka4mojqO0Pp/WztOJNm/mVOu/aa0173W6nb8DqQv64s2F85aQ6gQ+yykA2SPUwaDLFugLyBW5MO+iJHRQNm3aNNskxrVUJxAEUFz5nKFS2We87qIKBJXV69evX9SSCo66zXqDQXU2PeqoowKDPldCqoBRVVovvfSSzci6xbFdN1L3t+rxACDzQh8MArkmn+ddKBB86623bCdJdQ4977zzMrLOJ4Dc4c9Y5TJ/I5xYgaCyft27d7cBnE5ABpXRBmVTdYIuVgMUNxdOgaW3pFRBoO7P67333rP3m08nOoFsRTAIZJl8nXehZgVvv/22DQR1QKPyJQJBACXlDV4WLVpkVq5cacJEAZjmTifS/CaRclc14VK2zlWdKHgrrIzWm0ksrAFKIvsszVvUic9kn+jM98oaoDiiC78BZJx/zaF8WINIgaDLCCoA1ALHCgYBoKRc8DJ16lRbbRA2Ks1MZhdULXiubJ3WmFQgqLJOL/+cwAMPPNAuTaJ5foksgxC0z1Lw6c8OpuJEp6usUSZY33UZQHxkBoEsk29rECkQfPPNN83MmTNtIHj22WeHfiF2AJkXlCXSl4IrVxapIEW305rF/qxa+fLlc3pxemUDFTAp2PNyS0p4S0njZdf8z7Ma++k5Hj16dOQ5Vud3ZTi95aOpONGZr5U1QEkQDAJZVqKSb2sQae4IgSCAdM2/Hjx4sLnyyivtZfd58/jjj5vDDjvMfPvtt5G/VyBYs2ZN2608l2m9ZgWE8+bNs0tIOA0aNEhoX+R/nidNmmTLWv2BpDuxOWHCBLtMUCpOdMbraAogGMEgkET53PyluNzagZ07dzYtWrTI9HAA5Ah/VkjZQHeirmfPnlHLUClgmTJlii2d9M6f23vvvU39+vXNjBkzohZhDxtl5Zo3b27nS65du9Zm7hyVoCoIVkDoDYYTDaT8z7PbhvaBuh/dt/cEqZqDifdEabLm+uVbZQ2QDASDQBJRolJ0OtC67rrrWB8UQEqzRAry9KXr1qxZY/r37x91ewUfymZ5SxkVAHoDpDB0EA2iOdiFPQ4FbZoXWNRAKl631ldffTXSsMfd5vLLL0/ZidR8q6wBkoFgEEgiSlQKp4Ord955x64j6NYPJBAEkGzeLNHixYujMn4KjAYOHGhLI72ZKH9mafLkyUmvhFAJpcuYLVmyJG53U93Gm8ULonHHmtuodQZ79eplvv7660LH5tYcLCrvc6asnzqFOlq03mv48OE2EPcvOs+JVCBzCAaBJCpJiUo+tMRWIPjaa6/ZslDNU7n22mvtgREAJJs3S+Rf+04dNUeOHBlVzqgmJ/7PYM0jTIROaG3durXQ2x155JF2zqLTtm3buMFgUCDozwRWqFDBXHbZZfZxfPfdd3Zf4l1ncMyYMQU6hDoqDdVncElKKr3Ps79jqH95IM1JdM+7d9F5TqQCmUMwCCRRSUpUcn2+oQ5KFAj+8MMP9sDktNNOIxAEkJETdS4gcdT50tvt0n0G67pEVK1a1XTr1s2WRSrzFSub9+WXXxbI3BWVupx6l5rQ/Wmc06ZNMxUrVixw+1gNcBS4aZ5kMk86+oO/SpUqmaZNmxaYc6nL3pOgWt5CgakehzKUzPUD0od1BoEskctlMt5AUAc/5557rj1AAIB0rzWo7/6gRfzr7emyApNEgjcFgqIsX7yyzm3bthXI3BWFxh205qDbXyhLmCjNj0x29YmWkfBycy79zXdULus9CaogWbdzpaq5VhUDZDOCQSBL5Opi865MyQWC55xzjmnSpEmmhwUgTyj4Uplohw4d7HddPuGEEwoEMf4gTuXsuu6aa66JLLpeo0aNAttXYKMSU7eunqPA7JBDDon6GwU8N954Y2Rcy5cvL9JjidUoRvsLbc+7NEQ8ysL5s29Bz1NR6XnQ86TnS/fhnT8oylyqk6ubM5jLJ0GBsKBMFMgSudoSW2fXZ8+eHckI7rfffpkeEoA8ElSCf8MNN9jPJG8DmY4dO0b9nTJwQ4YMsYGLMoqi4FDXeQNB9zt/sxRlAZXpUpMYLwWNmjeocQXNF0ykaYxTu3Ztc95559nxa3uxGskkUh7qf55eeOEF+/yoBLY4UyUUVPqpmY23myhzBYHMIxgEskSutsTWzn3ZsmX2DDmBIIB0C8o+KSDR0hJu0XlR4Oaf0+f/e2W+/Ovm6bNbmTR/+aYCOgVXsUo3Y2XBEg0EpVatWnY7bt5dIpQFDSrD9I9Hcw07depkvvnmG1Mc/kDPZSPdOoNC0zUg8wgGASSd5odobou+ypQpY89cB83RAYBUSzT75AIRLU7vXYbCe/tYJ+3ilTdWrlw5KlBUNk+BjE6QxVqfL0hQxlDdQ0XbqVmzZkLbiRVsBq0XqEYvCnSLE2gFBXr+bdB0Dcg85gwCSCodaLz88svm448/jsxvIRAEkCkKQtw8Nn33Zp+88+QUXOh3CxcujHn7WOKVN6q5jLJi3gBLS1YUJQMohd1emTxlIfWljKFKWBVw+jOTb7zxRuD8QD1Of0CpE3sKuPTclLRpT7Kzdsw3BJKDzCCApNHByiuvvGJ+/PFHs2DBAtOmTRuzxx57ZHpYAPJYvOxTrOxSUTNMLmDUoureJi4KxO655x5z0kknRd1ejWRWrFhhks1lIPV91apVNphdunRpgRLWWI9bcwRVGupfCkLZ0ngZvkyUbDLfEEgOgkEASQ8EVc6k0lACQQDZLFnZJW/A6V3cXkHYHXfcEViC6e8Mqq6j/u6bJaXHozmC3qY3uhzrcatZjOYIKlvofRwqm9VXrHLMTJRs5mrTNSDdCAaBAP6znGqFXa5cuUwPK+tLQ+fPnx8JBPfZZ59MDwsA0ppdUkAybNiwqEycuoeq9HTSpEk2I+hs3LixQDmp9jOffvqpWbx4sV1MPtZSEo5KQY888ki72H1QZ1KViQY1vdH+Ld7j9gZaGot3DmVQwJyJks2SzDek+QzwPwSDQAD/WU7tlNV5DoUHgj169DCNGjXK9LAAIO3ZJQUUVapUKVCWqeu1nIPWGHz66aft7/23UUZO8+uUlfv6668Tuj8FaQr2xJv9KyxoKuxxe//GnyUMCpjDVrJJ8xngfwgGgQD+s5qaP4FgOuOtQFBdQxUINmzYMNNDAoCMLenjL8s888wzbUCl/YrWHvQHgY5+p4YuysQlStlABZj+Be8dXe+CRX8mLNHHnUjAHLaSTZrPAP9DMIicVNISEP9ZTpXiIJjWDjzttNPs/EACQQD5zl+W6dYbjEddPL0lpH5ajqJevXq2wYu3QY0MHTrU/PnnnzEzhwpEx4wZY7uNFicTlkjA7G7j9r1HH310Vpdfhi2TCaQSwSByUklLQPxnOTVnMJZ8nHugAw99uZblbdu2zfSQACAr+IMnZfsK488WKvhzS/Io06gA0y1u7w8s/YFgqVKlojqBPvnkkwWWpfBmwpK5D0tk37t161Z7GwW2mdpnhi2TCaQSwSByUklLQPw781mzZsW8bb7NPdCBx0svvWTPTl944YWmYsWKmR4SEGo6OFbjEDULUZOR3XffPWpdOoSbPwul11bLSnibsjRp0iQqM6gmXEH7EQUtCuwU4CmIC1K9evVIFjDW+oQqSdXfa1+XzH1YIvte3dfIkSOTcn/ZVB4MhFXagkEtclqYM844Iy1jQe5LZwlIPs090AGEAsFFixaZsmXLmvXr1xMMAiXw9ttv23Xopk2bZh544AF7WS655BJz2WWXZXp4SGEWypuNu+uuu8yAAQMKzVQpiBk8eLD55JNPzMyZM+MuPh9rbqIo8NT9KyBKZB+WaPYwkX2vfw5+Lu8zgTBIWzCoenXZsmWL+f77702LFi1s/bs+tPTBcNhhhxEMIpQlIPky98AfCF5wwQVmzz33zPSwgFDTMgSPPfaY/f9SV95nn33Wrjd3zjnnEAzmiFhZKP91/svxAjDNY/cGg/7gL6ijqZ+WsFDZqb9hTdA+LJHsocarLGSdOnUi5a1B+17/2HN1nwmERdqCQVcS0KdPH3PxxRebzp07R3734Ycf2oNMIIwlIPkw90AHqi+++KI9aHClbKtWrcqL+ZFAKqkb5MEHH2wPzCtVqmRatWpl53sVdiCP3BcvAOvbt6/97ubdKQjzLyz/2WefRZWeli5d2mzfvj1yWZ/n3iUsNE9R5alB+zBtK95lN17vGNREJ2j/4B97Lu4zgTBJ+5xBfYD4Jz+r69RNN92U7qEASZHrcw/8geBTTz1lli1bZj744AP7+1x+7ECqaU3O559/3kyYMMEce+yxtnpG2cJmzZql5P6Ukfn73/9uxo0bl5Lto2jiZf/ilW9qjqnWvm3ZsmVkO94Opio7veOOO8zcuXMjJxa8gaCozN9L6+nG4p93GDQPMdEpE/6xA8isUum+w6ZNm5pHH3000v1KB5r//e9/7dlQIF2041R5jLq86XusifjeBg8DBw5M+Pa5RAenOmjQDlw7dwWCDnM9gJK57777bFawWrVq9rNFzaq08LjmESbbxIkTTa9evQIP5JHZ7J8yf/quy7HKJ+OVU7qTknrv6LvmHypLFy/DrHJ/L7cEhncMjgLNeJeLOl4AeZwZ1Fmv6667znbCqlq1qlm3bp09O1TYGjxAMmmRXlfOop2wdoKalB9LNnQ/yxR1NrzooovsQYVK2rwZBXb2QMnsu+++dp04RyWjqZg28corr9j5iJdffnlKtj979uzIUghhoBN8hXWKTgdlhP2X3Zi0pJGydeqroHl2uux+p/HrSx2d3e9VfqmTdkHb9VPmWX+jeapBY/I/L7r9l19+GXXZf5t4483G5764GH/mhHnsqRq/KrZCFwxqUeo333zTLF261KxevdpOlN9rr73SPQzkudGjRxe4HC8YzLfuZ/rAWr58uS1hE2Ut9JUP8yOBdNCBcmHB03PPPVekbeqkVlAmqEyZMqZTp052HpkO6Jmjnz38zVR02bsWX1Cg5+iEpkr4xW1D5ZdB2/Vm9HQC4umnn7bbU3bwvffes8dj/jEEzfPzjidW+SeAcMnIOoP64Hn99ddtJ1GdEVU5jOYM+j/ogGyRT93PdBDywgsvmJ9//tl2NFRpd77MjwTS5ayzzkr6NqdMmWLLQP3OPPNMc//995tUat68uW1QEhbuzHym5q25uYJz5syx6w4qSDv88MMjcwZVMuyqUbTv0dqB/rVv1ZndSw1Z3OPRvFP9jfo06CSBTu6pskM/6z6VKdb2dMIh0WUjinpyorDnXt2odZJC49Y6i8pIqmIs22X6vZPP4w/z2FM1fs0Fnj59eriCQTWfeO2112zZmdZUUgCoDwLVt//zn/9M93CQp3SG3N95LZ586X6mgwIFgpoXqIOBypUrZ3pIQE5SgBZv5/7TTz8VeZsdO3a0DUMQrk6hbh/jDfaCmrF4gzYFT/vvv3/Mk5T+E3ea765g0Bk1alQk6MvUST4Fgq7bqb7r8jfffJP2cQD5Lu3BoA401TlNawwOGjTIzkfSQfmJJ56Y7qEgj+m9pzOx7qyp5gHqTGysM6L50P1MBxr631RGUM+B5qLUrVs308MCctr7779vm8h4y/TcOnHe9UuRWwrrvBm0fq1/qYkePXrYIDKRsn3/9n755ZfIovOZopOr8S4DyNFgUN1DXRmAmy+hmvWgzlRAqrgzoQoA3c7VTY53O0f/WdigORK5GAiWL1/ezmciEATSc2LqyiuvjHTrVZmnPpOC5m0hdwQFe15B87O1DJeXykT989lj0d8rG6ggMN7c90RLRpNB+1XvOoi6DCD90h6BHXPMMTbDom6Oom6i2hmqvAXIprOzQQv+JmvORLbNEdTcFM0pUSCojGCdOnUyPSwgL6xdu9acf/75toRPzUDUpVFTJlS6/re//S3Tw0OKFNaMK6h00x9AFuWEgbbXvXv3qNLUoLnv8Ra6L0rAGHQbt33NDdTY3333XXPKKadEzRkEkAfBoD4I9KFw2mmn2YNQBYH6MNDiqEA2nZ31B4qJnoFNpVSctVVmvlatWvbEDIEgkF7KwKuZWu3atW1mXtUzmqvrXxA8Wdq3b89BdxYozjw9bwBZlGoVt9/QtAjXrEbvA02R0FxC774kkYXjEwkYg24j7jrXFIc5gkCelomqe6i+dEZUJaOlSpVK9zCAQs/OluQsbKokshMuKpVrd+nSxWzYsCEUndyAXGskc+6559r13tRAQ9lAnaDJ5fnJKHkAmcg6ZS4I9JeHuiAyaF9SWPmqJBIwFuc2APIkGDzqqKPsh4syg8ceeyyBILL27Gxxz8KmUiI72ERs2bLF/q3moOh/UAEhgSCQfpojqBNNahhz9913mxEjRpiNGzeaiy++ONNDQ451LE0kMEtkLVl/wKiT/Ao8vVUqsYLKwgJNAOmX9kjs448/tqWhOlOlNXVuuOEGe53KFYpr/vz5plWrVrYdPhBEOyo1i1FJjL7rcqKB4tSpU+0812xYB9O/8yzOzlSBoOY+fvrpp3bNTwCZ1a5dO/v5oi81k9Gc+po1a2Z6WAi5WCcLtd845JBDoq7zX45HAaLKTR01gVHg6aV9pt7DOtmo77qsv9NJ1QMOOMA2KcvVJZqAsEl7ZnCPPfaw7ZD1pXkS48aNs2sM6gC1OG20FUTqQ0hnpoDillems4NaSSRy1jYezdNVIKiSoYoVKxbpAABA8qlhjOus7Td79uy0jwe5w5+d03xwNZHRfsM18SvOVATtG1XKHC/wVC8IHeOJvuuy5gdqW67ENRv3sUA+ysh6Djt27LDZFmUlxo8fb/bee29bNlocQ4cONQcddFDUwqtAUcsrUzEXLxVKsjiwAsFPPvnEzg1UIHjRRReRfQAy7KOPPoq6rEZO6u7btm3bjI0JuSHo5KELwNxSSo67nOhUhMLmFrKGIBAeaQ8G77zzThsAVqtWzXTu3Nm88sorZq+99irWtubMmWMDytGjR9t5FqmgM7Oxztqmkg7cE50kns2y5XFozp93x6XL3jH5u+vpsvf32fI4ikvjnzRpkvntt99sKZpKtFetWmW/wibsr4XD44ht586dJl/sueeeBS6rwdpxxx1nG8sAqTh5GCuYS6SBTCJVKqwhCIRH2oNBZSWeeuop07x58xJtR2WhyuaoxJRSg/ykg1Bl87TkgxowqAvfkCFDbOmxnHDCCXZOqoIf1/zF3dbfDEbXebPL2dA5NJkH1mop7gJBNXFSswoA2UmLiedTQIz0ixXMJTIVIZFpFTqhqu64rCEIZL+0B4PTpk2zC+qW1KOPPmon3atENJUUtJYuXdqkmzvLHvb24oU9jpLM1VMjGJVTiQI5HUB5z0RqAWetn+fOjMZbMH7YsGF2zaNY4wj766HF5F9//XVzxBFH2CY6YRb218LhccS2fft2M336dJMPdMDsrT7RY1+9erW59tprMzou5GfWMJGpCIlMq1B3atYQBMIh7cFg48aN7XxBLStREh988IGdlDxmzJjIdVorTZnC4s4/RNGVtPFKSebq+ecyBM1JSHTphZLMxQsD/d+deOKJLOUCZJn7778/6rL+RzV1QovQA9koWUscAcjTYHDTpk3m6quvtgff6izqPSPqn0gfz/vvvx91uWnTpuatt94y9evXT+p4EV9JG6+UZKfin9vgn6PgbpOP9H/22muv2Q5uNWrUsNcRCALZY/ny5fZ70D5L2UH9vl69ehkYGRBfovMKAYRD2oPBW2+9Nd13iSw+Q1iSnYp/bsNdd91l7rjjDttQSLp27ZrWdYyyZXkKBYLPPvusLTVTaejll1+ekSZIABIvD5XKlSvb/1/NF1SZHRkXZKPC5hVmy74QQJYGg5rn9/vvv9vOhlrrTGveuEXjS2Lu3LlJGyPSd4awOOvm+Xc0EydOjOxoBg8ebL+Kuo1k7KyyYXmKjRs32rmRCgTVJObss88mEASykLphi5perV271lx//fWmUqVKds3dRx55xGYHgWxU2LSKbNgXAsjiYFDNBnr37m1LYxTAqX32hRdeaLM6Z5xxRrqHgwwvgl6cuXrJ2NH4tzFq1KjIYrzFDQozPY9CgaAygmvWrLGBoNYRVCk2gOyl/9kpU6aYMmXKRJo9KTBUoycqaRBG/n2f9q/FPUYAkHppn0T0j3/8w9x+++32w2HXXXe1E+WffPJJ89hjj6V7KEgCF8ypKZC+p6MUJBlBl/9vlKVWcKggsbj8WdF0zqNQM6VBgwbZQHDHjh3mvPPOIxAEQkD/p/r89Pr4449pIIOso4oadfHWiQp91+Ug/n2f9q866VrSfSyAHMkMLliwwK7/Jq58TctDqEwGSFVpqr8s9JBDDonaRjKyeTrj+ddff0XmLOpn3W86AmTtZMuWLWvWr19vMw1aU5CyHCD79e/f3y4jsf/++9sAUI1jfvzxx4TK3YF0SrQqx1sxtHjxYhsMOrpO89gB5HFmcN999zXvvfde1HWa86XrgUTOQmpHo0Xj27dvb7+7HU+8s5ZuJ+bOTor+tk6dOknL5inoU7ZbOz59aS5Qus6Cag7uDz/8YJ555hmzbt06Gk8AIWoko6WSTj/9dLPffvvZeb66HPb1QJF7Eq3K8VYMafqFF51HgeyT9sygSkR79eplXnjhBdtIRvMHtVj4448/nu6hIKRnIWPNM4x31tK/01L2buHChTaQ9DeSKYl0zhvctm2bzQaKMp3usQs7XCC7ff3117Yq5ssvv7SXGzRoYL/kp59+sl/6vwbCXJUT1FdA720AeRwMtmzZ0owbN85mMnRGVGugDRw40LbRBrzlnCov8Ro+fLj9HqvJS7xAzL8TU+buxhtvtKVYySynTNf6Sxs2bLDloDpY9AaxTNIHwuHuu+82b7/9ti0TDaJpFEVZexdIteLsZ4rTJA5AjgeDovlMp556qj3wHzNmjPnss8/sZcCb3fPbvHlz5HdBO5d4gZh2WsOGDbPZaG92MNnzctIRlLlAUOWgyioou8AOFwgXBYIyYcKETA8FSAj7GSA3pT0YHDFihHniiSfsQbs6i86cOdOeAVWpaKwzpMgf/uxehQoVogK4oNskEohpJ6blFvzbCtvO0jWI0fdq1arZ5SNcqSiA8Pnzzz/tPPouXbqYZcuWmQceeMBWymjes/7HgWzAQvJA7kp7AxktKfHiiy/ag3KdGX344YfNyJEjzRtvvJHuoSAL+csqFcAVdptEl7no2rVr3MvZzh8IXnzxxWb33XfP9LAAlIDW2NX/tehgW02oFCDedtttmR4aELMJG0tEALkj7ZlBHcg2btzYjB8/3nZy1M9qhKE2/IA/u6f3hbpyOgceeGCxSy+1Dp8OtMI4r07/N+oUqhJRrUumjGBQoAwgXLTg/NixY82qVavMN998Yz799FNTuXJlmkAhq6SzORqAHA8GW7RoYRvGTJs2zZx00kl2fUEdpOsgH/CXWao0xR/AFbc0JczzHbTuGIEgkHv0GadS73fffdc0b97c/o9rv1imTJlMDw1Ie3M0AHkQDGo+hJaROOKII8w111xj5s6da0tiwpSlQfokK4AL+3wHdQ3V3NqmTZvarAGA3HDCCSeYSy65xC51c/3119u2+zfccANN1ZBV6FgN5K60B4M1a9Y0d955p1m5cqWZM2eOqVevnvn3v/+d7mEgz8RbgzAbKXjVnCGNVYGgdrwHH3xwpocFIAVLTGi5JZ3kOfzww83SpUvN+eefbxefB7JFmCtrAGRZAxnt6M455xxz3HHHmb/97W/mqKOOMpdffrlZs2ZNuoeCPBK2+Q4KBHfu3GmaNWtmM+lM1gdyU6lSpUzHjh3tkkvqtq21d/V/r+sBAEi1tO9tbr75ZtOhQwfz9ddfm8mTJ9t10ho2bEjnNCQlm6Z27Hp/6bsux5rfkM3zHXRiRHOI1ClUZ2PdnEkAuWfWrFm2VFQdRR955BGzevVqc+GFF9JhGwCQm2WimiOonZ4OcKV8+fLmpptuyuqDc4S/FDQs8x0UCOr/Q0Ggugvq582bN/P/AeQorbd7++2324ZqKgnfa6+9zJNPPmk/z84444xMDw8AkOPSHgyqccwHH3wQNTn+448/Nm3atEn3UJBDlAXUGpZe3mxarPkO2dRYRhkBBX+bNm2ypWLqHrr//vtndfAKoGQWLFhgM4OiJlFy0EEH2Y6iAADkTDDYs2dPu6PTYvPqlKY10+rWrWuzHzNnzrSlfUBxKaD75Zdfoq5LJJuWLY1lvIFgrVq1bJlYxYoV0z4OAOm17777mvfeey/qBOnEiRPt9clenkZZyNmzZ9sS9N69e9v5+wCA/Ja2YPCss86y3xUQqjGGDn6rV69uL5977rnpGgZylH9OXZ06dRLKpmVLY5kdO3bYLwJBIL+oRLRXr17mhRdesCdLFaR9//33tnFUMmkpJwV/OhGrwFDrlTZu3NhmIQEA+SttweCZZ55pvvrqKzNgwAC7I3L2228/u9SE5koAifKXd+r9410Qt3v37gmVe6ZiId3ilJ4qCLz44ottEFihQoUSjwFAOLRs2dIuLTFp0iTTqVMnWyI+cOBAU7Vq1aTdx7p160yDBg3seobqUqpupe3atTMzZsxIWjCojKMrcw2DrVu3Rhr4hFGYxx/msQvjz5wwjz1V41eCLTTBoB64znhqGYkuXbrY9QZXrFhhO6bp+ueee87OjwIS4S/v1Fnvvn37xm0QExSkpaKxTKKlp7/++qttDqNuuqL/CQD5Qzvx9evXm2rVqkWViW7bts2uv/v3v/+9SNv766+/bHbRTyejhg0bFrm8ceNG29GbqhwAQNqCwUcffdRcf/31djFdR2cqr7vuOnsGdMiQIeaJJ55I13CQRYqTSfOXc2qJkqlTp8bdrg6U9D5zQZouu2Ubktk8JpHSU9cpVAd9KgtVB0EA+UOVMloCR1MmmjRpYoYPH24rBHT9rbfearZs2VLkYHDKlCm25DSoMuf++++3P2u7V199tTnggAPsIvfJ0rx5c1O6dGkTFu7MvDKzYRTm8Yd57ML4MyfMY0/V+Ldv326mT58ejmDw22+/jdmY4+yzzzaPPfZYuoaCLFOcJi6JlHf6t6t5hF6jR4+ONJ1JZvOYwsbmAkGdwdeYNHcWQH755z//aU4++WQ7n17z+B5++GHTqlUrc++995pu3brZRmtFpcXrtXxTvKVrVImjTKQ+G8NU1gkASI20BYPK0sSqa9XZRDXPQH4qThOXoPJOfybws88+i7sNHRgV9X4TEa/0dOXKlbYkWoGguumqy67W2gSQXxYuXGiXwylbtqy54447zDHHHGMze0899ZRp37590u/v559/tlUI+ky6++67I2v9AgDyW9r2BiohGT9+vOncuXOB33300Ud2QjvyU6xMWrzyUf+6gbrtYYcdZjPQou21bt066n60gLP+Tgdgygj++eefBcaRDLHWNFQgqIygyrQIBIH8pqycAkFR4yh9hulEUSr2hfqsU0ZQAae6lwIAkPZgUDuim2++2XZKPProo21HM9W5vvvuu7Ys5r///W+6hoIsEyuTVpTyUd3WBYKOf91BnQnX3+t+vL/TgZjm7CiTqDk8qVh4XgtIu0CwXr165oILLiAQBBBRrly5lJ0UnTx5su3ivWzZMjNmzJio/fKVV16ZkvsEAIRD2oLBo446ytx00032oF2tVbXorcr0dCB+2223JXUiO8IlViatKOWjQb/zz4dRk5mgTKQCQRdIutske+F5vd/VNXTDhg02I5jsYBNAuGjahDpqu+kT/suiE0fJoIxgvLmEAID8ldZJA+popgnzOvBWIKjGGVrjyJXKAN7SUHXaLKyM091+8eLFUdcfeOCB9gSD6x7q/Xt/JtI/tzAVC89rXqwaJamDqTIAAPKbqgS0rqA3+FPQ5j2ZpbX7AABIpbTPIFdGJFlzs5B7vKWhLqjTyQJv+ag/YPSWh6oMuUqVKjYQvOeeewosHRGUiVRpqMsISrLenzrL//3335vjjjvOHtgpIAxT63UAqTNnzpxMDwEAgPQHg4CfN7jzZ/gUUKnrnres0h8weqlLp76UEVTGT935CivJTGTheZU26z7nzZuX0JqEy5cvNyNHjrSPTaXQam4DAAAAZBOCQWRcvOBOjV70e28mL9EyTmUM/X9blDmLXhqfgrtEmtl4A8H69evbUmgAAAAg25TK9AAAf3CnTrNeWgqiQ4cOtpxTAVasMk7/37lt62/0t95tFNWMGTPijtm7lpfaw+s+9tprL9s1tLA5gskYHwAAAFBUZAaRcf7unjt27CiQHdSXbqMGLFKnTh3z22+/2ZLQWH/ntl2UJSpi0ZqFM2fOjNpuUCCojKBKShUInn/++Qk1i0nG+AAAAICiIhhExrk5em4x+HhGjx4ddRsFaT/88EPUAvIKFBs0aGAOOeQQGzyOGDGixN1C+/bta7975wx6KQB84YUX7Pe9997b9OjRI+GuoUVZQgMAAABIFspEkXFuzp4CuMJ427CLFlL2BoLSvXt3M3HiRNtARo1kNm/eHPX7onYLVdmmMncqFY3VPEaBX+fOnU2jRo0SzgjGGg/ddgEAAJAOZAaRteWiXrVr17YLMPsXTvaWibrbKVhT6aV3yQlRV89evXoFdguNR9tyzWNcqagr41Rw6ha3b9GihWnevHmBxe4Lk0g3UwAAACDZCAaR0aUkvJk2b1CkEk/R+n+6jco9vQvIx6KAUdsKKrVUIFicuXixyjiXLl1q3nnnHXPeeeeZ3Xff3V5X1EAw0W6mAAAAQLIRDCLtYjVMiRcUqdNmIjSnTx05FUx6s4xavL64GTd/xlKXlyxZYucIatF7laSefvrpxdo2AAAAkCnMGUTaxcq0KWPYp08fU7duXfuln90yC/55dBUqVLCNYtRAxkvzA12gqaYvCgoVCO666642CC3Osg0KInv27GkOOOAAu82rrrrKPP/88zYQ1BzBU045pcjbBAAAADKNzCDSLijTJgrWvKWg+llBnLKFQfPqlEl0JafDhw+PahSj8tKpU6faLKELDnWdOpaqwUxQE5hYdLv+/fvbnytVqmQzgmpas88++9htlSlTJknPDAAAAJA+oQ8GP/jgAxssrFy50h6c33777TYThOwVq2FK0Dw/d50rIXXB39FHHx35W1da6oI+0e90WwV/XlqWwt0u0Xl62s7AgQPtOoJHHnmknRdIIAgAAICwC3UwuGzZMnPzzTebZ555xpYLjhkzxpbxTZo0KdNDQxyx5gYGdRP1l4fGmm8YFGDqtrHWLdSyE8oa+jON8bqJqgGNAkFlBQkEAQAAEHahDgbr169vJk+ebJcM0Pyt9evXm6pVqyb1PmbPnl2sDpElpcXLZdasWSbMivI4NC9v1apVZty4cfbyCSecYK/z/u2ECROi/kaX3e8vv/xy+yU//fRTgdt6bdy4MSqoXLNmTaQU1M9t58UXXzRHHXWUWb58ue0gGjb5+J7KZjyOxNcTBQAAqRHqYFAUCGrtuTPOOMOULl3aDB06NNNDQhEOIt1i7srsKqt766232q9YdDu31p+7nOhtmzVrZsqWLWuv969BqDHEKhF129GcxHfffdcGqAAAAEDYhT4YFM3f0sH6W2+9ZTtQjh8/3uyxxx5J2bYWEVeQmW7uLHvLli1NmMV7HCrT9C7mXr169ULn8Q0bNszezj/fMGjdwqDbulJQ3ff3338f2W6nTp0KjHHBggXmzTfftO8pFzDqdkVpPpOO9RkTlQ/vqTDhccS2fft2M3369KRtDwAA5HAw6OZunX322Xb+oLpGnnjiiZkeFoq5xERR5xt6O4Ymum5hrCY2jspM1XxGi93r55tuusmWC2fywD3WfEkAAAAg79YZnDJlirnkkkuirtPcwSpVqmRsTEicvzmMXrvC1gHU7xX8aRF6fdflWEGl5pC2bdvWLgeh72om4/5WgZUCQC0/4QJHZ/78+eall16ygWDTpk1N165dMzJvNBnBMwAAAJCTmUHNAVOJ0jvvvGMzgcrkqLyIpSXCQcGYOr+6+Xv6riAtVrZLgd9hhx0Wub3LjsVat1Alnd5ta9H4X3/9Nepv/felQNC9jxQIduvWLSNlwkFiPU4AAAAg74JBzQt87LHHzL333mvuuusu06JFCztPLBPzuVA4BXM33nijGT16tL2sjJsWlY+X7fLOk1Pm0N/4RddPnDgxsORz3rx5UbdVx9B49/Xjjz+al19+2QaCOtGg8WVLIJhIaSsAAACQN8GgHHzwweaNN97I9DCQAAV1Q4YMiVzWz/4srisVdQG9d55cEAVFuq1bV1CBkisBbdKkSVTwqGYyLjPo/tZryZIlWRsISrw5kAAAAEBezRlEdgmaz1fYHDdlBr0BoSsVjfc3jv7O201UQaPKKPVd5aRaBkK30fIj+q6OpVq+on379va7P7OmslItUZKNgSAAAACQbKHPDCJ7+Ltdau7d8ccfbwMvOeSQQ6LmvMnSpUsLbOezzz6zwaQrDfVSUKcAUs1d9N1lAf1Bo4LKgQMHmm+++Sbqen9mTdnAevXq2W2pSUy8dQsBAACAXEIwiKTxB2Tq3unWEdRczk8//bTA3+g2fgoAvaWhCgC1WLybJ+cNOrWMSFBzlaDx+M2dO9e88sorplGjRqZ79+4F5i9m41qBAAAAQLIQDCJpggIyt1i7AiB9T8SKFSuiLisQ1BIQsYI8ZSBnz54d1ZnUjSeWOXPmmFdffdXs2LHDBmOlSqWnYpq1AgEAAJAtmDOIpFGWSyWhderUibp+2bJlUY1jCrN69eqoy8oMeuciqtzUn10cMGCAXXfymmuusfevL5WSBq1bqMDRBYL777+/Oeuss9IWDLJWIAAAALIFwSCS3u1y4cKFUUHh2rVrbWDmVaNGDVO7dm1ToUKFAttRkOYPmLzNYcQfcOo2un+Veio41JcCUG8zGhcIamkLFwieeeaZgYGgvxnO1q1b4/4+KOgM4s9WslYgAAAAMoUyUSSdAiOVbK5atSrw9wq+/Nm/eL777ruoy5onqDl+3nmFLqiKl3lzGcGdO3eaVq1a2c6hsTKC/nJOrVHYv3//mL9PtNyTtQIBZILmSKuCQiXyVatWNX/7299Mt27dMj0sAECGEQwi6bREg39xeC8FYP7sXzz+26rBjDqOus6ihx9+eCSo8ncs9ZaUVq5c2ZQpU8auI3j66afHLQ31B5XvvfdepCtqSco9WSsQQLrpBFifPn1Mz549zQsvvGDmz59vA0F9PjZs2DDTwwMAZBDBIJJu3rx5Ba6rWbOm+f333+1C8D///HPMrKHKRrXEg75v2rTJbNmyJfI7rRfoX0hecwSlY8eOthRV246lfv36plevXmaPPfawgWC8zp7+ZjjKZCoT+NxzzwX+nnJPANlKn6lvvPGGKV++vL28fv16eyLNXU4GVV7ofsLClf7PmjXLhFGYxx/msQvjz5wwjz1V49fJvpIiGETS6QBj8+bNkcsqSdJ6fi7wqlu3bsxgUAGj6O+V+fMGfgrk/Bk4zf8LWp7Cu47h8uXL7VqCbq5iIqWeCgzVpdS7bW83VMo9AWQbnRBzn6Feqohw87P1eaV53MoUat42ACC/EQwi6fzll7rsDbxcUOadN6jbKCvnPZDRmWuVZnoDLm0naPmKIGoQ06ZNGzN06FC7xqEWlPdm/4KWqPDel39eondBeso9AWQbdVTWSTM/Ncq6//777c8TJ0601RuXXXaZ/Uw78sgjk3LfzZs3N6VLlzZh4c7Mt2zZ0oRRmMcf5rEL48+cMI89VePfvn27mT59eom2QTCIpHFll+vWrYu6XgHe8OHDo67bsGFDgXmB/jPayuq5gxcXwPkzcjoTHrRshQ5yNC9QZUvKLmobH3/8sf2dC+L8pZ6uC6m7zntfKk/1zhkEgGyjcnk1iomnXLlytoHWaaedZj8XkxUMAgDCiWAQSePP/jlByy6obOnPP/8scL1bMsIFZm57CuBcsKnmMWoi89JLL0UCP21PZ0eUTdQi9ccee6zNNn7zzTfm7bffjtRUe7OBLtjzl4O623mzf2GtTweAjRs32u7JY8aMsWX7os/Q6tWrZ3poAIAMY51BJI2/7FINX7xz9EQBmoK3Ro0aBW5D8wnV6MVLwZ832NTSEpq/t3LlSvuln4844ggb+D355JORQPDrr7+OCgT9jV5csNegQYMC44jVEKa46wsCQKaok7JOtA0ePNiehNNn4wcffGBOOeWUTA8NAJBhZAaRNP6yS81d0fp8I0eOjCoH9TZicQ1nXNdQlXSq86iXW7A+3vIN+p3apb/++us2EPzqq6/MO++8YwNBbW+fffaJ2ejFP241ronVEKa46wsCQCb95z//MXfeeaf9vFNg+MADD9jydwBAfiMYRNIogFLgpg6fop+1sLG89tprUR1Gvfzlov7bqfQzKGjz0u+U4VPGsVKlSubNN9+03fN0sDNhwoRIaVSscUvQEhN+xV1fEAAySQHgsGHDMj0MAECWIRhEkcVan09fCtzc/Ds1dlGjmP79+9u5KUHzCb2ZP6dp06ZRS0ro9yrL1ALJKjH1ZhYV8F166aV2DJo32KNHD9vR7qyzzooar0o6YwV73rmB3nmJul+3qL0Wa1bjBdYXBAAAQK4gGESRxSuV9GfK1LBAHnnkETNp0qSoIC+Iun8qmFOppso9tT6h+xvdlxaZP+aYYyKB3XnnnWebIyhQ82YREx1vvNs6mqOoclcFtawvCAAAgFxBMIgiZwDjlUr6M2daLkJzBnfffXezYsWKQu9Tc/w030801+/XX38tEJhNnTrV/uw6hcpee+0Vc/5LUUo7Y/3OZSNZXxAAAAC5gm6iiMllyRTc6bsuB5VGei/fddddkayel+YR+pdvKIw/EPTel+sUKu3atTP77bdfzO3EG6+/O6hKUYN4F5wHAAAAcgGZQcQUK6OmgE8ln/PmzbPZOF12BgwYUGgpqJvrp0Yvq1atilynrFu8pRpcl08FgmPHjrXXbd261d7nuHHjYjZ+8Zd2arxuDqHW2vKXoWpx+aA5gwAAAEAuIRhETLGapXgDPn2/4447bNCk4GrRokUFlo3o2rWrLRNVQxn//EAvZRO1LuHq1asj1+myOoQqIFMQpy8FkqKlJJ5//nn7s8apRejr1asXCeC8jW28pZ0KBGM1s9F29Lcay1FHHRXZBovOAwAAINcQDCKmWM1S/BnDeCWge++9t2280rhxY5ttc0Gk5hLqyyvoOgWGF1xwgQ3mbrjhBptNlClTpthsoJdbhN7NLZSg+X3x5gwqU8g6ggAAAMgHBIMotHHMxIkTo8ov463356dS0qOPPtp07969wDzCRA0fPtyWbL766qtmzz33tMtUfPjhh4X+nYLPIPHG/+OPP0ZdZh1BAAAA5CqCQRR5KQZ/xlCBmr8E1NsdVNk9/T5o2YdEg1O3fZf5S4R//UL/+EeNGlUgo+nPTLKOIAAAAHIVwSCKvBSDfw7e+vXrbRZu7ty5ZseOHTGbwMQKzvyU+VPgqG6i6u7Ztm1b8+yzz0YFarVr1zbdunWz5aCuA+iIESPM5s2bI7eJFXy68SsoPOyww2I2vKlTpw7rCAIAACBnsbQECiyxoHlz/i6eCsg0X0/fFfx5uYYyCtbidQNNlBZ4VyDWvn17c/LJJ9vAb//994+6ja7zBn2DBg0yvXr1irqNGsHEo6BQcw/VPVT3pcfppdLWoO6kAAAAQC4gM4gCpaGiwKhs2bK2TPLjjz+OLLquoE/LSSxZssQGfp06dTLTp09P+njUMfTEE0+0P3/yySdm2rRpUb+fOXOm/fKWssZqeBOPN8vpnytJVhAAAAC5jGAQgaWgCgSnTp1qf3YdPB2Vb6q8UmWh/jl2sSiT99tvv5ktW7YUelsFYi4Q1HqGCkYTaTIjsdYaTIS//BUAAADIZZSJIrBRiveyMoF+riw0SNBcvfPOO89UqVKlwPX+tQYVZLpAUF1MEwkERXMFldlUZg8AAABA4cgMotASywkTJtiAUBnBRCir6G0Wo4ybFovXovCF/Z1rBqNAUF9FxVIQAAAAQGIIBhG3RFLz6NQgpmHDhqZ+/fp23UBvx84g/lJQN7dw48aNgUtPOGpco66hTZs2jbkOoOYyxhsDS0EAAAAAiaFMFAk1ltESDq55TGG8AV4iZaV77LFH5Gd1KvUHgq6zqLp+uiylP0B0v09V05etW7eagQMH2m6r6rqajK6pAAAAudSRnmOk8CEziCj+jppaP9BLWblWrVqZn376yWbyVNqZaBOZIEceeaQ5+uijzSuvvGIb0gSpW7euLRlV9lIfMt51AdV1VEtIaGmJVC4DoYB45MiR9mcXrNJsBgAA5DtvR3qOkcKHzCAC/6H1z6zv/oXiVZ753Xff2VJOlX1eccUVxb6vjh07mmOPPdaULl3a1KhRI3K9gjrvuoIK/lxjGP+cQAWiQ4YMSXnjGLe0hsPcRAAAgILHRBwjhQvBIOL+A6szqMovK1asGHW9ArQbb7zRZhKDuocW5qijjrLzCGX8+PFRGUhtc8GCBYHjijUnMNUfPK1bt466zNxEAACA+B3pkf0oE0WBf2DvnD2VYCrVr/X+vOWZ8vTTTxerRFRlofqSDz/8sEApqvi36z5Y3JzAUaNGmV9++aXA7xMVtMB8vDJTBcSuTJYF6QEAAArvSI/sRzCIqABJZaFq2CJdu3a1/9C6fvny5QVuX5xA8JhjjrFZQRk3bpyZMmVKzNuWKVPGVK9ePTIOb9dTXfYHc6msby9Xrpzp37+/admyZZHuBwAAIB870iMcCAZhKeDTgu/e7J/KP13TlpUrVyblfipXrmy/f/DBBzaQ06LzmjOo+/J3n/rzzz9t9s+NI5kfPNS3AwAAIN+Ffs6g5pudeuqppm3btuass84yX3/9daaHFErKlPnLQFWKqTbB+p4sb7/9tu3K6YIvLUOhbGS8NsSpCNSobwcAAEC+C3VmcOnSpbZ07/HHHzcHH3ywGTt2rLnqqqvMRx99ZCpVqpTp4YVKUMClrJx3Xl6itNxD+fLlzZo1a+xldQb94YcfzI4dO2zwp2UpiiIVgRr17QAAAMh3oc4MrlixwpxzzjmmXbt2plSpUqZLly72+kWLFmV6aKFTWMClcs5EaS6hCwS1dITm/ClrWxQqA23Tpo1dUF4NZvr06WO/krWgqbavAFCPWwGhMqOJbJOFVQEAAJArQp0ZVBCoL2f69Olmy5YtpmHDhkm7j9mzZxcpEEqWrVu32u+zZs1Ky/317NnTBnCvvvpqYICjjF5RHXfcceaII46wPy9ZsiTqd5onuH379shlrVu4ePHiyH3ru9YzdLf58ssvI7dVwxeNVVnhkhg4cGDUQvLxtqnXQ18KTufMmZPUceTqeypVeBy5/ziK83kDAADyLDPopUBCmaPrrruOEtFicN0ygwLf4qwjePzxx0cCwXfeecdMmzYt6vfeQLBatWq2c6h/gXvvbQpbBL44/NsobJta3N4FgskcBwAAAJAJoc4MOjNnzjS9e/c2PXr0MJdddllSt928eXObxUo3d5Y9XUsZuHX31MGzpE444QTbmdQFgt6sXpB169bZr6LQgvUlfW60Db13EtmmXo/vv/8+JePI1fdUqvA4cv9x6ESQKj0AAEBqhT4Y/PTTT+2C4DfffLPp1q1bpocTWt519/z8Gbt4NEfQBYJq6PPVV18VKAktrtatW9u5fq7hS1EXji9pExndvzd4VMkojWcAhIk+N08//XR7ArWoc7kBALkn1MGgGsVce+215v777zcnnnhipocTGi6IUmMWBXoqA/XP6SuuhQsX2uYq77//fmSZj+IEglr4fuPGjWbz5s2R6xToTZ06NfIYdD+uTFPz91566aXICQFlI4sTIMajkw7Vq1cvdvAJAJn2n//8J2mf9wCA8At1MPjyyy/bhjHKCurLGT58uF1qAkXPApbUggULzMMPP2w2bdpUou3UrVvXfvcGg96Op3oM/vl6K1eutPP6HAWIEmtxeu/zUNht3bzKkix0DwCZpLnb+txUVQMAAKEPBtXwJCydHLNJshdxP+aYY2znz9WrV9vLJQ0Ea9asab799tuodQsvvfTSqJLMRB9DvNv5f5eKxe0RLiUtPQYySZUeWtrHTw26tM7r3XffbR555BEzYMCApN93pjpv52s33zCPP8xjF8afOWEeezZ33w51MIji0UGuy4SV1CmnnGKX99CZZmXltm3bVuxtaa3Iq666yo7t119/jVyvgxuVsnoPyhN9DPHWT/RvIxWL2yNcipotBrLJlClTTK9evQpcf+aZZ9rKhjPOOMPsu+++GRkbACA7EQzmIWU7hg0bFngGOVE6A6xA8JBDDrFnJSZMmFDkQFDBnXdNQ525VtB3+OGHF+hA6s/a6THoLPjo0aPt/WuOYdmyZU379u0LzBlMVgMZ5D6yxQizjh07mrlz5xa4XvPDdZLjzjvvTNl9Z6rzdr528w3z+MM8dmH8mRPmsWdz922CwRyiwOrGG2+0AZJ07drVDBo0KCqj5srgtBZjcYNBfyD4xhtvFGu9PY1FJaDecejgW81nXnjhhajsoD9rp8c0ePBg+1Vc2ka8rI+3ZLBJkya2gQxyG9li5CJ9ps6fPz9yskyfueqMrC+VjgIA8hfBYA5R4OJtoKKflWnzBjwKFr23KU4geOqpp9oGPSUJBB3/uoY6+NZ8Fm8gmKklHIJKBp977rm0jwPpQ7YYueiee+6xX07Pnj1t6ShLSwAACAZzSFBJm/86lzUsLq0hqEBQJZ0KBL3r7pUkGKxdu7Y577zz7MH30UcfHXUblX9moomH/7krSdCLcCgsWwwAAJBLCAZzhEoag+bsJbvMTWsHNmvWzLYoVwfRZFm7dq2ZNGmSnfPiX+Q+U6V6/pJBLToPAGE3cuTITA8BAJAlCAZzhEoavcsxqDxUXeX8ZW6aR1iSMlEFnU8//XRSWtn6M4Te8Wt5iYYNG9pmMkUt1UvW8gDekkHmDAIAACDXEAzmCH9Jo7Jr6uzmgiI1exFluhRoeefkFTZHsEuXLmbVqlWR+0h2IBhE4zv//PNtyZ6Cu379+iUc3Pnn+injqJbr8f4mVgDpSgbDuqYNAAAAEAvBYI4IWndP8wN/+eUX+7P/d+XLlzdbtmwpNBA8/fTTTZs2bewcwXnz5pk1a9YUOhYFoW7xYQWOanubyNISfi74LOrab/7AWBlHbSPe37C+HAAAAPJNqUwPAMlx1113mRo1akRdFy+Dl0ggqAWKXSA4ZsyYhAJB97fKTOrLGwiqdNUrXiAomgOp2xR17begOYaF/Q3rywEAACDfEAzmiDvuuMOsXr066rpatWoVa1suEFTDFAWCyjAWpUzS3wDG8QerhXEZPX9wV1hDGZV4ajmKovxNUe8DAAAACDvKRHNE0JIRK1euLPJ2SpUqZQPBAw44IBII/vDDD0kZ42mnnWYqVqxos27K+nkbxsSi206cOLFIa7+p/FRzBP1zAAvLrGpuoUph1SxGlwEUT7KaOAEAgNQiGMxhmzZtKvLf7LvvvjYQVHmnAsHZs2cnbTxajmL69OmRg8Xq1aub33//Pe7f6EDS28gl0YPMoq4Xp8yqC071XZcHDx5cxEcIQJiDCwBAOFAmGlKuw2aHDh3sd2Xz/CpVqlTk7SozNm7cOPPqq68mNRCU+fPnRwV0lStXjvp9mTJloi7XqVOnQEbPHWTqAFPfdTkVmdWgTCuAxDAHFwCAcCAYDKkbb7wxKijSPD8tGeFVt25d87e//S2h0tCyZctGLqvEcs6cOYX+nesYWpSsozegc2WsCvq0hl/v3r2jbt+9e/cCWT8OMoHsxxxcAADCgTLRkPJnrl5//XW7SLt3/UB171SGr7BA8OyzzzZVqlQxzz//vNm6dWtC91+hQgWb2SvKvER1Nw3qDtqgQYPIeoIac7x5fv4lNJJ1kNm1a1czZMiQqMsAisf97yY6ZxcAAGQGwWCIKFhSRlCBoD8IUydR/9IPWj7C32HUHwgq6GnRooXtAKoM3eLFixMai+b6ufl+CgwLm/snM2fOjHQH9QZ0bgkJN8/PlZEeffTRBeYFFucgM5F5hoMGDSo0EAWQmKLO2QUAAJlBMBgCLph56aWXYmbigpZziNcF1B8IvvzyywkHgomWi+p6/1qHrjuoOnd6G7Z4F4WP13yiOAeZiTSz4OAVAAAA+YZgMAS8wUwylC5d2gaCzZs3jwSCP/74Y7G3p8zg5s2bC1wftOi96w7qnaMo3tLRoHmBLiD+7LPP7JiVxTv88MMTalnPPEMAAACgIILBEEhm8KJAsFu3bqZZs2Y2qBo1alSky2csWgLCX4LqzwBqgfoZM2bEDRibNm1qJk+ebNq2bWuWL19eIEh0ZbDfffddgd8FBcRffvml/V5YRu+QQw6JKkvVZQAAACDfEQyGgH+OXUmo6Uv9+vVtIKiy059++qnQv9F8wPLly9s5iEFWrVplzjnnHHPkkUdGGtvUrl07Kjjcb7/9AheZV5Co5jUajwJBbxMX93sFiWqQE4QsHwAAAFA8BIMhcNddd9lOn/GawSRq/fr15tlnn7VB4YIFCxL6m1hBoNfjjz9u5x+qNFSZwnbt2tnrVX669957mxUrVsRtRKMgUA1sgn7/xBNPxLzfRLqJugxirMsAAABAPmKdwSymZR60oLzm9pUkEFRpqNYcdLT8RKKBYKK2b99uyzvV4OaXX34xw4cPt5lBBXNas3DTpk2FbiNojmEQZQtV6qm1CRPp+lnYmmfKPOp57tChg/2uywAAAECuIzOYxTRHbuTIkSXahhqtqISzUaNG5oUXXjCLFi0ymeAP9DSuMmXKRGUdFVCqvHTjxo12uYmgDqlyxRVXFKnzZ2HLUfi7jarTqRrcxFqGAgAAAMgFBINZLF5DlkQo4Dr33HPtfL0///wz5hIQ6aCg78ADD7TlosocKtDzB3ve7Keyf97fK0hs2LBhVDCXyPqBiSwb4Z936OY2xlqGAgAAAMgFBINZxgU4EyZMsNmxZAWCmcwKOsq2NWjQwAaDhfEvYn/eeecVCMoSWT+wpA16aFADAACAXEUwmGX8SyjE6+IZLxDs3r272XfffW1A+eKLL6YkEFSZpwJNP2X1NGZ/aajm+WmdQC9lC+fNmxe4TqGjxjJBcwOTtX6gt4xUz5e362kiDWoAAACAMKKBTJbxB0vFCQSVRXOBYEkygoWVlfoDQd23gjtl9byBoBrYXHPNNfZnb6Cl206ZMsX06tUrajsKgL0U2AaVfxbWGCbR5jCujHTq1Kl2PGpM0759+4Qb1AAAAABhRGYwy8RqmpKoHTt22C6kLhBcvHhxsbajwK5Jkybmhx9+KNLYg5aQUFA5aNAgc/TRRxcoG1UgpoBLTVtcoKgAuGbNmnaO4OGHHx4zICusMUxxSkkLm18IAAAA5AqCwSyirNXy5ctLHAxq4fcaNWrYxeCLS4GdAkFl7xQYzpo1q8A8viBB8wG1LWXm/BSw6jErAFNg6KXlL5RRjCeRwC1ZpaQAAABArqFMNIsoi6V1+rwKC4jc3D2VNbqyTgWEJQkEvRSkTZs2zVx66aVR16u75+WXX26DzkQ7o/q7oyoTqMesgDCoWY4CS2X1dJsgiZSAJlJKCgAAAOQjMoNZJChrpbX3CgsEe/ToYdcR3H333c24ceOSOqZYwZOauqj0s1KlSmbUqFEJdQiN9ZgV7HnnEgbdJkgiJaCJlJICAAAA+YhgMIP86+Sp22asJQ5iBYLnn3++nVuneYKzZ89O2tiU+VMjGhc8ffnll1G/V5avU6dOUUGcykmVSUyknNTR4/YHe9qOd+6knpfiloAyBxAAAAAIRploBrnMlgJAl+FSB8uKFSsW+rcKulwgqKBy5MiRZunSpUkbW7du3WwQ5bp4BmUI/dk8BXAuENTyErGoOYwCPNet07/tli1bJjRGSkABAACA4iMzmEH+TJayb1reQLxrDcYKBLWAuwsEf/7556SOzWUCXfZSS14oiFNjl0QooFMn0AkTJtimOKtXr478bp999ok8zqBSTv/yGgqWNSfQW+rpupB6/44SUAAAACBxBIMZEtQ0xWW2FNSsWbPGvPvuu/a7n8o3UxkIesfinZfnMn6JlIEqEFRmUV1IBw4caMfp33asUk4Fft6yVGUcg+YGUgIKAIn5+OOP7Xqv3s7Nzz77rDnggAMyOi4AQGYRDGaIv2mKlnBQEKgA78YbbzTvvfeeXbi9XLlydj6gl7p71qpVy64jWNKlKIIaw3Tt2jUq6+ZVpUqVuMGgf66hqBy0evXqCWfw/Bm/Tz/9NOr3/swhACC+OXPm2M/m22+/PdNDAQBkEYLBDFDApw6cQQuwKys2ZMiQuH+vRjE//fRT4HIMJaWuoGrg4p0r6G1qo0BRtJbhb7/9ViAw1BxGf7ZOAW1RMnj+jF/btm2jfu9tLpNocx5XWgoA+RoMHnnkkZkeBgAgyxAMZoCCFP9SDK50MqgjpoKpzp07m/Hjx5sNGzbY61IRCDreMdx1111m0qRJZt68eaZJkybmnnvuMVWrVjWDBw+26/v5u5+WtIlLUBCn4NTLf7m4y04AQC7xNvHyd55WMLhp0yb7uagmZVdccYU5++yzk3bfOknp1roNA1dxo6kMYRTm8Yd57ML4MyfMY0/V+FVFWFIEgxngD/hUmulKI/2ZOAWCPXv2NPXr17ellsOGDUv5+BRoKihTJm3AgAGRclZ912UXVPnH6kpdS5KpCwriNP/QO4dQlwuTyLITAJBLpkyZYnr16lXg+tNPP93uZ0477TTz+OOP2wOR3r172/1K+/btMzJWAEB2yJlgcMSIEWbu3Lnm/vvvN9nOH0R17949EhgpSHrppZfMypUr7XUXXHCB3WHrbO9bb72VlvEp6NO8RWX/4gVVQd08Y5ViJpqpC7q/iRMnFrifoj7HLDsBINd17NjR7gcLoxN3CgzV7TlZwWDz5s1N6dKlTVi4M/OJLmWUbcI8/jCPXRh/5oR57Kka//bt28306dPzOxjUk/DUU0/ZwEJnP8Mg3pIICqbOPPNM88wzz9iM4J577mkDweeee65Aaam7vbJuyaY5gQoG/UGVN2tYlG6ehWXqXOZw8eLFUdfr/ovTNZRlJwDg/1u1apXdh9xwww2RUk59lieypi0AILeFPhjs37+/2bhxo10kPRXz6FI1D+Lyyy+3X6JmMF6TJ0+OCgTV/luZwiAlCQQ1B1BzAYPo/rSWoVqRv//++3a+icsa6qyyGt60bt3adgpVKWth9dG6L29Qqcvemmn/8hM1atQwJ598sn0eiltbHe85zrc69Vx5DMLjyC7ZOgcC/7P77rub1157zX6uXnjhheabb76xSxepCgUAkN9CHwzedNNNdpkFZbFSsd5eJqjcRoHg5s2b7dncWIFgUamER81ftF2dEVZQd/DBB5vvv//eBpXewFAHYy+++KL9G++6VOICw5kzZ0YC8sIoaJQZM2ZEgkgvXe9Vr169hLYLAIhPJ+yeeOIJc++995qHH37Y1KxZ09x33332pBwAIL+FPhhUIJhK6Z4HoaBM8zgUrOnMrcp7kqVUqVI206a5e7qfl19+2QZlCsR0uVGjRgVKURUgdurUKRL4+en3jRs3jtkcxlsfrcA2Fv996HI21YSHvU49Vx6D8DiyS7bOgUA0LS7/yiuvZHoYAIAsE/pgMFfs2LHDBmsKqtasWWPnDDoKDHUG17tIfXEDW/+C7cOHD7ffFcCpkY1r8uJoDt8hhxxiy0XV0VOluN5xKPhLxjIOzPEDAAAA0otgMINc05SvvvrKHH300aZLly6BSyDss88+NluobFlJAsJ27dqZr7/+Ouo6lYy6QE4BmNapevXVV21Aqp+VKRwyZIhtS65gUesOankJb9CmsZd0GYfiNImJhQXnAQAAgMIRDKZBrOBE12ndQE3o10LqY8eOLdC9U7777jtzxx13mIMOOsj+rBIqzQEpavMYbX+vvfYK/J3GpjFp7qXG4s8QKih01/mDtmxbxoEF5wEAAIDCEQxmIDgZNWqUzbJNmzbNBoLKum3atMl2eHvnnXdsJk5ZOS8tn7Fly5bIZQWC6gynbqP6SoSawvi3GxTAxcvsBf3OX+Kp7GG/fv3sZZW3+pvFpBoLzgMAAAB5FAz26dMnazOA/mBEWTZlBHv37m1bfmtpDC0foQXmdftq1aqZX3/9NepvvIGgoyBQ5ZzuPjW3TyWesbqPKuj0lpkqA1i9enW7LId3jl5QdtL7u8JKPBUI+jNz8ZrHJFu2ZSoBAACAbJQzwWA2iFWe6A9O1BBGGUEFgir5VPmmAjNl7davX5/wuoYVKlSIuqxtKLBTZtFPrcT9S0To/hQ4+pvKeDN9CjBFzWOCGrsEBcD+4Ne/bESq0YwGAAAAKBzBYBrKE10wonX7tFREq1atTO3atW0gqAYuc+fOtb9XEKe1oGKVcvq57qPeAFTbVpDoLx1t2LChOfzww21Q56ds4Y033mjnCxa1mUtQAOwPfrWuYDolsxkNAAAAkKsIBtNQnuiCk48//tgGg1OnTjVlypSxy0n4A79EA0HRPMPJkydHXacGM0EUCLqgVHMW/esJqrxUmcWiduAMCoAnTpwY+TkTcwYBAAAAFI5gME3licoALlq0KHL5008/teWivXr1ijk/rzDK/mkdwHgUdGpuogvuFJTqZ80V9GYPNW8xVgfOeEs1BAXA3sycW5AaAAAAQHYhGExDeaICLTVQ6dGjh13k3WX/lDWLl60LovmE6grqDTLjufrqqwuMSeO89NJLo+YWVq5cOSo49Gb84i3VwPw8AAAAIJwIBlPMdQpV18/GjRvbhd/VUEWBoBaSV2AWtNi7P7unTF7Xrl3tZW8Q17Rp06gOoZqfd+SRR8Zs+OIMGjQoqixU9+ndbrylJryXmZ8HAAAAhBPBYJoCQXUOveiii8wNN9xQ4HbKvAV1AA3K7qlk0xvEaU2/AQMGFHmunz+I82833lITLNUAAAAAhB/BYIr89ttvNhBcu3ZtJBDU+oFB4i2KrrUBvYFZUCYuGZm5eBk+SkEBAACA5IrXlyNdCAbTEAhefPHFpmrVqjFvH2+R9+7du6f9TeFHKSgAAACQXPH6cqQLwWAKaMkHNXZRAKiMYLxAMNYi75988omd/0cWDgAAAMg9n8fpy5EuBIMpUK9ePdOzZ0+7dERhgWCszJtbksGbFXSp5M8++8w2fNEcP7d+YKazhwAAAAASlw19OQgGk1Tjq58VoCkQlD333DOlqWRHXUNTlVLOhjpmAAAAIBf9Kwv6chAMJiEwmzNnjl2nT5nACy+80NStWzcl96mMYJBUpZSzoY4ZAAAAyEW7ZUFfjlIZvfcQcwGYykDVIKZ06dKmfPnyNiBMVhMarStYqVIl07ZtW7N+/foC6w+mOqWcDXXMAAAAAFKDYLCYFIC5QFBLRmzfvt3+XKVKlaRs/9JLL7UZRzWi0aLynTp1snMEvRR49u3bN2UpZX+QyfqCAAAAQO6gTLSY+vfvbwO/UqVK2UCwT58+RQoEvfPxXAdRzf9z9cJLliyJuv28efNMr169InMERZdTmVrOhjpmAAAAAKlBMFgMGzZsMC+99JINBKtXr26Xj9CcwZLOx/P+vPfee9vMoNOkSZO0B2fZUMcMAAAAIDUIBouhQoUKNggsU6ZMsQLBwubf6XdPP/20LRVdunSpDQQnTJhAcAYAAAAgaQgGi0FB4HnnnWe2bt1qG7wkY10R/+9Ucjp69GjTsmXLEo4WAAAAAAoiGEzQ2rVrzezZs81hhx1mdtllFxsQ6qu4vCWfQXMGf/rpp6SNHQAAAAD8CAYTsGbNGvPss8+ajRs32o6e7du3L/E2KfkEAAAAkEkEg4U49dRTbfCnZjE1a9akbBMAAABATmCdwUK0bt06snyEmsUUd45gcWkJin79+pkOHTrY77oMAAAAACVFZjDAzp07Iz9rYfkVK1aYH374wZZ2KihMNjWiufPOO820adNMu3btzD/+8Y/IGG6//XbzxBNP2J9nzJhhSpcubQYOHGjCwj2OVDxv6ZQLjyMXHoPwOHL/cXi35f08RnbxvjZhex+H/f8vzOMP89iF8WdOmMeezfvLXXaypy1g27Zt5rvvvsv0MAAg77Vq1cqULVs208NAAPaVABD+/SVlogAAAACQh8gMBtixY4f566+/7M+aL6ilJAAA6aHdkj6HRR2c9TmM7MO+EgDCv78kGAQAAACAPMTpVgAAAADIQwSDAAAAAJCHCAYBAAAAIA8RDAIAAABAHiIYBAAAAIA8RDAIAAAAAHmIYBAAAAAA8hDBIAAAAADkIYJBAAAAAMhDBIMAAAAAkIcIBgEAAAAgDxEMAgAAAEAeIhgEAAAAgDxEMAgAAAAAeYhgEAAAAADyEMEgAAAAAOQhgsEsNmLECHPzzTebsBo/frw59dRTTdu2bc1ZZ51lvv76axNGH3zwgTnppJPMgQceaM4++2zz7bffmrCaP3++adWqlVm2bJkJo4EDB9rx67XQV/v27U0YLVmyxFx00UX2MZx44olm0qRJJmzeeuutyOvgvpo2bWrefvvtTA8NyOl9bJj3rbmyPw3rvjTM+9Aw7zffyvb95U5knb/++mvn0KFDdzZr1mxn//79d4bRkiVLdrZt23bnF198sXP79u0733zzzZ3t2rXbuXHjxp1hsnTp0p1t2rTZOX36dHt59OjROzt27LgzjP7888+dXbt23dmkSRP7uMLo4osv3jlu3LidYab/h86dO+987LHH7M+ffPKJfY9t3rx5Z5g9++yzO88999yd27Zty/RQgJzdx4Z535or+9Mw70vDug/Ntf3ms1m2vyQzmIX69+9vz/R169bNhNWKFSvMOeecY9q1a2dKlSplunTpYq9ftGiRCZP69eubyZMnm9atW5tt27aZ9evXm6pVq5owGjp0qDnooINMmM2ZM8c0a9bMhJnOhP/xxx/myiuvtP8bRx55pHnppZdM6dKlTVjp7PjgwYPNAw88YMqUKZPp4QA5u48N8741V/anYd6XhnUfmkv7zWVZuL8kGMxCN910k/2wqVmzpgkr7ai0w3WmT59utmzZYho2bGjCpmLFimbu3Ll2B/bggw/a1yeMO4D33nvP9O3b14TVqlWr7MHDP//5T9OhQwd7QKT3VRhfi8aNG5s777zTPo4zzzzTbNq0yZQrV86Elf4vzj33XLP33ntneihATu9jw75vDfv+NMz70jDvQ3Npv/lgFu4vCQazUK1atUwuWbx4senTp4+57rrrTKVKlUwY7bPPPmbmzJlmwIAB9rGsXbvWhIXOwN5yyy127LvttpsJq3Xr1tkDoSuuuMJ88skn9qx+79697fVhsmHDBjv+li1b2u+XXXaZufrqq+1OOoyWL19uJkyYYC655JJMDwXIq31sWPetYd2fhn1fGuZ9aK7sN5dn6f6SYBAppQ/87t272zNQ+ucNK6Xy9aUJ73vuuaf58ssvTVg8+uijdgcQ1rIWR5Otn332WTvxumzZsnZHpoO6b775xoSJ3kf16tWz/xd6HJ07dw7l43DGjh1rS3aqV6+e6aEAeSPM+9aw7k/Dvi8N8z40V/abY7N0f0kwiJT59NNP7dmP66+/3p79C6MpU6YUOIOjs4NVqlQxYereNnr0aHPwwQfbL9E8k6zpYpUgzfF58cUXC7wWYSsTadSokS1v8dqxY4eaeZkw0lnaE044IdPDAPJGWPetYd+fhn1fGuZ9aK7sNz/J0v3lrpkeAHKTJrNfe+215v7777ctgMNKE61nzZpl3nnnHfs4Ro0aZbZv327PrIXF+++/X+DsoNocazJ/mOhs4KBBg0yTJk1MmzZt7E5NO7JDDjnEhMlhhx1mJ70/88wz5sILL7QHEqtXrw5Vi2/vzlj/H5r/AyD1wrxvDfv+NOz70jDvQ3Nhv7kji/eXZAaREi+//LKd1K41nLzrqnz11VcmTPbYYw/z2GOPmeHDh9tJy+PGjTPDhg0L5XyBsNPaSHfddZeds6Gzsu+++6554oknQnFW06tChQrmueeeMx999JHdCT/55JO2/ChMc34czTX5/fffc2YOFpDtwrxvZX+aWWHeh+bCfnNdFu8vd9H6EpkeBAAAAAAgvcgMAgAAAEAeIhgEAAAAgDxEMAgAAAAAeYhgEAAAAADyEMEgAAAAAOQhgkEAAAAAyEMEgwAAAACQhwgGAQAAACAPEQwCAJJi69at5tRTTzWffPJJwn/z4IMPmqZNm0Z93XfffSkdJwAAmbQ1i/aXu5Z4CwBC7+effzZ77rlnpoeBENuyZYvp16+fmT9/fpH+7scffzSXXXaZueSSSyLXlS9fPgUjBID/b+fOnWbFihWmXr169vKyZctM/fr1Mz0s5IktWba/JDMIZIDO5rRp08YceOCB9qtdu3bmhhtuML/99luJt33zzTebwYMH258vv/xy89Zbb8W9/ciRIyO3LwrtPPU4gO+//9507drV/PLLL0X+259++sm0bNnS1KxZM/JVqVKllIwTQH7tW/V18MEH29/deeed5oknnrA/Dxw40IwZM8b+vHr1atO5c+di3V/Pnj3Na6+9lsRHgFz3fRbuLwkGgQwZO3as+fbbb+3XhAkTzK+//mruvvvupN7Hk08+abp06RL3NuvXr0/qfSL/fP755+aoo44yo0aNCtx56SzmAQccYI499ljz+OOPm+3bt9vfbdu2zSxdutQ0atQoA6MGkOv7Vn199dVX9vp//OMf5sorryyw3/vjjz9spgbI1/0lZaJAFtCZnRNPPNG89NJL9nKnTp3MYYcdZsaNG2euuOIKc9FFF9ns3Ztvvmk/GBTgqcSgTJky9qymsoFff/21PWOkkgFX8qmzlmeeeaY566yzbCZPZ0a1c6xRo4a58cYbzW677WaGDh1qS2a0c9RZ01mzZpl77rnHli/oQ+euu+4y+++/v93eq6++asehWvdzzz03o88ZskevXr0Cr9f7RNnpk046yb73li9fbr/vsssu9qBswYIF9v384osvmsmTJ5sKFSqYs88+2+4MS5XiXCWA5NF+UvvGWrVqmbffftt+Dq1bt86ejBVlEd9//327P77//vvt9WXLljXnn3++/RyThQsXmltuucXMnTvXHH744eb333/P8KNC2PTKwv0le1sgS+bsaeekclFHJaP6h+/evbt56qmnzLRp02xZi8o+Z8yYYUaMGGFvd8cdd5jq1aubqVOnmmuuucaedQpy7bXX2hKaL774wu7o+vfvb1q0aGF69+5tTjvtNBsIbty40X4Y9ejRw25PHzL6/aZNm2yQ+K9//csMGTLEfPzxx0WudUf+0XtaOyy913RiQQdP+tm9d7Vz046uTp069qSEdpI6E6qMNgCkgk5kap+nfZsOtp9//nl7vU6U1q5d2+4f165daz744AM7jUL73Xfffdfe5rrrrjPt27e3+2OdwFXJHxD2/SWZQSBDlN3T2Rxl5XQm8ogjjjDXX3995PcnnHCCPSupr9dff93utJTRk7/97W+2g5QyhupEpbOZ5cqVMx06dDBHH310gftSacG8efNsWYK2d9BBB9mdXMWKFaNuN2nSJNOgQYNIaekpp5xinnvuOXsf+vvjjz/eli+44PKjjz5K8bOEMFPJi86k64y7s2PHDluWpTPyen8deuihplq1avZ3Olmh6/WeU0YcAIq7b3U0P1Ald4nQ/lgVODrpqv2yvi6++GI7L1AVMjogHz16tK3KUUD59NNPp/CRIJ/8lMH9JcEgkCHa2cTrXuYCP9FE4z59+kR2cNph6QyRSjv/+usvezbTcd3RvFRKWrVqVRsIOq7000vd1XSm0024F21f169Zsybqfug+isLovaMTD/fee2+B31WuXNl+dzs2p3Hjxnb+rHuPA0Ay963xKCOocj01+PAekGt7QfvRoP0tELb9JcEgkKW8/9gKDLW+jMvKqWxTZ4S0Y9IZSgVryuiJPhjcB4ejIE6BoyYgux2ZSg86duwYdTt1ptKZp+HDh0dlFffYYw9biqCfnVWrVqXokSNX7LPPPjZrXbdu3cj7buLEibbBwwMPPGBLkzUv1tuN74cffrAlMgSCANLN7VNVIqrpF6J9rfad+tJ+VJkazbd3+1sg7PtL5gwCIaByFDVu0U5JXc9UMqomLyoNVenmf//7X3v9N998E1i6qbOXygRqG3/++adtNqNa8ypVqtgPHQWXog5XygxqTqDONOl2KrlR+cLJJ59st63ObNoZau4gEI/eO3of3XbbbXaOqeaz6r2r8mRluVXSrPLjhx56yCxZssSe0deJCM3lAYBU8e733IG3LpcuXdru6/7973/b5jAbNmywUyJ0IL7XXnvZefY6MavA8MMPP7Tz94Gw7y8JBoEQuOqqq8y+++5rTj/9dHPkkUfaYEzNXETLUai8QN1HNY/wmGOOCdyGAsY5c+bY2+nDRjs7ZQIVAGrivDqmqQRBQaJ2fCoV1eRlNahRRrJJkya2fEFdSDWGhg0bpvlZQNhoJ6aMsrLI6mirtTTVdEHvP2nWrJl9v2lOqtb5euSRR+z7Syc/ACBVNCdfjWFuuukmux/UPk1fWtRbB+CiE626naZE6HaiA3FlY9Ts7YUXXrCVNEDY95e77FQYCgAAAADIK2QGAQAAACAPEQwCAAAAQB4iGAQAAACAPEQwCAAAAAB5iGAQAAAAAPIQwSAAAAAA5CGCQQAAAADIQwSDAAAAAJCHCAYBAAAAIA8RDAIAAABAHiIYBAAAAIA8RDAIAAAAAHmIYBAAAAAA8hDBIAAAAADkIYJBAAAAAMhDBIMAAAAAkIcIBgEAAAAgDxEMAgAAAEAeIhgEAAAAgDxEMAgAAAAAeYhgEAAAAADyEMEgAAAAAOQhgkEAAAAAyEMEgwAAAACQhwgGAQAAACAPEQwCAAAAQB4iGAQAAACAPEQwCAAAAAB5iGAQAAAAAPIQwSAAAAAA5CGCQQAAAADIQwSDAAAAAJCHCAYBJNXOnTszPQQAAAAkgGAQ+D/z5s0z/fr1M4cffrjZf//9zRFHHGH69u1r5syZk9T7ee2110zTpk3NsmXLSrytnj172q9s8dFHH5n+/ftHLn/xxRf2seo7AAD4H06eIhvsmukBANngxx9/NOeee65p06aNuf3220316tXNL7/8Yp5//nlzzjnnmOeee87+DvE988wzUZdbtmxpXn75ZbPvvvtmbEwAgPD4+uuvzciRI823335r1q1bZ2rWrGkOO+wwc/HFF5vGjRsnvJ3NmzebF154wbz33ntm8eLFZtdddzX77LOPOfPMM83ZZ59tL8dz8803m9dffz3qOv1NtWrVzKGHHmquv/56U7duXXu9OymrcRflcT7xxBNm+PDhCf8NkAoEg4AxZsSIEfYDXh/K3h3EcccdZ0466STz2GOPmWHDhmV0jGFUqVIlgmgAQEKGDh1qHnzwQVuZc9NNN9lAUIHcSy+9ZIO4f/3rX+bUU08tdDs///yzufzyy83atWvNhRdeaNq2bWu2bt1qpkyZYu677z4zduxYu1+vXLly3O3o/ocMGRK5/Ndff5mFCxeaf//73zZY1XZ22223Yj3WV1991fz000/F+lsgmQgGAWPM6tWrbbnGjh07oq6vUKGCufXWW82WLVsi173xxhvm2WefNQsWLLAB5GmnnWb69OljypYta38/fvx48/TTT5vZs2ebP//809SvX9+eNTz//PNj3v9XX31lHnroIfPdd9+ZcuXKmWOOOcaWW+6xxx5JeXyfffaZefTRR83cuXNtsKsd7d///vfIWU3R4/nvf/9rpk2bZp8L7Ty1M3ZnYlXW+sgjj9idqc7WVqlSxRx55JHmlltusc+DHqP+VlQaqmyqaEesn9u3b28v6zHqsX7//ff2+WnXrp254YYbzH777Wd/r5JS/Y2yjArOdfZUQeUZZ5xhz8SWLl06Kc8JACB7TJo0ye6DrrrqKnPddddFrtc+Qp//2k8oW9ekSZPI/iKI9uO6rTKDyuzVq1cv8rujjz7anHzyyXYf849//MMMGjQo7pi0X/ef0Dz44INNmTJl7D5aUyMSCU6BbMacQeD/dhDLly833bt3t2UlOlvnavmVGdQZSdHvtANQ+aPOFl5xxRW2LOTee++1v584caK5+uqr7e911nHw4MFmr732sjudGTNmBN73l19+actfdHZRQZKCTwVV2ln98ccfJX5sCl4vvfRSG/hpR6vgTWc0VRa7Zs0ae5uVK1fay4sWLTJ333233UEqQL7ooovM+vXrbTCs8eh5ueuuu8xTTz1lL7/zzjv2LK7o+hYtWtgvlYbqOfCbOnWqOe+88+zP//znP+3ztmLFCvu8+8+QKlg96KCDbBlN586dzZNPPmnPpAIAcs/jjz9uyzivvfbaAr9T8KX9qE4GFlZW+fnnn9t9nE5megNB58ADD7T7trfeesssWbKkWGNt1apVJAMZRFlInYDV8YNue8IJJ9jqInfC2ZWg6u918lS9BIBMITMIGGN69Ohhfv31VxvkaIcjynYpg6ag54ADDrAf4vpwV+moC/5EgZKCImW55s+fbwPH2267LWrHo6yYMl6tW7cucN//+c9/TKNGjWx5jMt66XY62zhmzJi4GcXCaMwqZ9Hj0P04yvqdcsop9vFqh6ks3LZt22y5rMpipFmzZjZwUxBbq1YtU6dOHTNw4EAb3EqHDh3s71w2UPMClcGTWKWhGkODBg3sTtE9Vo3t+OOPt1nHhx9+OHLbbt262cBaND9DGVcF2wocAQC5Q9UmCuB69epldtlll8DbVK1a1c4dVDYungkTJtj9S6dOnWLeRvtXBZXa1iWXXFLk8apUVPbee+8Cv9OJ5CuvvNJMnz7dXHPNNXZfqv2/TvYuXbrU3HPPPTb7qRLWH374wZ5YDtoOkC4Eg8D/UVmKMnSffvqpPbOoD++3337bzglQtk5dRpVJU+Diddlll9kv0RwFUXmKdhY666iySFGw5adAUgGV/l47EM1HEAVcKs9UeaeCQXe9U6pUKftVGI1BQa5KZry041GQ6gI5lWIqgHOBoCj4+/jjjyOXX3zxRRtcKnuoORwKfFVa6h9bLL///rt9LrRz9JZ6qtxUZbEqEfLS+Lw0Hm0DAJBbVJkje+65Z9zb6WSiAjhVrCg4DKJsm07mappHLC74ipXZ8/Lu4zZt2mT3Y5q7qCkgqiry++STT+x0ClXiuBJSHT+o+kcnPHWCWWWumgYSVIYKpBvBIOCx++6725JEfYnO2t144422bNKVParTaCw606dySWWxdHZTOy7NL4jVQvq3336zAZbOUAaVvmj+oPhLLhVQaZ5iYbTDlBo1ahT4na7T43O3044tHmUNVbKp2+pvtfxG+fLlzcaNG00idDs9B7HG4t+Of1K+gl/acANA/nJZQ+033ZdXYR1CHXcydfv27XFvp2AxaMqDqndURRTUPEYnWTUOlYh6denSxQaD+n28OY9AuhEMIu9pvpzaTCszqNJEL81/09qDKld0Ow0FfP7yFgVVymRpnpuyZSq71GWd9VP275VXXgm874oVK9qdmzKSQZPQFWzJ6NGjo65X2WYi3JlTzf/zU8ZQZ09FHdX8j0uUIVWQqHKX+++/3wbGZ511VqSxjZ4zl/ksjO5DjzXWWGKd5QUA5DbXzKyw9Xf1e2X8tL/QtA1vp09RkzRlF5WdU4WO9rFBVK4pQXMKvVQto7mMjvbpqlLRieNYNmzYYPet/mZnrvIm0ROoQLrQQAZ5T1kpncVTGaQmffspuFOGTmfy9AHvLZ2UN9980zaS0ZxBlVtqorjmCLruotopif8MpmiOnQJO3Ycmmbsv3Zeaz7jF2r2/01ft2rUTemyai6gdkEpd/TtCBXiaOyjKXqpc1RsQqiRWZa8q39TjUjmnLrtAUDtaXe99XPFKV7UDVzZRaz55z8Zqx6i5gGoWAwDIP9qv6ASqqmq8+xQFVi5w075C5ZeaZ659jdYA1olS75dorqD2MdqW15w5c+x+WsaNG2e/d+zYMe64tB/37nvV7CVeICj6vU4S+7OOq1atst/dSVggW5AZRN7T2Tt10FT2TxlCzdHTfD1l9DRnTx1ElQHTB7hKM1UaolJR7XA0J0+NT/Q32gGo0YzmGaqsRGcPv/nmG9ssRRkx7/IUXlouQcGk5vWpjEQ7EC1NoeBMk8wL88svvxRY7F3UfluT7bV9dRB129dOSmdTNV43cV6ZSXUdVbDXu3dv27lNZ0P1GLR0huZoaJ0nZQc1v087NTWfUZbPu2NUwKgmAMooKsj10xg0P1KPV017tGPW86P5lK5ZDAAg/2j6g/ZBKqVURY5MnjzZVtycfvrp9mSt9qNqziI6KRp0YlQNx3SCU83T9N3NQ9Q2dRJT96H9l07cKrhLNi2Foe7X77//flTFj7qXijvxmci8fyAdCAaB/1taQqWc2kFoXpwyZDojqIBGSydopyEK+pTh0u20fIKCJXU/05coWFKnMH1Jw4YNzYABA+xOQGsJBtFZTm1PAZpaaisQUzCpOXqJTCxXkxpNZvfr2rWrDQZV1qlSGXUrVcClbKTWB1SQ6MpWVKKjzKjmRqrltR67spt67Ar21CFV5TnqbqrbaQd81FFH2YDujjvusMtCKIDW86P1A/V8aEz+clbtpPW4FEDr/nU/2lmrSylzKAAgf2lfqKWbHnjgATv1Qvsd7WvcurOi/VnQHD4vBVnqXK39kG6vZSSUddQ0B3UC16Lz2s8qyEwFZRu1/7z99tvtNBR1E9U8QfUF0GNS52138lQnVFV907x584SnfwDJtstOOjIAAAAgC6i65Nlnn7WVNapk0VQOZdvUZVsZNzVvUUBXWNMzdZ9WZY+WflIHbFUBaeqETu5qasKPP/5oT5AqWAyiE6MK4rRURWF69uxpv2vdYVEGUyc9dd86uayxqieBqnFcRnDevHm26khlsDoRrIoZIBMIBgEAABCKJSgUcKmkNFZzmETo0Ffz1zVF4YwzzkjqGIGwIRgEAAAAgDzE7FUAAAAAyEMEgwAAAACQhwgGAQAAACAPEQwCAAAAQB4iGAQAAACAPEQwCAAAAAB5iGAQAAAAAPLQrpkeQDbasWOH+euvv+zPpUqVMrvsskumhwQAeUPL3+pzWHbddVf7OYzsw74SAMK/vyQYDKCd23fffZfpYQBA3mvVqpUpW7ZspoeBAOwrASD8+0tOtwIAAABAHiIzGMCbYlWUXbp0aRMGs2fPtt+bN29uwijM4w/z2IXxZ06Yx56q8W/fvj2ScaJENHtlal8Z9v+ZXHkMufI4cuExCI8jPx/D9iTsLwkGA3jnPWjnFpZg0I07LOPNpfGHeezC+DMnzGNPx/iZh5a9MrWvDPv/TK48hlx5HLnwGITHkT12ydBjKO7+klOuAAAAAJCHCAYBAAAAIA8RDAIAAABAHiIYBAAAAIA8RDAIAAAAAHmIYBAAAAAA8hDBIAAAAADkIYJBAAAAAMhDBIMAAAAAkIcIBgEAAAAgDxEMAgAAAEAeIhgEACTVH3/8Yfr162c6dOhgv+syAADIvv3lrmm/RwBATrvlllvMQw89ZH/+4osv7PcHH3www6MCACC73JIF+0sygwCApPr888/jXgYAACYr9pcEgwCApDr00EPjXgYAIFdLPnv06GEGDhyYUMlnNuwvKRMFACTVv/71r8gZTu3Y3GUAAPKh5HPmzJmmevXqhZZ8ZsP+kmAQAJBUu+22G3MEAQB55fNilHxmw/6SMlEAAAAAKIFsKPksDjKDAAAAAFACrsRzwoQJpnXr1qGZIkEwCAAAAAAl4Eo+Z82aFbkcBpSJAgAAAEAeIhgEAAAAgASXj+jQoYP9nsjyEdmOMlEAAAAAKMLyEV988YX9nuluoCVFZhAAAAAACskCFmf5iGxHZhAAAAAACskCarkI97O7HHYEgwAAAADgEZQFnDhxYuRnBYJhWT4iHoJBAAAAAPAIygK65SNyCcEgAAAAAHi4rF8uZQGDEAwCAAAAgEcuZgGD0E0UAAAAAPIQwSAAAAAA5CGCQQAA8sT8+fNNq1atzLJlyzI9FABAFiAYBAAgD/z111923axt27ZleigAgCxBMAgAQB4YOnSoOeiggzI9DABAFqGbKAAAOW7OnDnmvffeM6NHjzYjRoxI+vZnz55tdtllF5MOW7dutd9nzZplwioXHkOuPI5ceAzC40jOfT/00ENmxowZpnXr1qZv376mXLlyxdpOuh7Dzp07czMY/OCDD2wr15UrV5p99tnH3H777ebAAw+Mus2OHTvseh9vvfWWKVWqlLnkkkvMFVdckbExAwCQjVQWqvLQAQMG2FbpAICCAeC2bdvsiTOZOXOm/d6/f3+T67IuGNSk9ptvvtk888wzNiofM2aMjcwnTZoUdbuRI0faF06B44YNG2wwuO+++5pOnTplbOwAAGSbRx991LRr1y6lJaLNmzc3pUuXNungzra3bNnShFUuPIZceRy58BiEx1E8/fr1szFFkHnz5hVrHOl8DNu3bzfTp0/PrTmD9evXN5MnT7aBoCL09evXm6pVqxa43dixY82ll15qf9egQQNzwQUXmDfffDMjYwYAIFvppKnKQw8++GD7JV26dDFvv/12pocGABn1+eefx/zdoYceavJB1mUGpWLFimbu3LnmjDPOsGcaNendb8GCBbaE1GnUqJF5/fXXQz0PIt/rxcM8/jCPXRh/5oR57KkafzLmQOB/3n///ajLTZs2tVMsdPIVAPLZoYcear744ovIZU1LK1u2rL1e09HyQVYGg6JAT/W62mH16dPHjB8/3uyxxx6R32/ZssWUL18+clnzIP74448MjRYAAABAmPzr/wI+ZQhdAJhvc6uzNhgsU6aM/X722Wfb+YNffvmlOfHEE2MGf/q5QoUKoZ4Hke/14mEef5jHLow/c8I89lSNPxlzIBCbKm8AAP8/nlDTynyWdXMGp0yZYpvBeGnuYJUqVQpkDhctWhS5vHDhQlsqCgAAAAAIYTDYrFkze6b5nXfeMX/99Zd5/vnn7Vli/9ISp556qhk2bJhZs2aNWbJkib3daaedlrFxAwAAAMhuqiZUF9EOHTrY7/k+zSzrykQ1L/Cxxx4z9957r7nrrrtMixYtbNCnNK4CwN69e9suaD179jSrVq2yP6vZgLKJxx57bKaHDwAAACBLad1VrS0oX/xf85h8LhXNumBQ1Pr6jTfeKHC9soXOrrvuaheCzIfFIAEAAAAkfzmJz+MsL5EPsq5MFAAAAABSURqqXiT5uJ5gqDKDAAAAAFDSIFBloaNGjTK//PJLXq8nGAvBIAAAAICcnh/opUBw6tSpGRlTtqFMFAAAAEDOiTUfMN9LQ70IBgEAAADkHH/QV6dOHdO3b9+8Lw31okwUAAAAQM5xQZ8yhG5+oJarw/8QDAIAAADIOQr88nkNwURQJgoAAAAAeYhgEAAAAADSaNOmTWby5Mlm2LBhBdY+TCeCQQAAAABIo2XLlpmPPvrIrFixwvzwww8mU5gzCAAAACBnFpn/7LPPzF9//WV23XVXc/jhh2e8cczq1avNt99+aypXrmw6dOhgr9tvv/1MkyZNTLNmzUyLFi0yNjaCQQAAAAA5ucj8l19+ab+nu5HMtm3bzKxZs2wQuHTpUntdlSpVTLt27UypUqVM6dKlzXnnnWcyjWAQAAAAQCgzgAq6fvnlF3v9xo0bi7T4fCqsXbvWLFy40Lz11luRuYC77LKLzQQeeOCBJtsQDAIAAAAITSB42GGH2YxbcRefT6WFCxfaL9ljjz1MmzZt7JdKRLMRwSAAAACAnAoEK1SoYFq2bBmZM5hsO3bsMD/99JMdi+5jzz33tNc3atTIbN++3RxzzDGmQYMGNiuYzQgGAQAAAORURvCKK65IyTzBdevW2TFMnz49UpZavnz5SDCobKDmBTZs2NCEAcEgAAAAgKwMAG+88Ubz6quvmjVr1tgOoX41atSwzVikTp06pmzZsknPBu7YscN8//33NghctGhR5HoFgQcccIBp27atCSuCQQAAAABZFwQ+/fTT5vfff495OzVkmTJlSlqWjfjoo4/Mb7/9Zn9u3Lixve+mTZva5SvCLNyjBwAAAJBT1Cl0yJAhcW+TqkBwy5YtZubMmWbevHmmR48eNuuopSBUoqogVc1gdt99d5MrCAYBAAAAZIWtW7eaUaNGpTUQ3Llzp1mwYIEtA50zZ45tACPz58+32T9p3769yUUEgwAAAACyghaNd+sGeqkcs3r16qZbt25m0KBBSQkEN23aZL766ivbDGbDhg2R6zX3UAHn3nvvbXIdwSAAAACArDBjxoyoy2XKlDG9e/dOWgDoDwYnTZpkfy5Xrpxp1aqVbQZTt27dpN5PNiMYBAAAAJAx69evN0cffbTt2KnOnV5XX311UpaIWLlypfnmm2/sun8nnXRSJAN48MEHm7322ss0b97cBp75hmAQAAAAQMZ06tSpQEZQVKpZkiUi1PDFLQmxfPnySLmpAk+XZTz11FNNPiMYBAAAAJCxJSQ0Zy+I1gwsTmnosmXLzJdffml++OGHyNqE6giqZjAKMLVd/H8EgwAAAADSToFgvCUkDj300GJtd+HChXZ5CKlZs6YNALU4fMWKFYs91lxFMAgAAAAg7UaPHh14fa1atcw555xTaImoloD48ccfbRlo69atTYsWLez1+nndunW2Gcyee+5p5wkiGMEgAMQoXdGit59//rk9M6kdUrK7mAHpNH78eNuEYcWKFaZhw4bmtttuMwcddFCmhwUgj8tDV61aVeB3PXv2NM8991zcv1+9erVtBqPs3+bNm+11Kgd1wWCVKlVMly5dUjT63EIwCAABFAhqrSP54osv7PdkdDMDMmHp0qWmf//+5vHHH7ed88aOHWuuuuoq89FHH5lKlSplengA8kys8lDN6evbt2/MheE1t1BZQH2mOSr9VCZQpaAoOoJBAAigjGC8y0CYKBuokqt27drZyzpjft9995lFixaZ/fffv8Tbnz17dtrKsLZu3Wq/z5o1y4RVLjyGXHkcufAYwvI4NMYHHnjAvPHGG5HxetWoUcOMGDEi7uP47LPPzJo1a+znjZaFaNSokf2u5jBaOkJf+fRa7Ny5s8TbIBgEgAAqDXUZQXcZCCsFgS4QFJ1d37Jliy0XBYB0BEjnn3++mTNnTszbnHzyyXbhd1dGunjxYrNkyRLTsWPHyPVNmjQxGzduNA0aNDDly5dP2/hzGcEgAARwk9a9cwaBXKADrD59+pjrrrsuaSWiWqy5dOnSJh3c2faWLVuasMqFx5ArjyMXHkO2Pw4tKL/ffvvZeX6xqMzziSeesJk/VSz88ssvkcXnt23bZhvBZOvjy+RroQY6sZblSBTBIAAEULMY5ggi16jZQu/evU2PHj3MZZddlunhAMhhyu7169fPDB06NG45Y/v27c2AAQNsMKisn6MuoJoHmIxSdsRGMAgAQB749NNPbWOGm2++2XTr1i3TwwGQB01iFODFUqFCBXPRRReZe+65xzz22GM2E6jF4FUCetxxx9nlJZB6BIMAAOQ4lV1de+215v777zcnnnhipocDIMepNDQoEKxXr15kAfh//vOfkeuPPvpoU716dVv2qGYwBILpQzAIAECOe/nll23DGGUF9eUMHz7cLjUBAMkMBBX0ad0/UaMXBX+a91e7du3I7dauXWv22GMP+/ORRx6Z9d1QcxXBIAAAOU5rDOoLAFKtU6dO9uST5vwddthhdu3AXXf9/yGHGk1pYXhlB6tVq5bpoYJgEAAAAECyGsZoUXhR1s911Pz1119t4yoFgSwJkV0IBgEAAAAUi8pBv/76azNs2LCodQRnz55tl2fSdVr+oGrVqhkdJ0IUDI4fP962dF+xYoVdEPe2224zBx10UNRt5s2bZ04//XTb/t1hYjwAAACQeloLUOsCfvXVV/Z4fO+997ZZvylTpkSCxHHjxtm5gQSC2SvrgsGlS5faeQ2PP/64ndQ+duxYc9VVV5mPPvooanFcnWVQ5yHdDgAAAEDqTZs2zbz55pt2GQhRILhhwwZbHupfAL1NmzYEglku64JBZQPPOecc065dO3u5S5cu5r777rNtsb2LTioYbNasWQZHCgAAAOQ2LRi/yy672DmBffr0MevWrTOtWrWyy0DoePybb74xCxYsKLCwvILECRMmZGzcCGkwqCDQBYKiMwzqSKRyUa+5c+eabdu2mWOOOca+Qc8991zTu3fvpI9H9c7afhhs3bo11G15wzz+MI9dGH/mhHnsqRq//4ACAJB+v/32my0B/fjjj+18wFWrVtnr69evb37++Wczc+ZM8/vvvwf+rZaSmDRpElnBEMi6YNBr8eLF9gzEddddF1UiKnpzqUNR9+7dzfLly20gWLNmTXPWWWdlbLwAAABAWCnbp4SLGsL89NNPNiGiZSFat25tPvzwQ3ubZcuW2a8gquL79NNPCQJDJGuDQZ1tUICnNrSXXXZZgd//5z//ify87777mvPPP9+mopMdDDZv3tyuiRIG7sy8a+MbNmEef5jHLow/c8I89lSNXwcj/nknAIDUUQmoMnnebJ8CQU3T0lzAH374Ie7fEwSGV1YGg3oz9e3b19x8882mW7dugW/Yhx9+2DaWqVy5sr1OJaPlypXLwGgBAACAcM4FlDJlykQCQVXjqbO/GsWoE2g8BIHhl3XBoM5AXHvttXGXidCE1MmTJ9s38Q033GAWLlxoXnjhBXPvvfemfbwAAABAGOjYWZ37le3TNKsrr7zSBoSqgjv++OPNxo0b7Xc3HzwWgsDckXXB4Msvv2wbxigrqC9n+PDhplevXva7lpwYPHiwGTBggGnfvr3NDurNfNRRR2V07AAAAEC22bRpk5kxY4YNAtesWRPVn6NOnTq2R8eTTz4ZdxsEgLkp64JBrTGoryB6AzvqLjpixIg0jgwAAAAIDy3ZprmA8+bNi3RqVkmo5nkfeOCBNqGy5557FloOqqQLa3vnphIHg6otVnchdR1Sy1mlmnWGoUOHDqZjx46mfPnyyRkpAAAAgLh27NhhSpUqZX/+66+/bHdQtySEAkAFggoS1YDxzz//LHR7OpZ/8MEHUz5uhCwYVC3xY489ZkaPHm3XEtEbS1/qArd69Wrz6quv2sXiu3btav5fe2cCdtW4/v9HxEkqlUpRJNKggcYTUhlTKKckHWSIEhk65OQgRxwJ5xCaJAkhU5mOkCE6mTopSvOkoqKBpJL+1+c5v2f/117v2sP77r3fd6+9v5/r2tee1l7rWfvd71rr+9zf+76vuOIKm+cnhBBCCCGESC+IPto9UPSlatWqplOnThEB2KFDB1OvXj3bgm3z5s1WENJHOxkqVapkq0brOj53KbIYpN3DOeecY6OC+++/f0x/8pQpU8yll15qnnnmmVTGKYQQQgghhPg/sH1SBGb27Nm2EiiCEHDqnXHGGbYoDI69E088MVKk8YgjjojYReOB+EMwkpYlcpsii8Fx48YlbOVAaVr6/xEdFEIIIYQQQqQOvVj/85//WOHnKFu2rGnZsqVp2rRpVI9sROBRRx0VEYvx4HNEGEn5EvlBkcWgVwgyK1GjRg1rHcU2WqFCBdO5c+fAZYUQQgghhBCFywMElwtIRVCE4D777GPq169vKlasaG2gVPx0YAlt06ZN0pZQ0r1o3aZqoflFygVkqOg5atQo88knn5ghQ4aYefPm2ZA0/uJYVUGFEEIIIYQQ8dm0aZONAnIjD7Bu3br29eOOO86UL1/eNGrUyFo6ue6G7777zopDhGCykM71yCOPKC8wT0lZDD777LM2H5Cqoq+99pp55ZVXTOXKlU3Hjh0lBoUQQgghhCgE2DmJ5tFSbfny5ZHXv/rqq4gYJBLYokUL+xjhd+aZZ5pVq1YVajsUh1m6dKkigXlOymKQH2CdOnXMO++8Y/3FPN65c2dSvmQhhBBCCCHE/0TgtGnTrMvu119/jbxO0RcqgFIR1JsHSGsIqvgXhYsvvtg6+xQNFCmLwQYNGphhw4bZUrZULqJp5fDhw+2PVgghhBBCCBFbAJL354q3EN1DCFJ/g0Iw3FzkDgsognDLli1F3p4soSLtYvDee+81I0eONCeccILp37+/WbRokW1geffdd6e6aiGEEEIIIXIKWjtg/yQPEJvmgAEDbLFFam6ccsop9r527dpm69attjro4sWLU94mttL58+erSqhIvxikctFtt90WeU4Vo/vuuy/V1QohhBBCCJEzENFzxWC8BV4Qe64KKLZPrx00FVgP7SeUEygyIgY7dOhgZy7i8e677xZ19UIIIYQQQoSeDRs22FzAJUuWRF4jEogAJFLXpEmTSOuIVGG933zzjZrFi8yLwXvuuaeoHxVCCCGEECJnIWWqdOnS9vG+++4bEYK1atUyH3/8sXnyySftMumKAD7++OO21QS9AoUoFjGIh9lBWwlC33igXTIsHmghhBBCCCHygR07dtj2D7SEKFOmjOnVq5d9/fPPP7et1ygOQ6HFdID1k/YTLgfQ9RkUothzBidOnGiLyLhWEghC7KOUu23fvn2qqxdCCCGEECIr4boXkYcApECLi/ZxXYwg9LaISAUqjSL4jj766LSsT4i0icExY8aYESNGmFKlSpl///vf5q9//attLcE/gBBCCJHvrF271tSoUcNGDV544QVbMr5z584lPSwhRIrQD/D999+PivaRH4gw/PLLL1MWgjSY/+STT1QARmSUUqmuAItou3btbL9B/inKlStnBg0aZN588830jFCIHIATwvXXX29at25t79M1UyiEyG7Gjx9vunbtah8PGTLETJo0yYwePdr25xVChAsqfTon3GeffWZ69+5theDOnTvN7Nmzzbhx42wPv5kzZ5pt27YVqfgLLSeINnJbuHChhKDI/sjgoYceavMD69SpY3744Qfz008/2VB2Uf4JhMhViJj/61//so+Z5YN//vOfJTwqIUSmefbZZ80zzzxjJ05fe+01mzdUuXJl07FjRztxKoTIfjZu3Ggr5CMAP/zwQ3vvxBuiDfsmgrAofPrpp6ZFixZpHrEQxSgGmRXp2bOnmTp1qjnnnHNssizVk1q1apXqqoXIGejzE++5ECI3oZcYk6XvvPOOLfTAYy4aXXRBCJGdrF692nTv3t063w477DD7Gu43Knc6MYj1G0toYZgxY4Y54YQTMjJmIUpEDGJ/ad68uTnooIPMTTfdZF5//XWzdevWiC1GCGHMH//4x0hE0D0XhQd7LVFWxDTf4T/+8Q/zhz/8oaSHJURMuJDEEsrs/xlnnGEtZeTVH3vsscU+FnKYbr/9drNixQo7LlpEUeZeCPG/iRv6/VEMplOnTqZx48Y2gg/0AKQxPMJv0aJFSa+T/GB6/rmKn0LkpBgkMR5b6Pr16+3z4447zt5v2rRJRWSE+D8QLeAVMaLwyG4rwgbVtkeOHGkjAf3797cXklQbvPvuu4t1HEQw2P7NN99sTjvtNFv87brrrjMvvfRSsY5DiGyB2hZnnnlmxO7J/4iDCCCvkf7kisGQBpUMEoAi78Rghw4dbCsJ12OQx9yqV69u/dVCCGOjVxItqSO7rQgbVapUMbfddlvk+THHHGPuu+++Yh/HrFmzbCEKV8W0X79+ZsKECbYRNq2gUoV+Z5z7iwN30R7mvmq5sA9h2481a9aY008/PfKc3+tRRx1lo/RU7aTwC4EM+OCDD+z5ZeXKlUmtu2bNmua5556zTd8BEcmtOAnT3yLX92NHMe6D018lKgaZ/fDCPxLVlGTdEkKkG9ltRVhwE6XxKM4JUyoUHnHEEZHnOHq4gF22bFlaxKAQYRCAULFiRSsAmzZtGhFvgDDEzg3r1q2Lu15qY1AQ6pBDDsnQyIUoPlIWg374J6N0PpaYq6++Ot2rF0LkMbLbirBAPl42QTVT/yQtqRzbt29Py/rr169vBWZx4GbbGzZsaMJKLuxDtu3H3Llzbc5fLIiMU+iwdu3aUf8XfA4r6Pfffx93/VhFySPMVrLpb5Hv+/F1Me4D7U7mzJmTXWKQJFuaz++///7pXrUQIs+R3VaEhZYtW0ZdcG7ZsiVi56GSKC2ZihOEn7+/KUKwbNmyxToOIdINEfZTTjkl8D3vhActz0hh4v+Q/z8EIO42LqaDUMsHkS+kLAYpseu3wnBy+dvf/pbqqoUQQohQM3HiRFtExrWS4EKUcybWzPbt2xfbOLCIvvzyy5HnXABTNdEbJREiDNDWwTvZEiQAGzVqZAsaEq0mFxAo3ETBJCKATM54GTt2rLn88sszPnYhclIM+nMeSpUqZRvq7rvvvqmuWgghhAg1VO0cMWKEPTfimqEiLq0lirvaNr1/KWhB03sqKDIu2krQ91CIbOejjz4yJ554Ysz3mWBhYoNcQCzL++zzv8tbJmEqVapkW7qAawvB/+P8+fPN0UcfHepCJUKUqBh0DTeD+Pbbb+29wutCZBfq0ydE8YJFtF27dmbjxo1WBFKyftCgQbZ/Gf+LxQX/56NHj7Z9Bu+44w57wezatAgRtuifF37LFIkhJ9Dx3XffmdmzZ5t58+ZFbKJeASiESIMY5GTmLC/80/FPSE+VDRs22NlH/tmYgRRCZA/q0ydE8XLooYfa/CQicJwb6VWGdY38peKGthYvvvhisW9XiHQKQP5/qObpcmAp4881KM8Rf4hAJmHU60+IDIvB6dOn23tmGSmti9eaWRegdxHVmYQQ2RWZU58+IYqX3r17m549e5qpU6faSoa9evWyF7LYNoXIdzZv3mxz+2h9koiqVavaZanoSfXEadOm2df57OTJk83ChQvNe++9Z6vZCyGKMWfw1VdftbM5TggCJzvZT4TIvsic+vQJUbx07drVNG/e3Bx00EHmpptuMq+//rrZunWrfV2IfAMnGc4x/geSYb/99rMRbUSgt6efK3zkegN6LaJCiGIWgzStpTpT9+7dI689/fTTUc1thRDZEZlTnz4hipe1a9daW9v69evtcy5qYdOmTcVeREaIMAhAB3mATKQQSXcVcIn+devWzdx6661m1KhRGRqxEPlFymKQRHSay/NPSQh/3bp1Nko4cuTI9IxQiByipCNz6tMnRPHSoUMHW+nQ9RjkMTf6nfmrcQuRS/ZP8v8WL16c9GcOOOAAm0vr/V9BCLKus846y1qr1RdTiCwUg02bNrUnNBJ2SY7HCsPMp1pLCFEQReaEyC8oYuGFiOC4ceNUxVfkHIg2InkUTEoWggd169a114303nzyySfNihUrzPnnn28nLmkNgT3U389aCJEFYvC1114znTt3LlAxFDvAG2+8YR936dIl9REKkUMoMidEflOxYkVz/fXX2yIXuGqECDsUcCGvrzAQOKAnYJMmTWxE0HHPPfeYHj16ZGCUQoi0i8EpU6ZYMRirTDWzOBKDQgghxP/n999/t83n999//5IeihBFhugdxVuI3BUGfvdE/WrVqhV5DesnohBxiEgUQoREDI4dO9beT5w4MZ3jEUIIIXKGevXqFbC4cfH7t7/9rcTGJERRoW0Ywq0wVKhQwWzZsiVSBIYoIv02EZMIQO4psiSECGnOINWhnnnmGdO3b18zf/58W+GJEr8UlqHZrhBCCJGv+IvEkCNVuXJl5dWL0OUDtm7d2lbzTAZvtA8b6EUXXWQaNGhg3/v222+tQCxXrlyGRy2EKBYxeMstt5hdu3bZ6k88pnoUNoDBgwfbRGAhhBAi36D/biy4GIYWLVoU44iEyKwVlImOOnXq2GIwFIVx0T4qgpYvXz6ynAIFQuSYGPzvf/9rpk+fbk9ulBDGNooYpKJUUXnnnXdskQ3aVBx++OFWZDZr1ixqmV9//dW+/sEHH9gZqOuuu05NfIUQQmQFgwYNsvdMlFJYDcfMwQcfbDZs2GArb9N3zV+ATYiw5gMiAs8555wo0UcVUCKD2EJpHi+EyFExCDt37rQCjjYT2AEQhkXtBbN69Wp7EqVPIYKSqqVXXXWVtdp4K0498MADZvv27WbGjBlmyZIl5vLLLzf169e3+RlCCCFEScIkKdx+++32ophzFJETmDBhgs29EiJbYIK9X79+5oknnkhqeaJ9VMcm9++II44wb7/9tnWDEQxo3LixFYH0nhZC5IEY7Natm43IMdM5fPhws2DBAnPllVeaXr16FWl9RAPPO+88azeFs88+29x11112pspbuhiROHr0aFOmTBnTqFEjW9l06tSpEoNCCCGyhldffdVaRp0QBM6P//rXv0p0XEIAUWsm0skJTIYaNWpYocd1F9FDGsFD7dq1zYUXXmgOO+wwFYMRIt/EIPbMk08+2UYCmR3CAkOfmDZt2hRpfYhAJwRhzpw5NgKIXdRBVSrEJ9tzcCD66KOPTLpB3Ial2emOHTvs/ddff22ydXxcAH355Zc2sZzfjtc6ku3jj0eYxw4af8kR5rFnavxYK3OFmjVrmpdeesl079498trTTz8ddf4SorijgNdcc4157LHHklqeSXcX7cPq7Pjll19sqxQ30aHftBB5bBOtUqWKjdRNmjTJXHvttdY2kA5WrlxpD1is02sRRRy6A5QDuwIHOJG9IARdKxJnkXJ5NWGGi+H777/f7hMnS7/IFULkL1TWprn8qFGjrG0O9wsXz6RCCFHcEAEkv+/HH39Mankm+//4xz+affb53+UiUT8iiZzrmISnirwQIs/FING4gQMHmhNOOMG899575rLLLjN33nmnzRvkcVHhwhq76QUXXFBgPQg/QPy5xr3ex+mEg15YLA9uZr5hw4YmG1m0aFGB596xZvv4Y3H99dfb9irw1Vdf2bLxFEAKE2H97nNh/GEee6bGTy8yXCG5ALn05LzPnj3bOlpoqk21RbWWENkYDaTQ0bZt22yVeK59BgwYYAsFEhF09lDvRLwQIvykLAbvvfdeM2LECGvtpEw2BwyS4xFwRRWDFIUhunLzzTdHWWu8B6tKlSrZPELXt2b58uV2lkpkL8wufvLJJ1HPc4H//Oc/cZ8LIfIP3DLksvsrhpKj9cYbb9jHXbp0KaHRiXwSgOPGjYtrvSbqR70FxB5WTwoCvvDCC/Z6DicW13bVq1cv1rELIUIkBjmxuTYSLrcOUYaXvCgg8JiJIu/w9NNPj7lcp06dzEMPPWTuu+8++xlOvMlWwRIlwz/+8Y+IWEIIuudhJ1dFrhCi6EyZMsWKwRdffDHwfc6XEoOiJCuDVqtWzUapyQf0RvtuvfXWSG4grysSKERuk7IYxDLw+OOP27LZDmaUvJU/C8Nzzz1nZ6KICnJzjB071vTp08feIz5vuOEGa0fFz86B6q9//au1dIrsBXtv2OyTyYCoxf5FYZwOHTrkjMgVQhQdzlXg8qSFKA6YoGdCPl4NBeyfl156qW154qA/IJZmbhUrViym0QohckIMDhkyxPTt29c89dRT1mfOTChec5LliwIFRWIVFcG37iA/UBfdIltErvvNhjXvSwiRGbZu3WpzijlPUmyDqAupDhSWOfTQQ0t6eCKHCsNQuyFWVV+igN9//30kJxexSCXQunXr2vYQ2EO97U+EEPnDPukom01/v3nz5tkqaVQWpW0ADUmFEEKIfOaWW26xE6TkbPGY/HomMwcPHmybdAuRak4g7iyEnZ9y5crZSB+5gExA4Myh2jtFzvj9cf1GWzAhRH6zT6oznqtWrTJHHnlkxF4AlCzGwpmLlkAhhBAiWXC0TJ8+3VbYXrx4sbWNIgZdrr0QRRWCrVu3tukJfgso0T4EINdmLtrHhMQHH3xgXxdCiLSIwbffftv85S9/sT3WKJXNDCc2g1dffdUMHTo0qjGpEEIIka/s3LnTVmhkwpSeuQhDRWREUe2gbdu2tW4sP9iOe/bsGfXb4hqta9eu1rGldiZCiLSKQRqIU/WzR48eZsyYMebhhx82tWrVstWraLB7ySWXFHXVQogYM8EUSvJWY3U9N4UQ2Um3bt3sxThFpoYPH24WLFhge+j26tWrpIcmQi4CEXcUftm4caN9vmHDBvsaVlB63j744IPm6KOPLsFRCyFyWgyuXbvWCj4sCIg/8iDwn7/00ks2QiiESC8IQSZhwLWykBVbiOyGnrlUvSZaw7mRC3ZaJ7Vp06akhyZCKgK51qIlBAXLEIJMyLsoIMX8zjzzTNvfUpOFQoiMikF6JDkvOjNRJMePHDlS1dGEyBBqbi9EOKGwGr1wJ02aZK699lobuREikRPk+uuvN6NHj7bXV9iLsXqS80dqjoPrL0Qf769evVoCUAhR/NVEHfvtt5+EoBAZRM3thQgfH330kRk4cKAt+//ee++Zyy67zBZYI2+Qx0IEceONN0ZadPHboYetm4AnB5UWEhQn4ndEX2eKw0gICiGKVQzSp+bzzz+3M1bw22+/RT2HFi1aFHX1QhQLYcrDc301vWMVQmQ39957rxkxYoRNpeCcSHG1CRMmWCEoMSj85yNE4FtvvWVTcRxYQRGCCL/Zs2dbIUguIJVEaRkhhBAlIgbpU3PTTTdFnnNA8j7HRvruu++mNDghMi3cwpSHx75m69iEEMF89913kTYSnBehdu3a5pdffinhkYlss4RyDsIKSnGh999/395g0aJF5pFHHrH5po0bN7a/KYlAIUSJi0H6JglRnGRCuCkPTwiRSRo1amSbgl9++eWR11544QVr7RP5C8VeiBp/+umnthAMv4dzzjnHvkcDeRrGO3iO62rTpk0SgUKI7M0ZFCLTpFu4MRtL7oUX5eEJIdLJkCFDTN++fW2Vx23btpnOnTvbBuAuH6y4oOH93//+d9vaokKFCra9xXnnnVesYxD/HyY2qTB78cUXR1778ccfbR4g9s+tW7dGFSBatWpV1qYwCCHCjcSgyNsCKkQaOfE6qNKmPDwhRDqhDcDUqVNta4B169bZC3usgKVLly7WcdACCvFHL2CEISKkTp06plmzZsU6jnyF6B6C7rDDDrNRwTfffNOew6pXr24FOrmAK1eujKq7QJ4gkWXsohKCQohMITEoQkO6C6j4I4uuRLcQQqQDojsIgCOPPNI0bdrU3lwEiIqixZUDjL0QEeJ6A9erV88WtCECJTGY+e9+zpw59sbvoUePHrZvIEVhEHlvv/22dal42X///a1Yf+CBB3ROEkJkHIlBERrSXUBFrRqEEJmCi/y//OUvNgpEX7gnn3zSNp1/9dVXzdChQ21V0XRDVe+gwjQcO11jcqDP4RdffGGFSboguuUK5GQavlOgqmY2QrX1NWvWmBUrVpj169dHXkf0de/e3RaCAW+/Sf5G5AxSiI9WXbB06VKT7WT73yJf9gG0H/m5D3s8boJiF4MXXnhhwgM/Jz8hshW1ahBCZDInbMCAAVZwIcQefvhhU6tWLWvTxLJJlC7dzJw50/Tp06fA6127djX33HOPfbx9+3bTv39/W5Xy+OOPT/sY8h2if/STJC/UsXz5ciu+v/nmGyvYg66nBg0aVMwjFUKIFMXgueeea++xmXAC4mBWo0YNO+M1ceJEnWRE1qNWDUKITEGfOGfLRPxhyyR/8KWXXrIRwkyA/XDhwoUx3//hhx9s4ZiKFStasZrOSF79+vXN3nvvbYoDN9tOFc6SBnGN5ZO/rcsNnDVrlrV6fv/997ZQ0ObNm2N+nhxSJgvCagfNpr9FPu8DaD/ycx92795tbeglIgaZaYSRI0faBrokQTvat29vevbsaQYPHpzS4IQQQogwgtBCCLp8ZKw8nC8PPfTQEhkPtsWLLrrIuiCocLrPPsoSKSr8LYn2UYAMeyxCjj6BiGH+5r1797ZWTwr0xBOCiHJ6CIZVCAohcoN90pEc7fztXiihLYQQQghjz5MlJQSxLBIRZKL2b3/7W4mMIRfYsmVLpBiMV+TRIoL8P0TdjTfeaCZPnmyjsEGWUER45cqVzcknn2wGDhyovoFCiPCLQRKeL730Ulv5qlq1atYaM378eHP++eenZ4RCZBAS+mkx4c0b1CytECId1p3PP/88ktyPMPA+hxYtWhTLWD766CPbTuLbb781L774YuR1BCI9EEViPvvsM/PGG29EiXsaxWMDo+onNlnyBYMK+ACtRPi+hw8fbs8xYS6OIYTILVIWg1xIkyP4wgsvWN88/nfyB9NZpUyITMHvl9wZcJVFE+URBglIIYTwQvSHypAOIkDe59hI33333WIZCxHBeLmEoiBUAcX2yd8RXE4gLTroSdugQQMr+Nu0aRPVrzYWFO1RjroQIifFIAdL/PFnn322zUngAMkMaHGVmBYinb0G/c+TEZDPPvusOfXUU811112X8LOKRAqRH0yfPr2khyCKUA7+q6++suKO65kmTZqYLl262PdoBXLttddaUc9xHGHPsf+7775LuF7EoyYNhRA5KwZpnktJZCqKkiRPpbRevXqZ0aNHm0aNGqVnlEJkiKL0GvQLRi4GiI4n006lKJFIIYQQmQHb7qpVq6wAnD9/fqQlBK9jC3XRW47XO3futMd78gNj2UGhTJkydkK8XLlytq+gs4YKIUROikGqklEx66GHHrJlrWvXrm198TTVfe6559IzSiGyqNegX0A6aLOSiUikEEKIzPDMM8+YJUuWRJ4fdNBBZvXq1bYvZGEL4dFOghoKEn9CiLwSg1wUz5gxw0YFnTWUnMERI0akY3xCpEw8a2aiXoPusx9//LG1P1MJrlWrVrZvGHmyXosQlqJMRCKFEEKkDjl+CD8msF1rjVq1apmVK1faQjDHHXecrfjKcbkwQhALKUXzZPsXQuSlGKxUqZJZtmyZqVevXuQ1+u8wuyZEJgkSascff3yBE3Iq1kzKhDND7K8qR34gv3MnMuvWrZtUzmBRIpFCCCGKDsXtsIHi3kDkdevWLdIMumXLlvbmbZEVy/3hRyJQCJELpCwGr7rqKnPZZZdZXzxe+3HjxplJkybZyllCZBKvyPMKNb/YS8WaSfQvCNbhjSomWyY8USRSCJEb4JBJVEgtUY6xKDrk95EDiAgkJ9Br5aRQjMMrAr0TjBR9cU4Qf84gf1cEpeygQohcIGUxeNZZZ5kaNWqYKVOm2J5JS5cuNXfeeafsbyLjxBJ17nV3YscC5CUdv039voUQ8Tj33HPtPdEoCqwhDjlXbtiwwRacwsUgMgPRP+oYIN4A8XbkkUdagYeLgyroyUww4vbQ5J0QItdJWQx+8MEHtnBMs2bNol4fM2aMueKKK1JdvRAxiWXlcULNf2L3WnqShdlfr020WrVqpmfPnrJ3CiHi0rVrV3s/cuRIM2HCBFO9evWovn8cRwYPHlyCI8wt8UcrCIQelC1b1h6rf/75ZysAyecuX758wvWowJcQIh9JWQxSOZSLb2bP6L/jGDVqlMSgyChOkAXlDAadyGkWzO+UiOH1119fIG8vqMgMNiDWq76AQoiisGnTpigroqOwlSpFNLR+WLx4sbWBLly40Eb/brjhBmsDhR49etjHhel5rAJfQoh8JGUxSD8disdgiaGCqEvK5kAtRCbx598h8ij4QnsTYGY4UcSQEz/RbUSkiwB6i8wox08IkQrnnHOObTdw8cUX22PS2rVrzfjx461LQRRNXNMYHvv/9u3bI69jwaX/nxODRAcLW22aVBcqRZN7rgJfQoh8IWUxyKwbTecbNGhgLrnkEnPTTTdZa12pUqXSM0IhkoQTutfSSaI/FiHannhP7P6IITPL69ati3pN9iAhRLqOS+QIUoyKqpZVqlSx+YNErkThQAS++OKLUZPRjRs3tsd5/+RfsvgnB8kTnDVrVtrGLIQQOS8GvYVk6N3DrNq8efPsBbgQxUmQgON36D+xJ1M2nMIDzBgTGYzXp1AIIeJBsZLevXubs88+2+a1MXGKrb0w9sV8hUk6egPS+w9wffB90rrq8MMPN6ecckqkX2Bh8B7T/QXGNBEohMg3UhaDXjsoJzlmP5lZ27x5c6qrFqJQBIm8oJwPxBzWUCKCDqLZ5B6617jnYgGLaCp9CoUQ+c2PP/5o3TNUFGVy6qWXXjK9evUyo0ePNo0aNSrp4WUdWD+ZUOYYjLuDXG/EtLN+khdIj1dIVgj6J/QQ4/7+sQ7lCQoh8o2UxaD3gto1oScfwv+6EJkGkcdJ3vUGROAF5XwQ1ePCzB/ta9euXeAMsSrMCSGKypAhQ6xrhlYHVN4mukXhtaFDh5rnnnuupIeXFTCpjMDjumHBggU2GghEAcuVK2efu3YQLiewMPgn9Kgs7YXniE7lCQoh8pF9UjnBceMgG4vmzZsXdfVCFAlmir0n9Vh2zqDCMLEqyflfx1ZENdKSsovKtipEeODYMWPGDBsVdNZQcgYpuCb+x8svv2yjgQ7y/8gDJHJaFPHnJ9EEHsV85PYQQuQrRRaDrmfSIYccks7xCFFkYtk5kxVPt99+u7WPLlq0yPar4jm4meJJkyaZ77//3lqX2A5RyJK4oJNtVYjwgFtm2bJltuq2gygYeW/if9AQnuMu4g8RyPVFOnMq/RN6uEb8LYOEECJfKbIYxOYCFIwRIhuIZedMVjzdcccdUTmDPPe2l3j22WejlseOWhJiULZVIcLDVVddZS677DLTvXt3s2vXLjNu3Dg7sdS/f/+SHlrWQEuq+vXrm9KlS2dk/d5K0nJTCCFEmsQgNpdEM3dPPvlkUVcvRCDxonyxbJ5+sYSoC/p8WESWGiMLER6otE0PvClTptg+dkuXLjV33nmn/m89kA/ocgIzgfrFCiFEBsQgTebhyy+/tMU4EIec8DZs2GB7KtHEW4h044/yIezI90DUxbJ5+sUTNk9u/ihhIpGFtchbgY7nXnbs2GFzCf1CM905fkGz3EKI7IRjEoVjmjVrFvX6mDFjzBVXXFFi48pVlFMthBDFJAa7du1q70eOHGkmTJgQySGE9u3bm549e5rBgwebVKAq6cKFC80999xT4D0u+M8555yogzzLnX766SltU2T3Sd5v1XT5e44gm6dXPFH8hc8ERf8Siazhw4fHzTNhHEyEgFdopjvHT7PcQoQHUio4XvA/e+CBB0ZeHzVqlMRgBlBOtRBCFI5SJkU2bdpk9ttvvwKvb9u2rcjrpIw0s6b33ntvzGW++eYb2wqAi353kxDM/ZO8V8h5QaDFsnk68UTzeaKIXlz0Lx2zyUTJg7YfFvupECL9lClTxhaPwU3z9ddfB/boFelDx1shhCjmPoNE5y699FJz8cUX23LQa9eutRE9/0V3YaBB708//WQT7nfu3BlTDHqrs2UKeh6ls6pZJsGmCN4LjjCRaPzTp0+P+VlsoeC1efKaf13YmX/44Qcr3Jo0aWKfs8ywYcOionosw+/Qy913322eeeaZyDLr16+3BZT4/RNx9ItHt33uE42rpMn13042E+axZ2r8uSSUOH9wLGnQoIG55JJLzE033WQt5qVKpTwXKwJQTrUQQhSzGCSawkU0lRU3btxoqlSpYi+we/ToUeR1crKsWrWqrdS4Zs2awGWwjyIUsaRysmV7rsKpyE0Qb3Pnzo08ZzKA3l28ft1110Ved0LPvcbFKrYh7+v+aLY/qud/DtOmTSvwfPbs2XZiArZv324qVqxoatasGbV9d+8flxAivwrJ0HyeCSR66nHsEulHOdVCCFHMYnDAgAHWztm7d2+TLhCCiSD3gnLURCCJRiIEEaKusE26oNx1JqucpRM3M8/3EkYSjR/rcOXKleNaOYMq2FLUxUX9EJOsw59D0qFDhyihyXP/OMgX9D9fvXp11GtMUAQJyXRU1s1kYYRc/+1kM2Eee6bGT6rAnDlzTC7gjXISHWTilAmhzZs3l+i4chXlVAshRDGLQS6gf//9d1Pc3H///VENa3v16mVthOkWgyL8J/lkckiSmU0Oqib68ccfR4rWeO2qmUCFEYQIH97jg2tCTyqF/3UhhBAilGKwZcuWVoC1adPGRua8ZKohPRGSBx980DbzLVeuXCQiE1TIRmQ33mgXQqqwFspkomXJ5JAkIzSDqomyfaKI2JZr1aoVN68xVVQYQYjwMGTIEHvj+BSL5s2bF+uYhBBCiLSLQfIeOKEhxmLl96UbLtw/+ugja78ZOHCgWb58uXn66afN0KFDi2X7In02xqBoV2EslclEy5LNIUk0/iDByGvkDTqrHM+Deg2mAxVGECI8uHZLhxxySEkPRQghhMicGIx1YZ0J6+ixxx5rxo4da8UnxWXoI9eqVSsbHezbt6856aST0r5NkVkboz+6FZRvl2q0LFl7qX/8v/32W4FIYCJhd+ONN0aspG4d/FbTgQojCBEeXEGzTDlkhBBCiKwQg4sXL7bNc6kk6hLluQCm1D75VKlwzTXXRD335lgcfvjhNu9CZA9FsTH6o11U24xFUOQundEy/3gp9OD6GiYrbvmM/3m6xKAKIwgRHqiqnagtUToKSwkhhBAl3loCYUbJbARg27ZtrWWTgi4iv0gkzILEnDfalShnMChyBwcffHCkoEsq0TL/+P0oR08IkSyumBluh5kzZ1pxWKNGDbNhwwZb3fj4448v6SEKIYQQ6YkM0oibfMG//e1vtvk8JznypijwIvKHRDbGWDZSF+1K1LTaL8YmT55svv/++8jzGTNmmHbt2hU5V4/PIDBddK9atWqRyCAw2cHvOt66gyqOCiHyj65du9r7kSNHmgkTJkRyCIH+uD179jSDBw8uwREKIYQQaRCDBx10kL2n0fbSpUsjrR7o/Sfyi0Q2xmRtpLEKufgjdz/99FNgvmFR2y6wDXIEnQDknjzVdevW2cfcnJiNte6giqNCiPxl06ZNgZWut23bViLj4fh6zjnn2JxGtWISQgiRshikgMu1115rhg0bZhu0P/TQQ/aiOpnG8SK/SDa/L1YE0R95nDRpkvnll18C1+GEZmErnPoFKkKQiQ5vhDCeXVR5fUIILwivSy+91LpmcBswUUq++/nnn18i46FH76pVq0pk20IIIXJQDN56661m3LhxtngM1T25bd261dx5553pGaHIGZKthplMBBE7JxMOXpuoFyc0C1sh1C9YEYFee5d33UIIkQiOQeQIYj+n0Br9eMkf7NGjR7GP5dNPP7UOChwP6WbBggUJC+akix07diSVWpDN5MI+5Mp+5MI+gPYjP/dhz/8V7yxRMVimTJlI6ewKFSpYYSjym1jRuGSjZn5BRg/L1q1b23tXUdZf6AW7Mvl5vM/nb7/9dpvfRyuSwlQIZazPPvtsVCQQ8UhhG1k/hRCFZcCAAebee+81vXv3zvi2mOwKckuULl3atnsaMmSIde8waSuEEEKkJAZVNluks99grAiiVwDGgxl3xOb7779vt49l2SvoYs2g+NufsA7sW278QEEkWT+FEEVh7ty5Gem7GwRVS/v06RNYzIa8xS5dutic/kzAMXfvvfc2xYGbbW/YsKEJK7mwD7myH7mwD6D9yM992L17t5kzZ07JiEGVzRbp7DfoxRtBJCKYLAg7rxD1UrZsWXuR9N5770VZS1esWGG34Y1gqrm7ECJdtGzZ0p4v27RpYy2iXtLdkJ7WTgsXLgw8NnJcvO2229K6PSGEEOGnyGJQZbNFUQrFFLagi39d5LpwsRNkhcIiFUt4IgQRmFyYeWHygps3gqkiMEKIdLHvvvua5s2bW5cDLZhKgn//+99myZIltuAbcPwkYskN66gQQoj8ZZ9cK5stSp54kTW/hZT8PGyZsaJvQety7Uz8UKWPGXiveKR6HxMTbj1ErD/77LPAz7MNv1gl95D8mmTFqxBCeIl1bCsu6yhQ0M1b1A0nDxO6ai0hhBBin1wrmy1KnniRNX/kztu77/LLL09qXXXr1g3MI8T+iWXZS/fu3QsUiPEKP9ebEFq0aFFArH7wwQcFitYoaiiESJbFixebUaNG2bxml7OMi2HlypUFcpaFEEKI4qZUqivg4pmkdKo0EkF55ZVX7Kwj1ReF8BOrLUNh8gqnT59umjRpYqt8+sEK5cUfBXTictasWebEE09MOI5FixYVeZxCCME5kmJrderUsVU9Tz75ZCsMe/XqVWJjIq9fUUEhhBBpEYNUD6Nk9tNPP23eeust89RTT9moYHH1GxLhgsgcEwUHH3xwQpFI5I72EBR44Z7ncOCBB9rKSbt27bKiMKi3S7z1xhKKPPcvTxQy2fUJIURQZPDuu++2k6Qcw3DRPProo+bNN98s6aEJIYQQqdtE582bZ211q1atKpAD8e6776a6epFjuMgcotBfSGb+/Pn2t0Q0jtewUj388MMFGsZjrXKP/ZE7XgfEZrxcxFiFbvw5ikE5g0EgQr1jV26hEAJcjnPNmjXN0qVL7WPaO5BSIYQQQoReDN56662mcePGNt+ruHoMifATlAuImMK+BIg0f/TQ2zA+EVx4Qbt27WKKs6DiNEHjSiZH0D/2ZD8nhMhtqOB57bXXmmHDhtlefDR95zhTtWrVkh6aEEIIkboYJAl+8uTJNhdCiMLird7pZs3TARHCRI3v09lCwluIBpRbKIRwE6bjxo2zxWNwGXDbunVrVHVPIYQQIrRi8IQTTrC2PSIwQhSWWE3igYsn+gpiB6UlhNc26oWINNFpcMv6q/TRwoLIH+KzQ4cO1s5JPiDFaMhBjEcyvRHJXaRnl0O5hUIIKFOmTKS5fIUKFawwFEIIIXJGDO7evdtcddVVtlJaxYoVo9578sknU129yFKCBBIUtkefP4JGfg2CDjsorSK4IQjd+nlvzJgxUU3n+Q3C7NmzI69RcMZbIIb1MTZvqwjuEYbezwXhbzcB/oiiq57rzRkUQuQvFIxJVEhN50ghhBChF4OnnnqqvYn8IkggQWF79PmLuHTs2NEKKm9uIOtA3LEc75UtWzZKDMJXX31lBaoTm4gxooHe9fB8/fr1UZ/zF6AJwi9Ygyyg++23nxk0aJBp2LBhwvUJIXIf17oBCzn9TxGHNWrUMBs2bLD5xTgYhBBCiNCLwa5duwa+vmXLllRXLbKYZARSMj36EG3YPykOwz1RPpq/ewUiTJgwwWzfvt0+3rZtm7WGuogg0GbixhtvNCNGjIi8Vr169SgxGFR8xt86Itmqo0IIkcy5ceTIkfb4xfHI0b59e9OzZ08zePDgEhyhEEIIkQYxiBXv/vvvt010yfECLuo3bdoUlUMlcosggcTf3fsa5dO9hVWCRBSRPGcLhWeeecbm32EX5TcVq38gkbidO3dGWkkAgtKJQSKXLioJbMO7bKlSpex2yBlMRFDVUSGESAbOhRyv/DCpJYQQQoReDA4ZMsTOcmLdw6rHbOjYsWPNRRddlJ4RiqzEK5CI5GHR9Oe/tGnTxv42vDmE2D39OYT+iCECslq1alGvId68fSyPPvpos27dupitJvzr9ApBGDBgQMSymqhATDqrjmaSZArdCCGKl3POOcdceumlttk8xzX6C44fP972QRVCCCFCLwY5sQ0cONCsWbPGvPfee+aUU06xeVO9e/c2l112WXpGKbIOr0BC4AVV+aQwy6xZsyJCBXHozyFEIC5btizh9lyTeQoyNGjQwEb0KNnu3W63bt0igoiWJ7Ggf6E3updMgZgwkCv7IUQuwf8lOYI4F3A7VKlSxeYP9ujRo6SHJoQQQqQuBmmci92FxHguwInecLFNkrzID2L11MPG6Yq6+G2bQAT56aefDvyt8Bv66aefogrFuOge0UbaQQwfPtwKRG8kzN+qgv6XLOvdBjPy3ohZMvmPYSBX9kOIXIL8ZiZHuQkhhBA5JwYpzc9JjnL/2AVvueUWe6F9+OGHp2eEIistiN62EbwXBOKPzxCdChImTCLEypvBAhprvYhIQPz5I1/+7VBYBiFIe4p99903MOfPn/+IiG3dunXorJYqdCNE9jFv3jw7QbVq1aooqzu8++67JTYuIYQQIi1ikAqOU6ZMsQnyf//73+1Jj4R5ojYid/BbEL1tI+LhxCICqzD4W0B4QUC6sSRqVeEVl8uXL48IO6+4Pe6442wxmSVLlpj9998/YTuMbEWFboTIPrCzN27c2Fx++eU2SiiEEELklBgkEd7lBlJEBkEIDz74oLn22mtTH6HICj7++ONC9+cDRInfIlqmTJlIm4hkQaSB1zYaq1UF+HsMuqbzCLtY+Yvgj1SGyWoZlkI3QuQTpE9MnjzZWtaFEEKInBCDRG1oogsPPfSQqVy5ctT7P//8s+2rJDGYO1Ul/dU4EWexLJ5+S2a7du2i3i+sEPRWD/WKwVitKhBEbLd27dpRghB7KRFNih59//33SW2XiGbLli0jBWxoFB0m66gQomQ54YQT7GSa/zgohBBChFYMVqpUyVZzxA5KTtaLL74Y9T5CAGuMyJ2qkgghL7Vq1TK9evWKtJZwPSeDxGcs62YQ2I29PQWpHsq6vJFFistQBMZvg/SLYKqLequNIl6TsbY6MYsQ9C/PPoIicEKIZNi9e7e56qqrTJ06dUzFihWj3vO34xFCCCFCIQYRBkQE3UUxrQVEbleVJCLmhBCceOKJEctloiikN5ctSGB52bNnj83fmz9/vn1MRM4fSaxZs2akcmisSqKIz6uvvtpcd911NiKYqMEz22Tc3n2giEwQRf0+1QdQiPzj1FNPtTchhBAi53IGf/jhB9OpU6fI8+eee87mkp100kmmbdu26RifyIKqkogYRBkROSDi5gSeX4A5O2VQ1VHvc2xTCEMazHuh2h6tI/yve1mxYkWUBdTtq1+kIV5dn0Nvu4kgEGVu2UQRzaJ+n+oDKET+0bVr18DXt2zZUuxjEUIIIdImBr/44gtbHe2CCy6wFUW5yH3++edNly5dbBEZ8gXPOuusoq5eZFFVSUSM126J2EM8IRIp1OKFxspekeatOspzlsfi+eGHH9r1+kUf0cBJkybFHU9QX0K3r0EimP1HpPL7/PHHH+1r9B6kAbR/WS/ue0O4+nMGi4L6AAqRfzApdf/999vjDcc34HhCmsXcuXNLenhCCCHynCKLQcTf4MGDTffu3e2JjebhRHzOPPNM07FjR9tvUGIwN6pKxhIxiDlvgZZYPba8sDy/Hb+I9ObX+Iu7JFN9lLzFWCKY7wMh521XwYVZvN6Dmfgek4nYykoqRG4xZMgQ63ag2vZXX31lI4VY1y+66KKSHpoQQghRdDG4YMECWzEUvv76a3uxjj0UGjZsaFavXp2+UYoSJZaI8YtEbKT+oi3+KqSORCLSS+/evW1hmUQ5h17x5hdV/tYYgBD0W0MzKcqSidjKSipEbkH14oEDB5o1a9aY9957z5xyyin2HMlxzbVlEkIIIULbZ9BdtDZo0MDOfALl/7nQFrlBLBHjF4muwqfLGaS/ViLR5/IQ4y338ssvm8MOOywq59BfFMZb3CZIVFWpUqVQuX/+zzt7ayqiMJlIo6ykQuQWVatWtceqGjVq2GMiedEc94Ls7kIIIURxU6qoH8RiR24XOVhcrJ922mmR95544glz3HHHpWuMooQgOkalWPpjEeHDiok4QSjxHsKIap2tWrWy904oIXiIuCGeEsEyRJmp5ulyEY855pioZRCKCDLEmWtZUq5cubjCzi+ivBde9Eik0mi83D//5529lX1Px3dKpVLueR5vP1It/iOEKFk6dOhgo4CbN2+2x1BSKMirP/zww0t6aEIIIUTRI4M333yz6dOnjxk6dKhp1qyZufDCC+3r5Aly4U0OocgcQTbGdBNkWYxVNdQbMXNjw5rJpIG/ybs3V49IHxdLrpAM6126dGnMMT3++ONRjedj9RyM19uwfPnyZsSIEXH3PdbnU43UJbKBprv4jxCiZKHA2pQpU6zVHRHI/z/FY4YPH17SQxNCCCGKLgZpoPvOO+/Y2U6a0Dv69etnL2L9zXVFegkSFVR3jUVRcuDiCR9/1VBwfQeJenmrhDZq1MgWGgpqSk90zJ8DGK9YjFcIAvbRIOulE1HYO/0WVIrHsN1430Gsz6caqUtkA0130RohRMkyfvz4SG4gqRQIQnjwwQdt1W0hhBAilDbR999/35QqVSpKCALVRP1CcPr06UUfoUhLbpkTj85umYzdsTDCx1th1N8ugoqiRPwQihSXqVy5srnqqqvs81QjbfQcDLJbOlGFWPTDWBJ9B+7zy5cvL2CFTQXZQIXIfahc/Morr9jbQw89FHnsbk899VSkAJsQQggRysgg0Zxx48aZc845x1ZHo2+bF3IJ3333XfPiiy/aPAmsgEWZUV24cKG55557CrzHxT+5F/SxY7aVC/VYzX3zubG8iwhScMVLMiLMa1nkbwguusd6R40aVUCUBVXthCeffDIS1UOMjRw50sycOdMsXrw45varVatmfvrppwLRQC/YT12ENCiiFs8umsx34I/UuZy/olYZlQ1UiNyHSVImQbGD7tq1y54HvWCTd/nPQgghRCjFIBfE5HY99thj5q677rLRnoMOOsg21SVncMuWLbaoDO9hKS0M9JpDaHIRjtgM4oEHHrB2whkzZpglS5ZYi2T9+vVNvXr1TD4QJCqCcu28dlIvTtDFs47ymNe8yxAR5vVrrrkmUJSRDxgEF0R+/BHEunXrmubNm9v9cOOJNf5khZ33e/K3pShKVC7V1g+ygQqR+5BPTUQQ+H/nfCmEEELkXGsJRB4X25T6p5kuggDrKEU9aDVRunTpIq130KBBNiJEnhkX8EG89tprZvTo0bYhOTlpnTt3NlOnTk27GKTS5V577WWyEQSwyxNEQO3YsSPS9zGWRZdKmn/6059swZ8rrrjCTJw4MSJsfvjhB/vdexk2bFjgMh9++GFM0cesOJFhB89p7UCUNx7MltOPi0ILbp8YJxFIvw3UD5MPCD4ijohMqpMSLWZd7nvi+0HIufdZN5+59NJLzapVq0ytWrVsgRoKzMTC/31i93LFk9x379+OG0c2E/TbCRNhHn+Yx56p8TOpmAtwvOzUqVPk+XPPPWcWLVpke/K2bdu2RMcmhBBCpJQz6KCaKBfxtJLo2LGjOf300+0FcCoWmJtuuskKvaDecO7Cn5PsEUccEXmtdu3aZtmyZUXeZq7C38ILQhAxhzjxR+awMiH+3MUd+Jdxz/3rdRAZpK+WF55j+SXyFw8so0ST77777sgYGGes6LCXb775xoo6hOvcuXPtvT+iyLrY92eeeSbyHfAZPosVlXsu3C644IIC34PDv98Uo/Fvh+fxxiGEyH2++OILm0JBJVHgOEDRGFdV9NVXXy3W8TgnCJb/448/3owZM6ZYty+EECKHIoPr1q2L5EAw0+nPF6TBbipFY/xiwo+rNklU0Gu/SxQ9KgpYT/fee28TBtzMfMOGDSOvYVUiastsNGKM5+7vRR4ngsWBIEK8YPl1VkZmr73L8Jz1cyHBcuQIujYTXGDQKsIfnaUnIJZMIoMHHHBAVLN4L4gvbog1IrLkFAIiLRlWr14d9Zx99n4XyXyGHB9u7LP3e3AXUoyF6Ko3j5HtuMgf2+N5YceRjb+dMBHm8Yd57JkaP6kCc+bMMWEG8Td48GDrcOEYSbslXDQUWWPilJx3WjEVF+Tec2zD6s89LXmY3KI4lhBCiPylSJHB6tWr2wv6NWvWWDsP994b72UyL8rbz87BYy7S8xmEFBEtb0NzLj7Ik+Nvwj3PHVh8g3L8aKeQSFi7fEIEIJFh7p1l2NtTELgQatmypY0eJ2v/YqxEe9u0aVMgOhmLI488Mur5ypUrAyuNeokXrfTmIbpcQQro+AvaJKoQqoqhQuQfTGjhxHCCmUlM7KFOOPsnojIJ6RZEKG+77TZbcO3QQw+14jSRW0MIIUTuU+ScQZdbxkmFHkqFqaiYKkS2yEOjgiW5iUALAMRDPnP//ffbqJq3MTziJV6hFaK8fuirhwgjMuf/vHuOwGIZV5DFFVPxr5+Iob+PYLIwDn+PwHggONu3bx/pDcgNAUfFWfYl6DdKBJsIKdE7JhMofhQk4vz7RV4sbSuCiveoYqgQwgvHR85VCDFgQolJtHTDMT+o+jITdLgX6A2MZZ88ePKoscSHMb8+7Hm2ubIPubIfubAPoP3Iz33Yk4Yc+5QKyLiWAVdeeaUpbsjtwvJ43333WVFIQZknnnjC5DPTpk0r0BgeK1CsFhREu2KJLQQc7/tbMzDDjOjiO/cKJ6/48S7PxUk8yAtFVPGPM3/+fGsPS8a6y4WHH3JwELeMw7tfbl+CotVMLMyePds+Dqqu6vDvF99rrOi3KoYKIXBdTJo0yVpCX3755UiUEDhX4ZRIN0x69enTp8DrHL+2bt1qj5uvv/66LZjVu3dvWwRONlEhhMhvUhaDnOiISJ1xxhm2tYR3ZrBGjRom3SdX+uXRfuCGG24wd955pzn55JNt7iAX8YgEEU1QlCpW70E/5AO6qqFBrRn8uPUTiUs2GkgRoFmzZlk7J83pg+A35Z35IG+vb9++tq2JV2xyscO+BfUWLEpPQQfrZDtEA6Fbt25R3yPrxm5F1VAhhICbb77ZCjOKrDVr1ixSdZg8QSbSsGmmG3K6g6o2kwNN7uKAAQOsA4K8bipwv/fee2kTg8WZXx/2PNtc2Ydc2Y9c2AfQfuTnPuxOQ459ymIQSx5gPfFfwAdFbwqDv5edV2BwQpP9LhoqcTqbqBMtQQIH4ZVMhUsEkPfz5KA2XucAADzcSURBVCLGE+quT2Fh7E8ITNZLfl+QDZPKd48++miUGOQxAgxB+PDDD0dexx6FOAsSpKnk7bFO73awvrKf3u/RiU8i5UIIQdQNW+bmzZttWoOjX79+9nhUsWLFYhsLbXM4J9OyiZx/dwERluJoQgghMkfKYjDZSo8i89Cjj5M7+W/xctX8UTIuEvic39JJz0gvQRE3JwS9OXmxlvPDdmNFEJ0NE8H1+++/B866EFHECuu1hLJvjIPxxLJ8Fhb/9+We+19PttCNECI/4BjqFYJANdHiBjt8u3btbM/Wu+66y06+YRdlok0IIUR+k7IYfOWVV5JarkuXLqluSiTA9dFLFJb2izUibUG5fV4R5rVKsjz33iqi3uIsXmsq1s+gggZuu15o5XDIIYfYgi7edcSCixtmub1i0EUA05m3R3TS+33x3G3L+3qs3otCCFHS3HvvvTa1guMr7gZs7aRcCCGEyG9SFoNvv/22rchIvkDNmjVtLgTRHp67ymlEgCQGsweElqu4GQ8uGGJZJXv27Bm3gArb4DMUmoklBhOJTz7vt486EKZOiBGZRJgmigAGFYgpShVctk3EkpxKts33RA6OcgaFENlK+fLlzfDhw0t6GEIIIXJNDJJ3cPfdd5uuXbtGXkMcTp482YwcOTLV1Ys4+MUNBQpc8/NEwscfUQuCqF8iq2SscXnbTiSLt+G7P+cPGyt2K/IIv/3226ixY4ulUEMiced6BYITkslED/3tNYiGe7ePCKRMuxBCCCGEEDnfdN7Lp59+aqujeTnxxBMj5fpF5nDiBmHDvbcojP89ejC6Buy85xVaVIGtVq2avWF1xAaJwMFySvnzAw44oEA+nLNKxhpXIiGYqB8VAs8/6UDU+YQTTrD5hF62bdtWYB+DiCVoWZ7PUcgm6POJis/EEsaJ1ptuint7QgghhBAiz8Xg0UcfbUaMGGGrQgL3tJrIRA8lEU28Aib+91wDdgSTqwDrwOaInZNlKMpCRJDPN27c2Io6xJZfWNA/i36DQaIjXtTQiUB/vqDXkupaTgTB2G+//XYrVp0NOWgfkxF17rlfOPPcC9vDDsr2uPdbnmOJxUTrTTfFvT0hhBBCCJHnYhBr3vvvv2/7KBERRAQuWbLEViwTmcUvQrwFTGIJFAST3x7Kc2ydlEDn3gkKf1N5Lz/88IO1T7IsZcuJHvK3Zx3xooa0BAnCX8Bm7dq1Mcd/xx13WHtnUHNltwzjYl+8gpDfKiKSvlrcxypS43/O9pwo5t4VX/Cvx09hrLXpoLi3J4QQQggh8jxn8PDDDzdTpkyxhT4QAjSar1KlSnpGJwrVUN41Nfa+RwTv+++/Dyx57i3YgsihcXo8ARgL9xnWQaU6b66hH7aRTC4hYjMWTuS4fYxVDIftEB1zeYGxKozGqhbq354DEUwENRH+aqOp9DpMhuLenhBCCCGEyHMxuGvXLvPmm2+as88+2woMSlfT0wibXnE21c1H/OKG3nv+94i4eauAet/3V/ksihAMEmCxonpYRIkgk/9Hywm/VTRZ/O0jEIVYQ4MEYTqiY0UVWX6xnkqvw2zcnhBCCCGEyHObKPlUEyZMsI8HDx5sLXQIxFtuuSUd4xOFKBxywQUXmGHDhkVZI/2VMB0XXXSRzX+LR6K2C4i7oEIwQZFIQPw99thjtlpoPCEYL7LMhAPRTqqhXnPNNXZfGae/qIyDHNZEBVX835H/eSx7aSKcWCWKyH1R2lgUhuLenhBCCCGEyPPI4MyZM81rr71m1q9fbyuIzpgxw5QrV04WtQwR1DLC2zIBoUXzdhcx9Ee1oGrVqpH3WTZWH0D6UvG3jBUxjCfoEIlFjfz9/PPPpm/fvjbKuGzZsqjtY211YpOIJ5MPLjoI9P4jGsrr3DtLarxWEkGRv3T1JBRCCCGEECJnxSAXzTT8fuONN2yjeXrB/fjjj6Z06dLpGaFI2CvPb4Ukh47XyH1DEPmFGY+dsLn00ksDbaQuCrdly5YijbOoQhC2b99u943JBfJQyUP86quvbMTZj3/fGfNJJ51kxVu7du3iLuvwC0nuvX0SC9OTUAghhBBCiLwRg6eddpq55JJLzPLly80NN9xgli5dagYOHGg6deqUnhGKhBUj/ZEtVzHUHxF0eCOBw4cPNy+88EJgvl3Xrl3NyJEjTbqhgTx5g/EgpxArKHTr1s1G+oIsr/72EOD22/+9YBl1ttIgeyVWUm+vRi+qzCmEEEIIIXKNlHMGhwwZYqtYIiq4aCdK2KtXL5s/KNJPUK881wePtg3JWBmp6OkIyrc7+OCDbW4coi0TJBKCQITOiVoil/7WE66VBvuOiBs7dmwB8UbEz5sXSaSPiF+sHMJ4gk+2ZyGEEEIIkWukHBnElnfGGWdEntesWdPeRPFVjCQqlky7Bkfz5s0jETLuEVoIQEDQI+x5D9FUErh8P//vDLHrjWoy8XDrrbcG2lz5btgHlvESz/rpjyQiJPm8KnMKIYQQQohcJGUxKIqXoF55/oiWE3ZB1k8gila2bFm7HoSkV0xRAIg2DVCtWrW0jp1xYf1MJFwRYH4xSOEYf6EblsHi6heSV199dUS8+XsIxvvegoR2NhSNUTEbIYT4/+zYscNa+hctWqRjohBCpIjEYAjxiwO/4MH2Ga/3HtCeATE1fvz4qNe//PLLyGM+26hRI1vNk4IwFHChOFA8EJDdu3c3zz//vK0w6xd0tGcgMvn000/bHL4KFSoUaDAf1ALim2++KfAalVP322+/qNcOOuigpAu9+K2fsZrSlzRB+ZDZOE4hhCgOOB5OnDjRPtYxUQghUkNiMIT4xUG/fv2spXHhwoWmVq1aNo8OYYPlM1alUNozxHrPy4IFC0zDhg3tDCxVPuNF/dieGxPCzw/icNSoUTbXz0X5/EIQgj4btG1yD/3RQjcGh7/oDOM87LDD0mb9dMJ8+vTpdr/GjBmT9hnqoKJBQgiRr3gnLUHHRCGEKDoSgyHCCQ9/sZSXX345EgEkgnbHHXdYoYPlM1WIHvpPvEF06dLF3icjMOfPn2/SSTyB588DJGqazhnkeD0e00VQH0QhhMhXmHjjeOvQMVEIIYqOxGCI8AqPeDBLyrLJiLh0QcSvTJkyaasmWliC8kaCiuOkuxBMcUTtgnIZhRAiX6HaNXhzBoUQQhQNicEQ4RcaFIHp06ePFTzeiBy9HoOKphxzzDFm3bp1gdbMdBDPRprIBlpYqC6K8MMmSlTUiWRvVM5fHIfiMn4LZ6rFWYojapetuYxCCFESkCs+aNAgm8IghBAiNSQGQ4RfeCAEEQkIGoQOeWurV682GzduDPz8XnvtlTEhmGqTeb8FaPHixVH5gIydIjaOqlWrWmuo9/tIFKV79tlnC4g+f/7lBx98YGbOnJm0IHQz0i5nUDPUQgghhBAiLEgMhoh4dkGig2vXrjWbNm2K+fl58+aZ4sL1MIRYQpCiN9h8tm3bFnkNUQv+wjBeIQibN28uUK3UG5Vj21Qr9eKa2DvRRwuLlStXRi1D2wsEoj8Sx/puvPHGSCsLbz9Glv36668j+y2EEEIIIUQYkBgMEbHsgn47ZDojemyTZu+ITH/vv3gEtYfwgujDLnrAAQdEicFkC9b414+w9IpjvhNvP0N/w/p4vQ6D8v783zGP2QfZN4UQQgghRFgpVdIDEMmDALr++utN69at7b0TRJkqq01ED6FGn8FUczNcxM8v+mhx4WygqUCUzxuV838n5cuXj/lZhKKXoLy/oO9Y5cyFEEIIIUSYUWQwRMRqPu7PJcwEFJ4pbO6fFxdVRBQGRRj9NlB/JC8RfgHn/06wdbJtBBwi2ht9vOiii6yQjFetM+g7btGihRXlfK5u3bq236N7XpRiNEIIIYQQQhQnEoMhIlaBFEQHAosCKeTSJWPnRKQQjUu2Aqg/Py+VCF6i8SEEL774YjNy5MjA96tUqWLF45YtW0zp0qWtmPMLOPeduBw/9xr7fc0110SJwWTsnv71ueb2XnFOg3v6PLrnIBupEEIIIYTIVmQTzWL7ZzLRL0DgUMykY8eO5sADD0x6m8kKwXRCURfEHD0JY/UlJCJIRU/6A3JjeS+1atWy1tWtW7eaK664IlL0xfu98Z0g8lzRGHL8WAYQbV78z4P+LnyW75gIKbcRI0YU+NyqVauinstGKoQQQgghshlFBrPY/lmYaqKsY+LEiZHnqVg609EDaseOHYHvEV1DyCXCG7nzi0YnuhJ9b7Eiqcn2Bky0fv96EKkuMhhvvan2NhRCCCGEECIdSAxmAYn64yXTfDybolA0hE8nfmFJURsEFbbYeN9BLNEXT1THW5//uXc9Lmfw+eefT7jeZMW/EEIIIYQQmURiMAtINlIVK7pEIRO/tTTZ6pxBBV2IxKViIfWvr1q1arbZfWFaU8TbF/IFa9eube2fXvzfWyzRF09UF+bv4l2P6zOYzHqTFf9CCCGEEEJkEonBLCDZSFWi6JKDHLtkrJjgBBrRPMQNxVjIhfviiy9MUalQoYLZuHFj5Hn37t3N5MmTI20kCovf7opQ9YtVcgv931uyos8rrj/++GP7nSCSW7VqZa6++mr7fST7d8mU+BdCCCGEECLdSAxmAYURLclEk2gQX1hoAE/hFkQKIqiwYpA8QQQUEbsFCxZEvUcFznPPPTdmddB0QHVPl3dXlJw8r7h2IAKvu+46M2vWrBIX/0IIIYQQQqQbicGQEq+3YDJ2zFj9/qjMuXr16iLl9TVu3NjMnj27QCQPO+f48eOt3dPfT7CwxBp3YXLygsRiLHGdCQtnUcS/EEIIIYQQ6UZiMKR4o0vkDML7779vlixZErM1hZdjjz3WRgAff/zxAs3dvRbPwhCrRQMkMyavWIq1PEIQGyw5iEQzvdFHZ+fE6ull0qRJkWIzrj8grSacWPzggw/M8ccfHyiuZeEUQuQCtMT529/+Zis1Y+XHAt+1a9eSHpYQQogSRmIwpLjokjfXbc2aNQVElGvz4I/KuQbqfiGYDSQSjkH5kK6fIIIOoevFm6uICCS/0B8NRQxiCfXmDPKaLJxCiFxg6NChplGjRmbs2LHWyt+zZ0/TvHlzU7NmzZIemhBCiBJEYjDkBOW6BbVlcEKQQjEVK1a0AijTFEevw9KlS5tKlSpFCT6EHMKOqOnKlSsLVB0NgqhiunMDhRAiW+BYeMQRR1hHBZODHDs5RgshhMhvJAZDTmFz2rgQwGJZVBCTXntmEGXLljV9+vSxETZnx0wlAoqgRMwi+ti218a6a9cuU6NGjSgxuGLFCjNt2jRz4IEHmuuvv76AWMYqSgTQK4hTaRDPMsOGDbP2qw4dOqiJvBCiROCYG+T2QPhdcskl5vbbbzfjxo2zx9TbbrvNHjvTBdHGZFsapYqb5HQtfcJILuxDruxHLuwDaD/ycx/2pFiLAyQGQwwiZOfOncW6zURCEBCCWFhbt26dVsvo+vXrbc9CP0QCve00uEeUUcwGYeYssU4IDh8+3D72i7yiNohnmYkTJ9rHc+fODVxGCCEyzcyZM+3x1w+5gc2aNTM33nijtYfOmTPH5gw2bdrUNGzYsETGKoQQIjvISjFIhIUZTCI8DRo0MPfcc4+pVatW1DI///yzLZzijcAMGDDAzn7mC4iQRHbPypUrpxQJTJYmTZrYvwV/E8RXy5YtzfLly9O+naCZZ3L75s+fH/XaokWL7D1jGjFihL35SVeDeDWRF0JkA23btjULFy4s8DrOic6dO9sJLdwdHJ/POOMMM3Xq1LSJwfr16xeb7dTNtodZyObCPuTKfuTCPoD2Iz/3Yffu3XaCL6fEIKHV/v37m5tvvtmcdtppZsyYMTb/66WXXopajhPeUUcdZU9m+UoyomPr1q1pz+0LahGBgEd4LVu2LKmG9xR52XfffSPi8ZVXXrGWT8Trt99+G7OwDZE9ln/yySft8kwWMHFARVCvMD7yyCPNNddcUyAiWFj7ZjIN4tVEXgiRzWCtx0WCswMx6BwV3IQQQuQ3WXcmoIgHuV7MYkK/fv3MhAkTbMsELvAd33zzjalXr17Gx1OceRBBwhiLIkKLyBuimOqgjrp168bsNehAMCWisEVeYvmTsXQmqgTKd8nf94EHHrDWTgfWpgsuuCBwVhsQcTSuJ/LLd+LEIt8NkwInn3yyfY0eiUSR+W148xV5/OOPP1rhG+v7DOLCCy+0kVX3GZ77PeC8hoUViygiN2iZbCfsHv0wjz/MY8/U+NORAyH+P5w7Oe7ihiCPmvPa66+/bidbhRBC5DdZJwaxFlLxzMHFO6WviTh5xSCiARvp6aefbkVAp06dzA033GCjTbkCosefizZo0KDI+4gZQKgcc8wxVtTNmzfPzv4y40sVzVg9A6koumnTpiKP7aCDDrLfe2FbU3CRx3Y7duxounfvHtmHXr16xRSCjPWdd96JCDf21wv7+Nxzz1kR9uKLL9rXEJZ+KCrjvo+g7zMItpnMMgMHDow8FkKIbILj0ujRo81dd91lc7lxYNx66612gksIIUR+k3ViEHHht/KVKVPGbN++Peq1/fff3+Y9XHnlleann36ylkBOdtynk+LMg/Dj8t68z/3+Y+ySDgqmICBZDquiv5onUTn2hXVQXe7zzz8v8tj+/Oc/p1QtlAgiQpfmxy7SGy/y0Ldv30jfP4rDODEX9P2w7qC/md8SFfR95qvHXeMvOcI89kyNPx05ECIa3BJuclEIIYTIWjGI8PNbDRGCtCvwQk6ho1y5cuaKK64wo0aNSrsYLEmSzUVz7Q+eeuqpSOSLz1F501tlk6gcAo7ImteiCTRi79Kli82xixVN9P6N3n33XWvdTZXHH3/c/v0STRDQB5AbuMqfkyZNimopQf5hUGEdRCAz4Xwf3p6Dyu0TQgghhBD5zP8yybMILKLYP70zxKtWrTK1a9eOWu6hhx6y+WEOkuNzzaKH6MFG2apVK3ufqP2BX8QhlGIVc/G+Tp4b9lwisomEoBPn2FH90VovFClgvVhCnRU0ltAjshskOGMVzWGc5L5gM421jBcEMN8FIpgxJfo+hRBCCCGEyAeyLjLIhToFO6gueeaZZ9oEdwqC1KlTJ2o5WgkgYO6++25bGITl6J+USzjRk4hUWxmsW7fOtGvXzqxcuTLwfSJrCKrCQCSOnlfsgxNdNHrnb+YXnAcccECB3MNYQpNonouEjh8/Puo9FzkkQhirsA77wjr4zliHGsQLIYQQQoh8Jesig1yYk/tHbgPCEEHhmn5TJMa1khg6dKgVKCeddJJtG3DKKafknBhMlkR2R3+unD+fDusk4slrofTnTZKjWRiIPLZp08YKNydqyfkLijxS8Aabqh9eQ9gRzeOeJsmsD6HJb2Lbtm1Ryydj++Q3w2fZX+4RhEIIIYQQQuQjWRcZBCpjuqqQXiiF7a1mGdRIPB8huuXPnwOKxNBawh/V87aSQOR5o3Lk1RGJ5TMuIpiosX0s+ByVYIny0QbD9bfyw/bJ+XSi33H++edbEekigc8++2ygYCWflNYULgLpIoReUXnYYYdZsUh00osaxAshhBBCiHwlK8WgKBxE3mrUqFFADBJBixXtiwXCiYisa9aebL+vRo0amaVLlxawe7pIIMIwVh4gfQIRnYyXaF/58uVN9erVrXCjJ1aiqqVOCCIY+Yw359QrKoH1ecWiisgIIYQQQoh8RWIwR/BbQYnwYZ9N1PqBSp5eAUe7BX8fv2QgwkiuJ4IuVu9BIoS0t/AKTMa9du3aqHGSQ+jGgHDz75sXLKROCPoji0AfLcQkvbUQfrfffrt9HdHI605wKndQCCGEEELkG1mXMyiKBvl4XsifHD58uBWF8YSUvyJnvAqh8aCaa9u2bRPmFvojjQgyf8VTf16h3+bKNohgkkPoitTEsnsSLUVouhzBO+64I5K/SLQSsancQSGEEEIIkY8oMhhCXA4dAohoF1EtbkTmiKjRlN1Fuvz2UXItKSBDO4c1a9aYDz74oEjVQr1N7CkAU5S8wqJslzxI9tMfxfP3ZIyFE41+8ajcQSGEEEIIkW9IDIYQryXSCSCiXYMGDbKPGzZsGFnWb7HcvHlzRIBh51y/fn2Rx9G0adMiF5eBZISg31baoEGDQDunt32FK35D9M+fb+hyBP3iUbmDQgghhBAi35AYzIKoXmFz1eJFtXbs2GFz4Nz6mzVrFlUwJZEAI3KYTON5IoLk+mUahO1XX32VULTF6snI9w2uIA77z2tOPHr/DkIIIYQQQuQTEoNZEtUrjHgMimqx3LBhw8ybb74ZEXMsg7grDLRgII9wwoQJNg/wwAMPtLZMf65fpUqVClQvTRXGetppp9lqoq+88op9zZ9PSP9JbyP7RMKa50QJXVVVooQ85zuP970LIYQQQgiR60gMFjOFzVULEo9BUS2WQyj5SSbK52XVqlU2euYqgvJ5BJWLsAHP6R/oF2pB+PsYxitm88gjj1gh+Nhjj8VsiUHrCW/V0GSEtfIDhRBCCCGEKIjEYDFT2Fy1ICETZIlMl8BB4PkFpFcIOusmRWOSEX1EF/15f0GQezhy5Eib9+jfFyqHUvAGIegYO3asbYuRzHeg/EAhhBBCCCEKIjFYzBQ2Vy1ZIeNfDgFFhK+wkUHwCzeaxXtbTiAES5WK7kpStmzZQDFYmGqhrregf19oGg/eiCDC0CsO3eeCUH6gEEIIIYQQBZEYLGZiFTqJRbJCxttagn5/MHr06JTHS9N2Intz5syJKh6ze/fuqOUSRf4QlAhI9p9xBkFBGvIeH3rooah9plH8rbfeagUu4tYrMIlIEqmkcmis7yaZ7zwdhX2EEEIIIYQIExKDOSIeWc61lhg1alRUO4VUWLx4sbV6ekEI+ltWJGpWX7duXSsoEV1t2rQJbEmB0CPvsXLlylH7THXUWPtDNBIhmGoxmMIW9hHRSEwLIYQQQoQPicEcwVUTJTK4ZMmShMsTUXPCzuX11atXzyxfvjxK2AVZP10PP2/LClpaePHnCa5bt87eIxBmzpwZUxACgsIrLlauXFmg8fyuXbuilk8VFZlJDYlpIYQQQojwEZ34JUKLqyY6d+7cpKp3sgw3RByijTzA/fbbL2GED1q1amWtmtWqVYtECL3WTSyhQVE/InyIPCcIr7vuOmv99IM4rV27thUXCAt/ZdFjjjkm6nk6CsL416EiM4VDYloIIYQQInwoMpgjfPzxxymvY9GiRXHfr1q1qjnkkEPM5MmT4/YYDBKUiE0XOSJi5OyviAav2CMfMShi6HIDEaJEM12ksVu3btaSmKpNUUVmUkMVW4UQQgghwofEYI7gr9pJA3eEFS0ZDjjgAFO9enWz7777mtWrV8fs4UdeXyzrJrCOeO8ngz9i5BcR/sI0DqKY7OMLL7wQNX4ik4g+oo6p2BQLW9hHRCMxLYQQQggRPiQGcwR/QRdslp9++mmB5byiyf/55s2bm2+++SamVdS1fkgFLKDOKgqIhmeffTamQPUSJESduJRNsWSRmBZCCCGECB/KGQwZCCkEXevWrSM5eEBBFy/uuX952jRUqVKlwHqJutHIPZmcwWSh9+DVV19t21N4BR12Tq+IcH0EHccee6y1g3KfCGdHVM6fEEIIIYQQhUORwRyp2ujtM9ihQ4eITc+//COPPFKoRvBBUCmUnoHYTlmXt7Kn33Y6fPhw065du7hROzfW6dOnW+E4ZswYKxK9eYDz5s2LKoxDRdH+/ftHPiubohBCCCGEEIVDYjBkxLJDevsMUmgl1vKxhFthoPoouX2JooguCpiouIizGH799deR597XoV+/frZ/ouOyyy6LsiXKpiiEEEIIIUThkE00ZBTWDpmsXdKfcxgE0cB4PQtjVTklekgLCW7YRgsTtXM215dffrlAdNLZX6+55hp781tnhRBCCCGEELFRZDDL8bdMIOevMHZI3v/ggw/iVgFFzPmbxsdqLRFU6KV8+fIxextSMObhhx8uUP0zaP+wldJ70IvX5uoFcejG4o06quG5EEIIIYQQySExGNIcwWRBeJHbF49kmtS7nn5An0FaVpQrV850797dvuYVfPF6FxIpJHrnxCxRQ/dZ9u+zzz4zFSpUiAjdolQFpTppUfsNCiGEEEIIkS9IDGY56WiZ4M/Zc5U+EXPJtHQACrtQDAZhNWLEiKj3iO4R8WNsK1eujFqnP6+QSKFX3GId9UJrC/de0NhZnuqjXhHph+1zU5RQCCGEEEKI2ChnMMtJR8sEomPe9g5wySWXBLZ0iMX3338feexvV+EE16xZswqs00+y4hMQl4wd6yitJrhfvnx5xBrr8hD79u1rcxFZxi8u1W9QCCGEEEKIYBQZLIY8v1SsikVpmRC0/RNPPLFA03jyD8knxMpJvt4bb7xh7rrrLmsDpU2FtwUFIq5WrVp22TvuuCOmddU7XqKA/lxFCr/4racvvPBCoEhk7EFVQhGg3qigdxne8+YYqt9gen+PQgghhBAid5AYzMI8Py9FaZkQtH1y8bzwHFHnxBr3w4YNsxZQLJ9BRVs2bNhgjjrqKFO7du2Y0Tc3Xhc9pD+gV1Qi/pyl1AkT//acFTSW8I1nnS2MeM4XkZTO36MQIvuZO3eu+ctf/mKmTZsWeW3FihVm8ODBZsGCBXZib+jQoaZRo0YlOk4hhBAlj8Rglub5pXv7Qb3+/Mt99NFH5rjjjisQQfSyceNGU7NmzajXyBNE+Hkb3VPExR/tw4bq8g6TaTofi3h9CwsjnvNFJJX071EIUXy8//77tucseeFeOEZ37NjRTJgwwUydOtX079/fvPPOOwkLjAkhhMhtJAYzQKIm6yWx/aCIGWLIuxyijuhfIojkkb/nBB83b2QvKKoIXHQEiTxv03laXCSK1rl9cT0MuafPoIt4tmjRIvI4XsQvX0RSSf8ehRDFw/PPP2/F3uWXX24mTZoUeX3p0qVm1apV5rLLLjN77723+dOf/mSeeOIJe8w76aST0rJtIo7+NIBM4Vohcc4IK7mwD7myH7mwD6D9yM992LNnT8rrkBjMAEXJ88vU9hFGCKZ27drZsTBr7ISRf5yjR49Oav3HH3+8FW98zhv9QxzSciIWToTEs2ciJCdOnBgzWuf9LPvlbK5eG2yyfQfTKZL4x2fs5FQWh+W0MBbXkv49CiHSB8e9oHZApUuXNh06dLBWfI6HXjG4bNkyc9hhh1kh6MDuz+vpEoNCCCHCicRgBihKnl+mtu8tqOIXRv5xxhODXGhUrlzZXmg4MeEXU0FFYGhoT1N67+fi2TP9FlV/tC5WE/p4xIr4+Qvo8LyoJBKx6aYwFteS/j0KIdLHzJkzTZ8+fQq83rVrV3PPPfcEfgbxWKZMmQLHBX/rn1SoX79+lNjMJG62vWHDhias5MI+5Mp+5MI+gPYjP/dh9+7dZs6cOSmtQ2Iwy0mlyAmfJVoXSxj51x3P4lOpUqVIURd/ZDEoP5B8FS5YgsYbz55JziDFD2JF64raZzEIfwEdnhdVNCUSsekmXyyuQoho2rZtaxYuXFiozyAEOd574TmTdUIIIfIbicEsJ5UiJ3zWL9K8wsi/7ipVqgTaj1yfQbesP7Lot4sCQjDWOOPZM8lFJAIZy9Lo/yxFachF9OYJBuUMZlpQJRKx6UZ5gEKIZDniiCPM6tWrze+//25Klfpfe2F6tpI7KIQQIr+RGMxykhUszPLSGoIIFXkjCCD/stWqVbP5JjSLRzxQeMULOSUQr4hM0Pb9wgRRGc9yydgYB/0Fgcdu1nq//faLK3b9+W9sh4iee+7NiSxOQYWIBW/OYCZRHqAQIlmOPPJI27KHVACKyLz22mtm06ZN9lwghBAiv5EYzHKSFSxE+VzOmotQ+T9bo0aNSLN2Xieq5uWEE04wM2bMsOuivUNQlDBo+wgRcu+c5RIxGc9yiVijIqmLJjImnlMBLxH+/Ld4OZHFKagQsZRzLy6Pu/IAhRCFgePsLbfcYsaOHWv7DD766KP2uCWEECK/kRjMcpIVLP4oH88//PDDqM/6l3EtIvz5iIgMlvVW6CS35IorrgjcPp/x96pKZLkMingmIwaTWU+ySFAJIXKRVq1a2b6tXhCAbsJQCCGEcPwveUBkLU6wzJo1y97HskBitfQ/x3pJxO6rr76y982aNSuwTKzCNLSP8IIQjLd9f8QwkeWysMtnej1CCCGEEELkG4oM5ghE+fzPyR30VsukeICLBO7cuTPyXpC9srAWynQsT2PkwqLcOSGEEEIIIYqGxGCOQCTPa+vkObkhXpYsWRLpReIvHOC3VxbWQpnp5TO9HiGEEEIIIfKNrLSJUhGzS5cupmnTpuaCCy4wq1atKrAMUa677rrL5kYQEaLgST5DROzCCy80jRs3ttE/ntNI3Yv3ebrtlVhSKeaCyOTe39NKCCGEEEIIkV1kXWRwx44dpn///ubmm282p512mhV5iJuXXnopajkS4RGNb731ltmyZYu55JJLbPlsrJHpZNeuXWb37t0FXqdXk9eaie0yFjRzL126dJGWZft79uxJuCwRsoEDB9rH9evXt/d8N2eeeaaNCPLdvP3225HP/v3vf7f3n376qWnZsqWt/ukdl7cgTLwxuGVdz0K+k9mzZ9vXaXURtKw3ZxFR733u/X7YN/YxaFk/RV2Wv23Q37ewy7LNvffeO/I80Xr5nly/r0wty3fgzyX1wnjdmFmWW6zfpn/Zwqw32WX5jfFbK8qy/t+O9/8z0XqLuqx3e6ksy9jdbyyTx4h0Luv/X+Y3GW/M8f7v4y0rch8mDjl/xMpfF0IIkWdikEIpBx54oOncubN93q9fPzNhwoSIoHHQJ4l+SSzL7c9//rOZMmVK2sXgfffdF3jxTc8mWjE4Xn755ZgX6QcddJBp165d5PnUqVNjXjhVrFjRnHzyyZHnb7zxRsxG8OXLl7eC2fHOO++Yn3/+2bzyyiuR19z3SDXQNWvW2Bu8++679ntzn/daLbkYO/vssyPP6d23cePGwDFwcd61a9dI5brzzjsvEoEMyt/r1q1b5DEnfzceL278RIfdxTQW2JUrV5pYnHXWWZEy6eRCxss/7NixoylbtmykDQe9AWNx6qmnmgoVKtjHX3/9tVmwYEHMZU888US7DCxcuNDMmzcv5rJt27Y1VatWLWDfDQLLb/Xq1e3jFStWmM8//zzmskRmDz30UPv422+/tf9PsWjevLk5/PDDI8syMeD97XghSu/+/9avXx+pVBtEo0aNzNFHH20f//jjjwWqGnph4sK142BSxzth4YffFZFv2LZtm3nzzTcLLOPGX6dOnUjrFCaYXn311Zjrpb9mixYtIkIl1ncAhxxySFQU3fXKDKIwx4jKlStHTSZk6hgxbdo0s3Xr1sBlOUYweeTgGEEvuiC8xwi+35kzZ9r9i3eMcHz00UeRtjJBcIyIJ0JFbuEmEovSHkgIIUQO2kSXL19ujjjiiKgLiZo1a5ply5ZFLcdz73K1a9cusIwoPpo0aVLSQxBCCBEyUmkPJIQQIgcjg8xw+y0iZcqUMdu3b496jee87uAzmchT+8tf/hKx3cWzgB111FEx1+G3XxVmWaIhydq6nFXO2USLul6/VSuZZbHzEt1gZpeIF7bTIKuPd71Ej7x2MRd1c+P3WjT9y/op6rL16tVL2iYab1nGzsSFi3KxrIvKJrJzsizRymSXJVqZzLJ8j+3bt0/azlmjRo3A345/WZbxtx6Jt17yepNZlt8Y0cpklyVaGeu347d+HnPMMTHX61821nfgXzbR/3KyyzJ2fmPut5PJY0S6lvX+LxMNJ9LNbzPRssn8f7Is/2fxouUidyDS7iKC7rkQQog8FoMIPL+oQ/g5W18s8cdjbE7phosjr30rHXkuhVnWe3GWCHfhmcz6C7PeZJYtSlXPoHYYscbvX7Yw642HV2Cksqx/m+labyrLIkaS/a2xbLLLF3a9yS6LGCnqsvF+O6msNxHpWDbo95qpY0SmluU3meyYC/P/KXIftQcSQoiSJevOylg/vbknzBBTTRQbqH858qfcDDr2Uv8yQgghhMhe1B5ICCFKlqzLGcRS9sMPP9giDhRQGDlypKlVq5YtCOGlU6dO1prIsojFp556yhYREUIIIYQQQggRQjHILOHo0aNt6wiEIVXqXKUxBCBV9oCeeuQXUdHu/PPPNz179oyqsCeEEEIIIYQQIkQ2UaDYw4svvljg9ddffz0q72TQoEH2JoQQQgghhBAi5JFBIYQQQgghhBCZR2JQCCGEEEIIIfIQiUEhhBBCCCGEyEMkBoUQQgghhBAiD5EYFEIIIYQQQog8RGJQCCGEEEIIIfIQiUEhhBBCCCGEyEOyss9gSbNnz57I4927d5uwjTtMY86V8Yd57KDxlxxhHnumxu9dl/d4LLKLkjpXhv1/Jlf2IVf2Ixf2AbQf+bkPu9Nwvtxrj860Bdi5c6eZN29eSQ9DCCHynkaNGpl99923pIchAtC5Ugghwn++lE1UCCGEEEIIIfIQRQYD+P33381vv/1mH5cqVcrstddeJT0kIYTIGzgtcRyGffbZxx6HRfahc6UQQoT/fCkxKIQQQgghhBB5iKZbhRBCCCGEECIPkRgUQgghhBBCiDxEYlAIIYQQQggh8hCJQSGEEEIIIYTIQyQGhRBCCCGEECIPkRgUQgghhBBCiDxEYlAIIYQQQggh8hCJQSGEEEIIIYTIQyQGhRBCCCGEECIPkRgUQgghhBBCiDxEYlAIIYQQQggh8hCJQSGEEEIIIYTIQyQGhRBCCCGEECIPkRgUQgghhBBCiDxEYlAIIYQQQggh8hCJQSGEEEIIIYTIQyQGc4zx48ebm2++2YSNd955x3Tq1Mkcd9xx5txzzzVffPGFCRNvvfWWOeOMM8yxxx5r/vSnP5n//ve/JmwsWbLENGrUyHz77bcmTAwbNsyOm++eW6tWrUxYWLVqlbn44ovtuE8//XTzwQcfmLAwderUyHfubkcffbR59dVXS3poIg9YuHChueCCC+w5o0OHDmby5MkmjCxevNhceOGFpnnz5ubkk082zz//vAkrc+fONaeddpoJE19++aXp0qWLadq0qf09cUwOM2G9BsyF68BQXw/uETnBb7/9tmf06NF76tWrt2fQoEF7wsSqVav2HHfccXs++eSTPbt3794zZcqUPS1bttzz008/7QkDq1ev3tO0adM9c+bMsc9feOGFPW3btt0TJnbt2rWnW7due+rWrWv3J0z07t17z7Rp0/aEDX7rnTt33vPoo4/axx9++KH9HW3btm1PGJkwYcKeHj167Nm5c2dJD0XkOL///vueU089dc+TTz5pHy9atGhPkyZN9ixfvnxP2DjttNP2PPbYY/YcvmDBAnvu+/zzz/eEjffee8+OvX379nvCwq+//rrn+OOP3/Pqq6/u2bFjx54RI0bs6dq1654wEuZrwFy4Dgz79aAigznCoEGD7CxK9+7dTdhYt26dOe+880zLli1NqVKlzNlnn21fX7FihQkDhx56qPnoo49MkyZNzM6dO83mzZvNgQceaMLE6NGjTbNmzUwY+eabb0y9evVM2GC28NdffzV9+/a1v/sTTzzRTJo0yey9994mbBBNHjFihLn33ntN6dKlS3o4IsfZa6+9zCuvvGL+/Oc/2+ccc/fZZx9TpkwZEyY2bdpkDjvsMHPJJZfY/3uOY5wHiVaFCaKZw4cPN5dffrkJE7NmzbLn6s6dO5t9993X9OvXz6xevdq6ZMJGmK8Bc+E6MOzXg/uU9ABEerjppptM1apV7QXZmjVrTJjgn5+bY86cOWb79u3m8MMPN2GhbNmy1raE3YSTOuIqTGLqzTffNC+88IK1mISJ9evX24Pt3XffbcVVrVq1zODBg63lJwzfe506dcxtt91m3n77bVO9enVzyy23mP3228+EjX/+85+mR48e9vsXIl389ttv5pdffinwOhMO+++/v338xz/+0fz444/mmmuuMdWqVTNh2o8//OEPZsyYMZHnP/30k72g538pTH8LbLrdunUzn332mZ3QCgvLly83RxxxROQ55+6aNWuaZcuWmSOPPNKEiTBfA+bKdWCYrwclBnMEDgK5wMqVK+1J/dprrzUHHHCACROcVMiZIJeKfcD/XqlSJZPNMHP117/+1dxxxx32wiRsMLPOCeSKK64wDRs2NFOmTDFXXnml+fe//20qVqxospktW7aYDz/80IrBW2+91UybNs3079/f5huEYSbRsXbtWjN9+nT7excincycOdP06dOnwOtdu3Y199xzj338/vvvm0WLFpnLLrvMzsYTYQ/jfnDhy/9/48aNzfHHH2/CuA9hA3HrP+8RXeZvETZy5Row7NeBYb0elBgUWQP/OFzIk8TNiT1sOHscCcNPPPGEnSWlKEg288gjj1gxFVaLKAVLJkyYEHmORebJJ580s2fPtsUYsv33UqNGDXP++efb51iVmEFk7My0h4XXXnvNXoBXrly5pIcicoy2bdvaGfZ4EEmngNRZZ51lhWE2isFE+/HDDz/Ycx8TWP/617+sDTaMf4uwgfDDqu8FIUhkR5QMYb8ODOv1oHIGRVYwY8YMmzdxww032FmUMMGMKWP3R9zKly9vsh2iUNhDqWTHDfDqh6UiJJaqZ555psB3HwarZe3atc3PP/8c9drvv/9OUS8TJohuhq2CoAg32CmZ7MEi7v2/L1eunAkbWPrIlSJfcOTIkaF0aIQVojfenLTdu3fbaqIcm0XxE+brwLBfD0oMihKHg/GAAQNs3lcYk585iX/99dfm9ddft3kVTz31lD2pUFY428FOiaD6/PPP7Q2wNTDLHgZI+qdwAWPnuycqyIG3RYsWJttp06aNzSdg1hARiMV148aNoWqNwbj57WPPE6K4QPQdfPDBNj9q165d9hjGxNaZZ55pwgRjJwrSvn17M3ToUFsERxQfHGuJylKMiPMGYpy8Z3K5RfES9uvAsF8PSgyKEue5556z1gx643h7ljlxku3gA3/00UfN2LFjTevWrW3uF0UBNMObebCH3X777TbvkcjmG2+8YUaNGhWKyCAFMBCv7777rhWvjz32mLXthilHgpxN8m5yKV9FhIP777/fVn6kgAzHACrZ1q1b14QJqg7SZxB3hvfcxzFMZB7O0VjzJ06caIUhUR1suqL4Cft1YNivB/eiv0RJD0IIIYQQQgghRPGiyKAQQgghhBBC5CESg0IIIYQQQgiRh0gMCiGEEEIIIUQeIjEohBBCCCGEEHmIxKAQQgghhBBC5CESg0IIIYQQQgiRh0gMCiGEEEIIIUQeIjEohBBCCCGEEHmIxKAQQoi0sGPHDtOpUyfz4YcfJv2Zf/7zn+boo4+Out11110ZHacQQggh/sc+/3cvhAg5e/bsMevWrTM1atSwz7/99ltz6KGHlvSwRJ6wfft2c/3115slS5YU6nOLFy82l112mbnkkksir5UpUyYDIxRCCCGEH0UGhchyiJQ0bdrUHHvssZFb8+bN7Xu33XabGTVqlH08bNgw8+KLL9rHGzduNJ07dy7S9i688ELz0ksvpXEPRK7z1VdfmW7dupnvvvuu0J9dunSpadiwoalSpUrkdsABB2RknEKI3IJJz1xm165d5vvvvy/pYYgcR2JQiBDw2muvmf/+97+R2+eff25f//vf/2769u1rH2/evDmy/K+//mojNUIUB//5z3/MSSedZJ599tlAsUfUr3Hjxubkk082I0eONLt377bv7dy506xevdrUrl27BEYthIjH5ZdfHpmArF+/vmnUqFHk+dSpU82IESPMzTffXGLjmz9/vrn00ksjz7Gou3NjUejQoYP55JNPAt/7+eef7YQry7D/p59+up2IRaxlkhtuuMF8/PHH9jHfOX8T4Hvn+xciHcgmKkSI4YRwyCGHmKpVq5pXX33V7LXXXmbTpk1m+vTp9n1OWv/+979tpOWee+6xr++7776mV69ekZPK8uXLzV//+lezcOFCc/zxx5tffvmlhPdKhI0+ffrEzCHkd3bGGWfYKPbatWvtPb9TJjGWLVtmheEzzzxjPvroI7P//vubP/3pT1Y8liqluUohSpLHHnssyjHStWtXc+6550ZeK2kx8tNPP5nffvst8vz111/PyHY4jnHOJO1i/PjxplatWtbePmTIEDN79mwzevRoe0zLBN5J3rPPPtvehEg3OtsKkQP06NHDnHXWWebKK6+0F9tPPfWUfZ0oYrVq1awQ/PHHH81bb71lJk6caO2kb7zxhl3m2muvNa1atTKffvqpne3E8idEOmCCAoE3aNAgG/1jsoHHXFABYpCLqIMPPtheUCEqiRx6L0KFENnLhg0b7P9ts2bNrFWcSL/jiSeesG6AP/7xj2bw4MFm27ZtEUfAvffea0444QR7TLj11ltt5M1NcBIN471+/fpFjiNMKLVs2dJcffXV5ocffrCTlmyXCSaXNuGN7BE15LzIhCjnRhcxXLFihZ2gYv28xzbctmPBOZMx/+tf/zKHHXaYPWbVrVvXRga//PJLM23atALb96dczJ071/z5z382rVu3tt8VE7DOIcHnxowZY+85FyMyXXEtxn377bebsWPH2nWxTj98r3yHfJft27ePOn4yGXzaaafZ7+788883X3/9dZH+ziK3kRgUIgQwG8gJz93efffdQhWWmTJlirnxxhtthJDZzd69e9sTy6pVq+wFef/+/U3p0qXtSbNBgwYZ3ReRP2ARJfLszXfld8hsNxHsM88801pMucAjN5bIw1VXXWUjhUKI7Oezzz6zk5CzZs2yDhUmdZyAmzRpkp34eeedd6xgQQDCgw8+aEUO56A333zTrF+/PqqCMAKL1Ijhw4ebL774wtx9993mgQceMDNmzLAF0phQYpIJgcRzvzUU4YbzANso40PwcYzhdUQTYoyKx4xr5cqVdlvxeO+996yg4hzppXz58lbAOSdOPK677jorlvmeXnnlFbvOmTNnRt7HCsrrCE/O10QcKcjF+f6OO+6I6b6AWJO9iE1E+EMPPWRFatu2bc19992XcKwi/5BNVIgQQK5AUSuDcpLA5sKJyPH777/b9VFo5sADD7TWUYerRipEqmDh4sJr6NChBd4rV66cva9YsWLU63Xq1LHRBiYxMmW9EkKkByJsLjLXrl27iOMEYUOVYCyVTgx16dLFRr0Qigg8xCMg7pjwdMcJIlyclwBhdN5550UmKRFIHFM4d8UCAbn33nubiy66yD5n0onzGscT8v4OOuggm1dPYRa2E29dwPs4bIJgH5Jx0xAl5bvA2kpks0KFClHbJWqHuORWr149O1F73HHHJT3ZyzUCk73c3GQv+122bFnz3HPP2Yk2RDuTbUL4kRgUIsfhZMeMJrOGlStXtq8RlWGWlBtRGk6Mf/jDH+x7XIgLkQ6OOOIIa1OqXr16ZMLh/ffftzPxRAmwWWGx8lavxd6FpVRCUIjsB/Hi2GeffSLWR9oc/eMf/4hEA90kJEKICUrvpCN57xRi4XVArDlYD2Ln6aefjtoO9tBYsA0nNB1U5AZy/a644gp73qMoDuIMQRUPxhOroifnS/+EVhBE+ih2w/dD9WT217vdSpUqRe0f31Wqk72AZZTcTgQiwnDgwIFWlAvhRTZRIXIELrZd7oO78OY5M6QdO3a09hDyLLZs2WIGDBhgL8Rr1qxpZ1zJTUAYvv3229aiI0Q6YLafC55bbrnF9h/EEkpOKxclFIghkrBo0SKbi8NMOLPbWL+YwRZChBcEFNZPLJzcsETy/82EJDnCXjFHewgmLImWgXciiPVg8XTr4fbyyy/HTWcgiof11AvWVLZJhJK8RApWcazhHJgI8vCYTHWVQ5mwwhrK+ZXJLY5jbtxODHuLv9Byh+Peo48+au2hDz/8cMQZkc7JXvf9kEbCvnG+ZwxsD3sqEVhso1u3bk3LtkXuIDEoRI5ATgO5AjfddJPt1XbiiSfaGzOhnIjg1FNPtcsxE8tywIU4JzcSzJl9JdlfiHSA6GNmmgszqhAyK02RIsQhYIeiYAz5O/TFJLeFnEJyV4UQ4YX/Yf73iewhophwvOaaayKTRPyvc1xAmBA9pNCMN13BwbLkHpJ/TMRrwoQJtrIn62R5Wih5BRg0adLECjNyj3kPd8LkyZOtACOKRr4hk1QIOvIQvRVJg6BoC84ZhCQ5hqwDoUtRG2ztTLYCxWXYFutGkDFmQDQyHtbBeBgL1bsTbdc/yRtEvMletkXuJPvIeohgMob99tsv4XZFfiGbqBBZDieNeInj3twNbyK9vyIjlp0gsOuQdC5EJn6vCD4u4GLhJi2EELkDtkXSERBuCJRjjjnG2hVdWxmECxNECCuEoJsg8sPkJAXOyHXDkon4okhNmTJlbNEpzl8tWrSwE0oOhA+TTBReuf/++61IIyqHGGQ7RBoRYq5olRNtsUA8UaGb8V988cU22oYNlYghEUZEJ4KRfEYmXslp5Hx8yimn2M8feeSR1qbJd8L+U0gLIZlou0ARnDvvvNNaX9mPINgmOZhM9rJfjIvJXr4jRCLvEZ0kIsvkr8Sg8LPXnkRmaSGEEEIIIUQU5BKSWoHjRoiwIjEohBBCCCGEEHmIcgaFEEIIIYQQIg+RGBRCCCGEEEKIPERiUAghhBBCCCHyEIlBIYQQQgghhMhDJAaFEEIIIYQQIg+RGBRCCCGEEEKIPERiUAghhBBCCCHyEIlBIYQQQgghhMhDJAaFEEIIIYQQIg+RGBRCCCGEEEKIPERiUAghhBBCCCHyEIlBIYQQQgghhMhDJAaFEEIIIYQQIg+RGBRCCCGEEEIIk3/8P0nZ/+WjqkBTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1050x1050 with 4 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.plot_diagnostics(\"train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6xmgQsGtlS2U"
   },
   "source": [
    "# Section 4: Causal Inference\n",
    "\n",
    "\n",
    "\n",
    "In this section, we use the TableMage package to estimate the causal effect of education on wage using a labor market dataset.\n",
    "\n",
    "First, we load the datsaset, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "gAztqx2hlS2U",
    "outputId": "047ef3c3-b0be-42dc-c5b5-b7cfe4115162"
   },
   "outputs": [],
   "source": [
    "data_path = curr_dir.parent / \"demo\" / \"causal\" / \"data\" / \"card.csv\"\n",
    "df_causal = pd.read_csv(data_path, index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ad-wI6GIlS2U"
   },
   "source": [
    "Next, we initialize an Analyzer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "Q4T3bygQlS2U",
    "outputId": "76659ba5-8ef8-4cce-b75c-e14bbe6860fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mUPDT: \u001b[0mAnalyzer initialized for dataset \u001b[93m'Labor Market Behavior'\u001b[0m. \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mLabor Market Behavior\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTrain shape: \u001b[0m\u001b[93m(2408, 34)\u001b[0m                      \u001b[1mTest shape: \u001b[0m\u001b[93m(602, 34)\u001b[0m                      \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mCategorical variables:\u001b[0m\n",
       "  \u001b[93mNone\u001b[0m \n",
       "                                                                                        \n",
       "\u001b[1mNumeric variables:\u001b[0m\n",
       "  \u001b[95m'IQ', \u001b[0m\u001b[95m'KWW', \u001b[0m\u001b[95m'age', \u001b[0m\u001b[95m'black', \u001b[0m\u001b[95m'educ', \u001b[0m\u001b[95m'educ_binary', \u001b[0m\u001b[95m'enroll', \u001b[0m\u001b[95m'exper', \u001b[0m\u001b[95m'expersq', \n",
       "  \u001b[0m\u001b[95m'fatheduc', \u001b[0m\u001b[95m'libcrd14', \u001b[0m\u001b[95m'lwage', \u001b[0m\u001b[95m'married', \u001b[0m\u001b[95m'momdad14', \u001b[0m\u001b[95m'motheduc', \u001b[0m\u001b[95m'nearc2', \n",
       "  \u001b[0m\u001b[95m'nearc4', \u001b[0m\u001b[95m'reg661', \u001b[0m\u001b[95m'reg662', \u001b[0m\u001b[95m'reg663', \u001b[0m\u001b[95m'reg664', \u001b[0m\u001b[95m'reg665', \u001b[0m\u001b[95m'reg666', \u001b[0m\u001b[95m'reg667', \n",
       "  \u001b[0m\u001b[95m'reg668', \u001b[0m\u001b[95m'reg669', \u001b[0m\u001b[95m'sinmom14', \u001b[0m\u001b[95m'smsa', \u001b[0m\u001b[95m'smsa66', \u001b[0m\u001b[95m'south', \u001b[0m\u001b[95m'south66', \u001b[0m\u001b[95m'step14', \n",
       "  \u001b[0m\u001b[95m'wage', \u001b[0m\u001b[95m'weight'\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer = tm.Analyzer(\n",
    "    df_causal, test_size=0.2, split_seed=42, verbose=True, name=\"Labor Market Behavior\"\n",
    ")\n",
    "analyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fDtEtSHylS2U"
   },
   "source": [
    "We can now make a basic causal inference model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "KcIl0HxrlS2U"
   },
   "outputs": [],
   "source": [
    "causal_model = analyzer.causal(\n",
    "    treatment=\"educ_binary\",\n",
    "    outcome=\"lwage\",\n",
    "    confounders=[\n",
    "        \"exper\",\n",
    "        \"expersq\",\n",
    "        \"black\",\n",
    "        \"smsa\",\n",
    "        \"south\",\n",
    "        \"smsa66\",\n",
    "        \"reg662\",\n",
    "        \"reg663\",\n",
    "        \"reg664\",\n",
    "        \"reg665\",\n",
    "        \"reg666\",\n",
    "        \"reg667\",\n",
    "        \"reg668\",\n",
    "        \"reg669\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bYUJ_S6slS2U"
   },
   "source": [
    "We can compute the difference in means naively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "rxqMG7gelS2U"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mCausal Effect Estimation Report\u001b[0m\n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mEstimate:\u001b[0m \u001b[93m0.194\u001b[0m                             \u001b[1mStd Err:\u001b[0m \u001b[93m0.016\u001b[0m\n",
       "\u001b[1mEstimand:\u001b[0m \u001b[94mAvg Trmt Effect (ATE)\u001b[0m             \u001b[1mp-value:\u001b[0m \u001b[92m0.000e+00\u001b[0m\n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTreatment variable:\n",
       "\u001b[0m\u001b[95m  'educ_binary'\u001b[0m\n",
       "                                                                                        \n",
       "\u001b[1mOutcome variable:\n",
       "\u001b[0m\u001b[95m  'lwage'\u001b[0m\n",
       "                                                                                        \n",
       "\u001b[1mConfounders:\n",
       "\u001b[0m  \u001b[95m'exper', \u001b[0m\u001b[95m'expersq', \u001b[0m\u001b[95m'black', \u001b[0m\u001b[95m'smsa', \u001b[0m\u001b[95m'south', \u001b[0m\u001b[95m'smsa66', \u001b[0m\u001b[95m'reg662', \u001b[0m\u001b[95m'reg663', \u001b[0m\u001b[95m'reg664', \n",
       "  \u001b[0m\u001b[95m'reg665', \u001b[0m\u001b[95m'reg666', \u001b[0m\u001b[95m'reg667', \u001b[0m\u001b[95m'reg668', \u001b[0m\u001b[95m'reg669'\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mMethod:\n",
       "\u001b[0m  \u001b[94mNaive Estimator (Difference in Means)\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = causal_model.estimate_ate(method=\"naive\")\n",
    "report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jg131ggklS2U"
   },
   "source": [
    "With the introduction of these cofounders, we should estimate the ATE with a method that takes confounding into account. Let's use outcome regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "LLH5JH5ylS2U",
    "outputId": "a91ef67e-c545-4f40-e6f0-424c88b2bdb9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mCausal Effect Estimation Report\u001b[0m\n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mEstimate:\u001b[0m \u001b[93m0.238\u001b[0m                             \u001b[1mStd Err:\u001b[0m \u001b[93m0.017\u001b[0m\n",
       "\u001b[1mEstimand:\u001b[0m \u001b[94mAvg Trmt Effect (ATE)\u001b[0m             \u001b[1mp-value:\u001b[0m \u001b[92m5.107e-42\u001b[0m\n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTreatment variable:\n",
       "\u001b[0m\u001b[95m  'educ_binary'\u001b[0m\n",
       "                                                                                        \n",
       "\u001b[1mOutcome variable:\n",
       "\u001b[0m\u001b[95m  'lwage'\u001b[0m\n",
       "                                                                                        \n",
       "\u001b[1mConfounders:\n",
       "\u001b[0m  \u001b[95m'exper', \u001b[0m\u001b[95m'expersq', \u001b[0m\u001b[95m'black', \u001b[0m\u001b[95m'smsa', \u001b[0m\u001b[95m'south', \u001b[0m\u001b[95m'smsa66', \u001b[0m\u001b[95m'reg662', \u001b[0m\u001b[95m'reg663', \u001b[0m\u001b[95m'reg664', \n",
       "  \u001b[0m\u001b[95m'reg665', \u001b[0m\u001b[95m'reg666', \u001b[0m\u001b[95m'reg667', \u001b[0m\u001b[95m'reg668', \u001b[0m\u001b[95m'reg669'\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mMethod:\n",
       "\u001b[0m  \u001b[94mOutcome Regression\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = causal_model.estimate_ate(method=\"outcome_regression\", robust_se=\"nonrobust\")\n",
    "report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lzQKYflilS2U"
   },
   "source": [
    "Here's another example where we apply the IPW (inverse probability weighting) estimator to estimate the ATT (average treatment effect on the treated)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "u3ndjd1ulS2V"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mCausal Effect Estimation Report\u001b[0m\n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mEstimate:\u001b[0m \u001b[93m0.232\u001b[0m                             \u001b[1mStd Err:\u001b[0m \u001b[93m0.020\u001b[0m\n",
       "\u001b[1mEstimand:\u001b[0m \u001b[94mAvg Trmt Effect on Trtd (ATT)\u001b[0m     \u001b[1mp-value:\u001b[0m \u001b[92m1.975e-31\u001b[0m\n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTreatment variable:\n",
       "\u001b[0m\u001b[95m  'educ_binary'\u001b[0m\n",
       "                                                                                        \n",
       "\u001b[1mOutcome variable:\n",
       "\u001b[0m\u001b[95m  'lwage'\u001b[0m\n",
       "                                                                                        \n",
       "\u001b[1mConfounders:\n",
       "\u001b[0m  \u001b[95m'exper', \u001b[0m\u001b[95m'expersq', \u001b[0m\u001b[95m'black', \u001b[0m\u001b[95m'smsa', \u001b[0m\u001b[95m'south', \u001b[0m\u001b[95m'smsa66', \u001b[0m\u001b[95m'reg662', \u001b[0m\u001b[95m'reg663', \u001b[0m\u001b[95m'reg664', \n",
       "  \u001b[0m\u001b[95m'reg665', \u001b[0m\u001b[95m'reg666', \u001b[0m\u001b[95m'reg667', \u001b[0m\u001b[95m'reg668', \u001b[0m\u001b[95m'reg669'\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mMethod:\n",
       "\u001b[0m  \u001b[94mInverse Probability Weighting (IPW) Weighted Regression\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = causal_model.estimate_att(method=\"ipw_weighted_regression\", robust_se=\"HC0\")\n",
    "report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jK0OtjQilS2V"
   },
   "source": [
    "# Section 5: Machine Learning\n",
    "\n",
    "Now, let's work through how to do machine learning model benchmarking using TableMage.\n",
    "\n",
    "To begin, let's use the house price data once again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "HeVRZfpjlS2V",
    "outputId": "6f07e2d8-bbe5-4f28-f71a-1d29bc068ac7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mUPDT: \u001b[0mAnalyzer initialized for dataset \u001b[93m'House Prices'\u001b[0m. \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mHouse Prices\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTrain shape: \u001b[0m\u001b[93m(1168, 80)\u001b[0m                      \u001b[1mTest shape: \u001b[0m\u001b[93m(292, 80)\u001b[0m                      \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mCategorical variables:\u001b[0m\n",
       "  \u001b[95m'Alley', \u001b[0m\u001b[95m'BldgType', \u001b[0m\u001b[95m'BsmtCond', \u001b[0m\u001b[95m'BsmtExposure', \u001b[0m\u001b[95m'BsmtFinType1', \u001b[0m\u001b[95m'BsmtFinType2', \n",
       "  \u001b[0m\u001b[95m'BsmtQual', \u001b[0m\u001b[95m'CentralAir', \u001b[0m\u001b[95m'Condition1', \u001b[0m\u001b[95m'Condition2', \u001b[0m\u001b[95m'Electrical', \u001b[0m\u001b[95m'ExterCond', \n",
       "  \u001b[0m\u001b[95m'ExterQual', \u001b[0m\u001b[95m'Exterior1st', \u001b[0m\u001b[95m'Exterior2nd', \u001b[0m\u001b[95m'Fence', \u001b[0m\u001b[95m'FireplaceQu', \u001b[0m\u001b[95m'Foundation', \n",
       "  \u001b[0m\u001b[95m'Functional', \u001b[0m\u001b[95m'GarageCond', \u001b[0m\u001b[95m'GarageFinish', \u001b[0m\u001b[95m'GarageQual', \u001b[0m\u001b[95m'GarageType', \u001b[0m\u001b[95m'Heating', \n",
       "  \u001b[0m\u001b[95m'HeatingQC', \u001b[0m\u001b[95m'HouseStyle', \u001b[0m\u001b[95m'KitchenQual', \u001b[0m\u001b[95m'LandContour', \u001b[0m\u001b[95m'LandSlope', \u001b[0m\u001b[95m'LotConfig', \n",
       "  \u001b[0m\u001b[95m'LotShape', \u001b[0m\u001b[95m'MSZoning', \u001b[0m\u001b[95m'MasVnrType', \u001b[0m\u001b[95m'MiscFeature', \u001b[0m\u001b[95m'Neighborhood', \u001b[0m\u001b[95m'PavedDrive', \n",
       "  \u001b[0m\u001b[95m'PoolQC', \u001b[0m\u001b[95m'RoofMatl', \u001b[0m\u001b[95m'RoofStyle', \u001b[0m\u001b[95m'SaleCondition', \u001b[0m\u001b[95m'SaleType', \u001b[0m\u001b[95m'Street', \u001b[0m\u001b[95m'Utilities'\u001b[0m \n",
       "                                                                                        \n",
       "\u001b[1mNumeric variables:\u001b[0m\n",
       "  \u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'3SsnPorch', \u001b[0m\u001b[95m'BedroomAbvGr', \u001b[0m\u001b[95m'BsmtFinSF1', \u001b[0m\u001b[95m'BsmtFinSF2', \n",
       "  \u001b[0m\u001b[95m'BsmtFullBath', \u001b[0m\u001b[95m'BsmtHalfBath', \u001b[0m\u001b[95m'BsmtUnfSF', \u001b[0m\u001b[95m'EnclosedPorch', \u001b[0m\u001b[95m'Fireplaces', \n",
       "  \u001b[0m\u001b[95m'FullBath', \u001b[0m\u001b[95m'GarageArea', \u001b[0m\u001b[95m'GarageCars', \u001b[0m\u001b[95m'GarageYrBlt', \u001b[0m\u001b[95m'GrLivArea', \u001b[0m\u001b[95m'HalfBath', \n",
       "  \u001b[0m\u001b[95m'KitchenAbvGr', \u001b[0m\u001b[95m'LotArea', \u001b[0m\u001b[95m'LotFrontage', \u001b[0m\u001b[95m'LowQualFinSF', \u001b[0m\u001b[95m'MSSubClass', \u001b[0m\u001b[95m'MasVnrArea', \n",
       "  \u001b[0m\u001b[95m'MiscVal', \u001b[0m\u001b[95m'MoSold', \u001b[0m\u001b[95m'OpenPorchSF', \u001b[0m\u001b[95m'OverallCond', \u001b[0m\u001b[95m'OverallQual', \u001b[0m\u001b[95m'PoolArea', \n",
       "  \u001b[0m\u001b[95m'SalePrice', \u001b[0m\u001b[95m'ScreenPorch', \u001b[0m\u001b[95m'TotRmsAbvGrd', \u001b[0m\u001b[95m'TotalBsmtSF', \u001b[0m\u001b[95m'WoodDeckSF', \u001b[0m\u001b[95m'YearBuilt', \n",
       "  \u001b[0m\u001b[95m'YearRemodAdd', \u001b[0m\u001b[95m'YrSold'\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "\n",
    "data_path = curr_dir.parent / \"demo\" / \"regression\" / \"house_price_data\" / \"data.csv\"\n",
    "df = pd.read_csv(data_path, index_col=0)\n",
    "analyzer = tm.Analyzer(\n",
    "    df, test_size=0.2, split_seed=42, verbose=True, name=\"House Prices\"\n",
    ")\n",
    "analyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "an36oYmWTNRv"
   },
   "source": [
    "Feel free to perform any preprocessing steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "3iezDVKJTMxq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mUPDT: \u001b[0mDropped 0 rows with missing values from train and 0 rows from test. \n",
      "\u001b[92mUPDT: \u001b[0mDropped variables \u001b[95m'Alley', \u001b[0m\u001b[95m'Fence', \u001b[0m\u001b[95m'FireplaceQu', \u001b[0m\u001b[95m'MasVnrType', \u001b[0m\u001b[95m'MiscFeature', \n",
      "      \u001b[0m\u001b[95m'PoolQC'\u001b[0m with at least 30.0% of values missing. \n",
      "\u001b[93mNOTE: \u001b[0mNumeric variables \u001b[95m'ScreenPorch', \u001b[0m\u001b[95m'GrLivArea', \u001b[0m\u001b[95m'GarageCars', \u001b[0m\u001b[95m'2ndFlrSF', \n",
      "      \u001b[0m\u001b[95m'BsmtFinSF2', \u001b[0m\u001b[95m'PoolArea', \u001b[0m\u001b[95m'BsmtFullBath', \u001b[0m\u001b[95m'YearRemodAdd', \u001b[0m\u001b[95m'WoodDeckSF', \n",
      "      \u001b[0m\u001b[95m'BsmtUnfSF', \u001b[0m\u001b[95m'TotalBsmtSF', \u001b[0m\u001b[95m'MiscVal', \u001b[0m\u001b[95m'BsmtHalfBath', \u001b[0m\u001b[95m'OverallCond', \n",
      "      \u001b[0m\u001b[95m'BedroomAbvGr', \u001b[0m\u001b[95m'BsmtFinSF1', \u001b[0m\u001b[95m'MoSold', \u001b[0m\u001b[95m'OpenPorchSF', \u001b[0m\u001b[95m'TotRmsAbvGrd', \u001b[0m\u001b[95m'FullBath', \n",
      "      \u001b[0m\u001b[95m'YearBuilt', \u001b[0m\u001b[95m'GarageArea', \u001b[0m\u001b[95m'3SsnPorch', \u001b[0m\u001b[95m'OverallQual', \u001b[0m\u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'YrSold', \n",
      "      \u001b[0m\u001b[95m'LowQualFinSF', \u001b[0m\u001b[95m'Fireplaces', \u001b[0m\u001b[95m'HalfBath', \u001b[0m\u001b[95m'MSSubClass', \u001b[0m\u001b[95m'KitchenAbvGr', \u001b[0m\u001b[95m'LotArea', \n",
      "      \u001b[0m\u001b[95m'EnclosedPorch'\u001b[0m have no missing values. Imputer will consider all specified \n",
      "      variables regardless. \n",
      "\u001b[93mNOTE: \u001b[0mCategorical variables \u001b[95m'RoofMatl', \u001b[0m\u001b[95m'LotConfig', \u001b[0m\u001b[95m'LandContour', \u001b[0m\u001b[95m'Foundation', \n",
      "      \u001b[0m\u001b[95m'Exterior1st', \u001b[0m\u001b[95m'HeatingQC', \u001b[0m\u001b[95m'Condition1', \u001b[0m\u001b[95m'Neighborhood', \u001b[0m\u001b[95m'RoofStyle', \u001b[0m\u001b[95m'Street', \n",
      "      \u001b[0m\u001b[95m'SaleCondition', \u001b[0m\u001b[95m'Condition2', \u001b[0m\u001b[95m'HouseStyle', \u001b[0m\u001b[95m'Functional', \u001b[0m\u001b[95m'ExterQual', \n",
      "      \u001b[0m\u001b[95m'LandSlope', \u001b[0m\u001b[95m'SaleType', \u001b[0m\u001b[95m'LotShape', \u001b[0m\u001b[95m'BldgType', \u001b[0m\u001b[95m'Heating', \u001b[0m\u001b[95m'ExterCond', \n",
      "      \u001b[0m\u001b[95m'Exterior2nd', \u001b[0m\u001b[95m'PavedDrive', \u001b[0m\u001b[95m'Utilities', \u001b[0m\u001b[95m'CentralAir', \u001b[0m\u001b[95m'KitchenQual', \u001b[0m\u001b[95m'MSZoning'\u001b[0m \n",
      "      have no missing values. Imputer will consider all specified variables regardless. \n",
      "\u001b[92mUPDT: \u001b[0mImputed missing values with strategy \u001b[93m'5nn'\u001b[0m for numeric variables \u001b[95m'1stFlrSF', \n",
      "      \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'3SsnPorch', \u001b[0m\u001b[95m'BedroomAbvGr', \u001b[0m\u001b[95m'BsmtFinSF1', \u001b[0m\u001b[95m'BsmtFinSF2', \n",
      "      \u001b[0m\u001b[95m'BsmtFullBath', \u001b[0m\u001b[95m'BsmtHalfBath', \u001b[0m\u001b[95m'BsmtUnfSF', \u001b[0m\u001b[95m'EnclosedPorch', \u001b[0m\u001b[95m'Fireplaces', \n",
      "      \u001b[0m\u001b[95m'FullBath', \u001b[0m\u001b[95m'GarageArea', \u001b[0m\u001b[95m'GarageCars', \u001b[0m\u001b[95m'GarageYrBlt', \u001b[0m\u001b[95m'GrLivArea', \u001b[0m\u001b[95m'HalfBath', \n",
      "      \u001b[0m\u001b[95m'KitchenAbvGr', \u001b[0m\u001b[95m'LotArea', \u001b[0m\u001b[95m'LotFrontage', \u001b[0m\u001b[95m'LowQualFinSF', \u001b[0m\u001b[95m'MSSubClass', \n",
      "      \u001b[0m\u001b[95m'MasVnrArea', \u001b[0m\u001b[95m'MiscVal', \u001b[0m\u001b[95m'MoSold', \u001b[0m\u001b[95m'OpenPorchSF', \u001b[0m\u001b[95m'OverallCond', \u001b[0m\u001b[95m'OverallQual', \n",
      "      \u001b[0m\u001b[95m'PoolArea', \u001b[0m\u001b[95m'ScreenPorch', \u001b[0m\u001b[95m'TotRmsAbvGrd', \u001b[0m\u001b[95m'TotalBsmtSF', \u001b[0m\u001b[95m'WoodDeckSF', \n",
      "      \u001b[0m\u001b[95m'YearBuilt', \u001b[0m\u001b[95m'YearRemodAdd', \u001b[0m\u001b[95m'YrSold'\u001b[0m and strategy \u001b[93m'most_frequent'\u001b[0m for categorical \n",
      "      variables \u001b[95m'BldgType', \u001b[0m\u001b[95m'BsmtCond', \u001b[0m\u001b[95m'BsmtExposure', \u001b[0m\u001b[95m'BsmtFinType1', \u001b[0m\u001b[95m'BsmtFinType2', \n",
      "      \u001b[0m\u001b[95m'BsmtQual', \u001b[0m\u001b[95m'CentralAir', \u001b[0m\u001b[95m'Condition1', \u001b[0m\u001b[95m'Condition2', \u001b[0m\u001b[95m'Electrical', \u001b[0m\u001b[95m'ExterCond', \n",
      "      \u001b[0m\u001b[95m'ExterQual', \u001b[0m\u001b[95m'Exterior1st', \u001b[0m\u001b[95m'Exterior2nd', \u001b[0m\u001b[95m'Foundation', \u001b[0m\u001b[95m'Functional', \n",
      "      \u001b[0m\u001b[95m'GarageCond', \u001b[0m\u001b[95m'GarageFinish', \u001b[0m\u001b[95m'GarageQual', \u001b[0m\u001b[95m'GarageType', \u001b[0m\u001b[95m'Heating', \u001b[0m\u001b[95m'HeatingQC', \n",
      "      \u001b[0m\u001b[95m'HouseStyle', \u001b[0m\u001b[95m'KitchenQual', \u001b[0m\u001b[95m'LandContour', \u001b[0m\u001b[95m'LandSlope', \u001b[0m\u001b[95m'LotConfig', \u001b[0m\u001b[95m'LotShape', \n",
      "      \u001b[0m\u001b[95m'MSZoning', \u001b[0m\u001b[95m'Neighborhood', \u001b[0m\u001b[95m'PavedDrive', \u001b[0m\u001b[95m'RoofMatl', \u001b[0m\u001b[95m'RoofStyle', \n",
      "      \u001b[0m\u001b[95m'SaleCondition', \u001b[0m\u001b[95m'SaleType', \u001b[0m\u001b[95m'Street', \u001b[0m\u001b[95m'Utilities'\u001b[0m. \n",
      "\u001b[92mUPDT: \u001b[0mScaled variables \u001b[95m'GarageYrBlt', \u001b[0m\u001b[95m'ScreenPorch', \u001b[0m\u001b[95m'LotFrontage', \u001b[0m\u001b[95m'GrLivArea', \n",
      "      \u001b[0m\u001b[95m'GarageCars', \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'BsmtFinSF2', \u001b[0m\u001b[95m'PoolArea', \u001b[0m\u001b[95m'BsmtFullBath', \n",
      "      \u001b[0m\u001b[95m'YearRemodAdd', \u001b[0m\u001b[95m'WoodDeckSF', \u001b[0m\u001b[95m'BsmtUnfSF', \u001b[0m\u001b[95m'TotalBsmtSF', \u001b[0m\u001b[95m'MiscVal', \n",
      "      \u001b[0m\u001b[95m'BsmtHalfBath', \u001b[0m\u001b[95m'OverallCond', \u001b[0m\u001b[95m'BedroomAbvGr', \u001b[0m\u001b[95m'MasVnrArea', \u001b[0m\u001b[95m'BsmtFinSF1', \n",
      "      \u001b[0m\u001b[95m'MoSold', \u001b[0m\u001b[95m'OpenPorchSF', \u001b[0m\u001b[95m'TotRmsAbvGrd', \u001b[0m\u001b[95m'FullBath', \u001b[0m\u001b[95m'YearBuilt', \u001b[0m\u001b[95m'GarageArea', \n",
      "      \u001b[0m\u001b[95m'3SsnPorch', \u001b[0m\u001b[95m'OverallQual', \u001b[0m\u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'YrSold', \u001b[0m\u001b[95m'LowQualFinSF', \u001b[0m\u001b[95m'Fireplaces', \n",
      "      \u001b[0m\u001b[95m'HalfBath', \u001b[0m\u001b[95m'MSSubClass', \u001b[0m\u001b[95m'KitchenAbvGr', \u001b[0m\u001b[95m'LotArea', \u001b[0m\u001b[95m'EnclosedPorch'\u001b[0m using \n",
      "      strategy \u001b[93m'standardize'\u001b[0m. \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "========================================================================================\n",
       "\u001b[1mHouse Prices\u001b[0m \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mTrain shape: \u001b[0m\u001b[93m(1168, 74)\u001b[0m                      \u001b[1mTest shape: \u001b[0m\u001b[93m(292, 74)\u001b[0m                      \n",
       "----------------------------------------------------------------------------------------\n",
       "\u001b[1mCategorical variables:\u001b[0m\n",
       "  \u001b[95m'BldgType', \u001b[0m\u001b[95m'BsmtCond', \u001b[0m\u001b[95m'BsmtExposure', \u001b[0m\u001b[95m'BsmtFinType1', \u001b[0m\u001b[95m'BsmtFinType2', \u001b[0m\u001b[95m'BsmtQual', \n",
       "  \u001b[0m\u001b[95m'CentralAir', \u001b[0m\u001b[95m'Condition1', \u001b[0m\u001b[95m'Condition2', \u001b[0m\u001b[95m'Electrical', \u001b[0m\u001b[95m'ExterCond', \u001b[0m\u001b[95m'ExterQual', \n",
       "  \u001b[0m\u001b[95m'Exterior1st', \u001b[0m\u001b[95m'Exterior2nd', \u001b[0m\u001b[95m'Foundation', \u001b[0m\u001b[95m'Functional', \u001b[0m\u001b[95m'GarageCond', \n",
       "  \u001b[0m\u001b[95m'GarageFinish', \u001b[0m\u001b[95m'GarageQual', \u001b[0m\u001b[95m'GarageType', \u001b[0m\u001b[95m'Heating', \u001b[0m\u001b[95m'HeatingQC', \u001b[0m\u001b[95m'HouseStyle', \n",
       "  \u001b[0m\u001b[95m'KitchenQual', \u001b[0m\u001b[95m'LandContour', \u001b[0m\u001b[95m'LandSlope', \u001b[0m\u001b[95m'LotConfig', \u001b[0m\u001b[95m'LotShape', \u001b[0m\u001b[95m'MSZoning', \n",
       "  \u001b[0m\u001b[95m'Neighborhood', \u001b[0m\u001b[95m'PavedDrive', \u001b[0m\u001b[95m'RoofMatl', \u001b[0m\u001b[95m'RoofStyle', \u001b[0m\u001b[95m'SaleCondition', \u001b[0m\u001b[95m'SaleType', \n",
       "  \u001b[0m\u001b[95m'Street', \u001b[0m\u001b[95m'Utilities'\u001b[0m \n",
       "                                                                                        \n",
       "\u001b[1mNumeric variables:\u001b[0m\n",
       "  \u001b[95m'1stFlrSF', \u001b[0m\u001b[95m'2ndFlrSF', \u001b[0m\u001b[95m'3SsnPorch', \u001b[0m\u001b[95m'BedroomAbvGr', \u001b[0m\u001b[95m'BsmtFinSF1', \u001b[0m\u001b[95m'BsmtFinSF2', \n",
       "  \u001b[0m\u001b[95m'BsmtFullBath', \u001b[0m\u001b[95m'BsmtHalfBath', \u001b[0m\u001b[95m'BsmtUnfSF', \u001b[0m\u001b[95m'EnclosedPorch', \u001b[0m\u001b[95m'Fireplaces', \n",
       "  \u001b[0m\u001b[95m'FullBath', \u001b[0m\u001b[95m'GarageArea', \u001b[0m\u001b[95m'GarageCars', \u001b[0m\u001b[95m'GarageYrBlt', \u001b[0m\u001b[95m'GrLivArea', \u001b[0m\u001b[95m'HalfBath', \n",
       "  \u001b[0m\u001b[95m'KitchenAbvGr', \u001b[0m\u001b[95m'LotArea', \u001b[0m\u001b[95m'LotFrontage', \u001b[0m\u001b[95m'LowQualFinSF', \u001b[0m\u001b[95m'MSSubClass', \u001b[0m\u001b[95m'MasVnrArea', \n",
       "  \u001b[0m\u001b[95m'MiscVal', \u001b[0m\u001b[95m'MoSold', \u001b[0m\u001b[95m'OpenPorchSF', \u001b[0m\u001b[95m'OverallCond', \u001b[0m\u001b[95m'OverallQual', \u001b[0m\u001b[95m'PoolArea', \n",
       "  \u001b[0m\u001b[95m'SalePrice', \u001b[0m\u001b[95m'ScreenPorch', \u001b[0m\u001b[95m'TotRmsAbvGrd', \u001b[0m\u001b[95m'TotalBsmtSF', \u001b[0m\u001b[95m'WoodDeckSF', \u001b[0m\u001b[95m'YearBuilt', \n",
       "  \u001b[0m\u001b[95m'YearRemodAdd', \u001b[0m\u001b[95m'YrSold'\u001b[0m \n",
       "========================================================================================"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyzer.dropna(\n",
    "    include_vars=[\"SalePrice\"]\n",
    ").drop_highly_missing_vars(  # drop variables with more than 30% missing values\n",
    "    exclude_vars=[\"SalePrice\"], threshold=0.3\n",
    ").impute(  # impute missing values\n",
    "    exclude_vars=[\"SalePrice\"],\n",
    "    numeric_strategy=\"5nn\",\n",
    "    categorical_strategy=\"most_frequent\",\n",
    ").scale(  # scale numeric variables\n",
    "    exclude_vars=[\"SalePrice\"], strategy=\"standardize\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n_AcKvbplS2V"
   },
   "source": [
    "We can use the `regress` function in our analyzer to properly benchmark certain models. In this case, we compare the Random Forest, XGBoost, and linear regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "niKBNAW0lS2V",
    "outputId": "b527dd60-f289-4ffc-a812-089fd5ac87d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[93mPROG: \u001b[0mFitting \u001b[94m'BorutaFSR'\u001b[0m. \n",
      "\u001b[92mUPDT: \u001b[0mFitting model \u001b[94m'LinearR(l2)'\u001b[0m. \n",
      "\u001b[93mPROG: \u001b[0mFitting \u001b[94m'LinearR(l2)'\u001b[0m. Search method: OptunaSearchCV (100 trials, 500 total fits). \n",
      "       \n",
      "\u001b[92mUPDT: \u001b[0mSuccessfully evaluated model \u001b[94m'LinearR(l2)'\u001b[0m. \n",
      "\u001b[92mUPDT: \u001b[0mFitting model \u001b[94m'TreesR(random_forest)'\u001b[0m. \n",
      "\u001b[93mPROG: \u001b[0mFitting \u001b[94m'TreesR(random_forest)'\u001b[0m. Search method: OptunaSearchCV (100 trials, 500 \n",
      "      total fits).  \n",
      "\u001b[92mUPDT: \u001b[0mSuccessfully evaluated model \u001b[94m'TreesR(random_forest)'\u001b[0m. \n",
      "\u001b[92mUPDT: \u001b[0mFitting model \u001b[94m'TreesR(xgboost)'\u001b[0m. \n",
      "\u001b[93mPROG: \u001b[0mFitting \u001b[94m'TreesR(xgboost)'\u001b[0m. Search method: OptunaSearchCV (100 trials, 500 total \n",
      "      fits).  \n",
      "\u001b[92mUPDT: \u001b[0mSuccessfully evaluated model \u001b[94m'TreesR(xgboost)'\u001b[0m. \n"
     ]
    }
   ],
   "source": [
    "reg_report = analyzer.regress(\n",
    "    models=[\n",
    "        tm.ml.LinearR(\"l2\"),\n",
    "        tm.ml.TreesR(\"random_forest\"),\n",
    "        tm.ml.TreesR(\"xgboost\"),\n",
    "    ],\n",
    "    target=\"SalePrice\",\n",
    "    predictors=[\"1stFlrSF\", \"2ndFlrSF\", \"YrSold\", \"BldgType\", \"MSZoning\", \"LotShape\"],\n",
    "    feature_selectors=[tm.fs.BorutaFSR()],  # select features\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2OZlShEClS2V"
   },
   "source": [
    "Lastly, we can compare model metrics and save models for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "TnzEy1mClS2V",
    "outputId": "4cd88292-7368-4918-e15f-34543136e1bb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LinearR(l2)</th>\n",
       "      <th>TreesR(random_forest)</th>\n",
       "      <th>TreesR(xgboost)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Statistic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rmse</th>\n",
       "      <td>50294.432</td>\n",
       "      <td>47174.405</td>\n",
       "      <td>45710.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mae</th>\n",
       "      <td>32321.420</td>\n",
       "      <td>30499.842</td>\n",
       "      <td>29765.441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mape</th>\n",
       "      <td>0.194</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pearsonr</th>\n",
       "      <td>0.832</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spearmanr</th>\n",
       "      <td>0.861</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2</th>\n",
       "      <td>0.670</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adjr2</th>\n",
       "      <td>0.666</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_obs</th>\n",
       "      <td>292.000</td>\n",
       "      <td>292.000</td>\n",
       "      <td>292.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LinearR(l2)  TreesR(random_forest)  TreesR(xgboost)\n",
       "Statistic                                                     \n",
       "rmse         50294.432              47174.405        45710.156\n",
       "mae          32321.420              30499.842        29765.441\n",
       "mape             0.194                  0.187            0.180\n",
       "pearsonr         0.832                  0.849            0.859\n",
       "spearmanr        0.861                  0.866            0.870\n",
       "r2               0.670                  0.710            0.728\n",
       "adjr2            0.666                  0.706            0.724\n",
       "n_obs          292.000                292.000          292.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare model performance\n",
    "display(reg_report.metrics(\"test\"))\n",
    "\n",
    "# Predict on new data\n",
    "new_df = df.sample(frac=0.3)\n",
    "new_df = new_df.drop(columns=[\"SalePrice\"])\n",
    "\n",
    "y_pred = reg_report.model(\"LinearR(l2)\").sklearn_pipeline().predict(new_df)\n",
    "\n",
    "# Save model as sklearn pipeline\n",
    "joblib.dump(reg_report.model(\"LinearR(l2)\").sklearn_pipeline(), \"l2_pipeline.joblib\")\n",
    "\n",
    "# Load model and predict on new data\n",
    "y_pred_from_save = joblib.load(\"l2_pipeline.joblib\").predict(new_df)\n",
    "assert np.allclose(y_pred, y_pred_from_save)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7xTHOfk7lS2V"
   },
   "source": [
    "# Section 6: Conversational Data Analysis\n",
    "\n",
    "In this section, we demonstrate how to use TableMage's conversational data analysis capabilities. This is particularly useful for no-code use of the package, helping those that may not have the background necessary to do thorough data analysis on their datasets.\n",
    "\n",
    "First, we enable the use of agents in TableMage with the `tm.use_agents()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tablemage as tm\n",
    "\n",
    "data_path = curr_dir.parent / \"demo\" / \"regression\" / \"house_price_data\" / \"data.csv\"\n",
    "df = pd.read_csv(data_path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 263,
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     ]
    },
    "id": "HFB9L1ywb38w",
    "outputId": "715e5225-7d39-4ed9-cdb8-08000ef0b919"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mUPDT: \u001b[0mThe 'tablemage.agents' module has been imported. \n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Markdown\n",
    "\n",
    "tm.use_agents()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6eSh5msDlS2V"
   },
   "source": [
    "\n",
    "\n",
    "We then set the API key for the OpenAI agent using the `tm.agents.set_key` method. We also configure the agent to use the GPT-5.1 model with the `tm.agents.options.set_llm` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "ue9IBfKPb_x2"
   },
   "outputs": [],
   "source": [
    "tm.agents.set_key(\n",
    "    llm_type=\"openai\",\n",
    "    api_key=\"your-key-here\",\n",
    ")\n",
    "tm.agents.options.set_llm(llm_type=\"openai\", model_name=\"gpt-5.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6aTMKH17lS2V"
   },
   "source": [
    "We initialize a `ChatDA` agent with the training dataset `df` and a test size of 20%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "ERBs1CIT_JXT"
   },
   "outputs": [],
   "source": [
    "agent = tm.agents.ChatDA(df=df, test_size=0.2, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "87HT-V2-lS2W"
   },
   "source": [
    "We interact with the agent through the `chat` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 180
    },
    "id": "ufhH6Oaj_TNo",
    "outputId": "11bd335c-c9b9-48b9-91f9-216ba2cffa91"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Here’s what your summary tells us about the dataset:\n",
       "\n",
       "1. **Size and structure**\n",
       "   - **Training set**: 1,168 rows × 80 columns  \n",
       "   - **Test set**: 292 rows × 80 columns  \n",
       "   - This is a **moderate-sized tabular dataset**, typical for housing/real estate problems.\n",
       "\n",
       "2. **Variable types**\n",
       "   - **37 numeric variables** – continuous or discrete counts, e.g.:\n",
       "     - Size-related: `1stFlrSF`, `2ndFlrSF`, `GrLivArea`, `TotalBsmtSF`, `GarageArea`, `LotArea`, `MasVnrArea`, `WoodDeckSF`, `OpenPorchSF`, `EnclosedPorch`, `ScreenPorch`, `PoolArea`, `LowQualFinSF`, `3SsnPorch`\n",
       "     - Counts: `BedroomAbvGr`, `TotRmsAbvGrd`, `FullBath`, `HalfBath`, `BsmtFullBath`, `BsmtHalfBath`, `Fireplaces`, `GarageCars`, `KitchenAbvGr`\n",
       "     - Quality/overall scores (ordinal, but stored numeric): `OverallQual`, `OverallCond`\n",
       "     - Time-related: `YearBuilt`, `YearRemodAdd`, `GarageYrBlt`, `YrSold`, `MoSold`\n",
       "     - Others: `MSSubClass`, `LotFrontage`, `MiscVal`, and crucially **`SalePrice`**.\n",
       "   - **43 categorical variables** – building, quality, location, and condition descriptors, e.g.:\n",
       "     - Location/land: `Neighborhood`, `MSZoning`, `LotShape`, `LandContour`, `LotConfig`, `LandSlope`, `Street`, `Alley`\n",
       "     - Exterior: `Exterior1st`, `Exterior2nd`, `RoofStyle`, `RoofMatl`\n",
       "     - Basement / garage: `BsmtQual`, `BsmtCond`, `BsmtExposure`, `BsmtFinType1`, `BsmtFinType2`, `GarageType`, `GarageFinish`, `GarageQual`, `GarageCond`\n",
       "     - Quality/condition (ordinal but stored as categories): `ExterQual`, `ExterCond`, `HeatingQC`, `KitchenQual`, `Functional`, `FireplaceQu`, `Fence`, `PoolQC`\n",
       "     - House configuration: `BldgType`, `HouseStyle`, `Foundation`, `Heating`, `CentralAir`, `PavedDrive`\n",
       "     - Transaction: `SaleType`, `SaleCondition`, `Utilities`, `MiscFeature`\n",
       "\n",
       "3. **Likely task**\n",
       "   - Presence of **`SalePrice`** (numeric) suggests a **supervised regression problem**: predicting house sale prices from structural, quality, and location features.\n",
       "   - The test set with the same 80 features (presumably without `SalePrice`) is likely intended for out-of-sample evaluation or competition-style scoring.\n",
       "\n",
       "4. **Characteristics/considerations**\n",
       "   - Many variables are **housing attributes** (structure + location + condition), matching the classic **Ames housing** dataset style.\n",
       "   - You have a mix of:\n",
       "     - **Continuous features** (areas, frontage, lot size),\n",
       "     - **Discrete counts** (rooms, baths, cars),\n",
       "     - **Ordinal categories coded as text** (e.g., quality from Poor→Excellent),\n",
       "     - **Potentially sparse features** (e.g., `PoolQC`, `MiscFeature`, `Alley` often have many missing values in similar datasets).\n",
       "   - There are **many categorical variables (43)**, so modeling will require:\n",
       "     - Some form of **encoding** (e.g., one-hot encoding),\n",
       "     - Care about **high-cardinality** variables such as `Neighborhood`.\n",
       "\n",
       "If you’d like, the next steps I’d suggest are:\n",
       "- Show summary stats of **`SalePrice`** (distribution, range, skewness).\n",
       "- Inspect **missing values** by variable.\n",
       "- Look at simple **relationships with price** (e.g., `OverallQual`, `GrLivArea`, `Neighborhood`)."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = agent.chat(\"Tell me about the dataset.\")\n",
    "Markdown(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "hN7Uxj-6AI78",
    "outputId": "e789c8e1-3d2b-4568-d8c0-74115381fdd6"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Here’s what we can say from your data.\n",
       "\n",
       "## 1. Summary statistics\n",
       "\n",
       "### 1.1. Target variable: SalePrice\n",
       "\n",
       "- Count: 1,460\n",
       "- Mean: 180,921\n",
       "- Std dev: 79,443\n",
       "- Min: 34,900  \n",
       "- 25%: 129,975  \n",
       "- Median (50%): 163,000  \n",
       "- 75%: 214,000  \n",
       "- Max: 755,000  \n",
       "\n",
       "Implications:\n",
       "- Prices are quite spread out (std ~ 80k).\n",
       "- Strong right skew (few very expensive houses).\n",
       "\n",
       "### 1.2. Numeric variables (high level)\n",
       "\n",
       "For each numeric variable you have:\n",
       "- `min`, `max`, `mean`, `std`, `variance`\n",
       "- Quartiles (q1, median, q3)\n",
       "- Shape: `skew`, `kurtosis`\n",
       "- Missingness: `n_missing`, `missing_rate`\n",
       "\n",
       "Examples:\n",
       "- `GrLivArea` (above-ground living area):\n",
       "  - Mean: 1,515 sq ft, Std: 525, Min: 334, Max: 5,642\n",
       "  - Skew ~ 1.37 (right-skewed, some very large houses)\n",
       "  - No missing values\n",
       "- `OverallQual` (overall material/finish quality, 1–10):\n",
       "  - Mean: 6.10, Std: 1.38, IQR: 5–7\n",
       "- Variables with notable missingness:\n",
       "  - `LotFrontage`: 17.7% missing  \n",
       "  - `GarageYrBlt`: 5.5% missing  \n",
       "  - `MasVnrArea`: 0.5% missing  \n",
       "\n",
       "(If you want, I can list the full numeric summary table explicitly, but it’s quite long.)\n",
       "\n",
       "### 1.3. Categorical variables (high level)\n",
       "\n",
       "For each categorical variable you have:\n",
       "- Number of unique categories\n",
       "- Most common and least common category\n",
       "- Missingness\n",
       "\n",
       "Notable:\n",
       "- Very high missingness:\n",
       "  - `PoolQC`: 99.5% missing\n",
       "  - `MiscFeature`: 96.3% missing\n",
       "  - `Alley`: 93.8% missing\n",
       "  - `Fence`: 80.8% missing\n",
       "  - `MasVnrType`: 59.7% missing\n",
       "  - `FireplaceQu`: 47.3% missing\n",
       "- Neighborhood has 25 categories; most common is `NAmes`.\n",
       "\n",
       "These highly-missing variables may need special treatment (e.g., treat “missing” as its own category, or drop if not informative).\n",
       "\n",
       "---\n",
       "\n",
       "## 2. Variables most correlated with SalePrice\n",
       "\n",
       "Among numeric predictors, the top correlations with `SalePrice` are:\n",
       "\n",
       "| Variable       | Correlation with SalePrice |\n",
       "|----------------|----------------------------|\n",
       "| OverallQual    | 0.79                       |\n",
       "| GrLivArea      | 0.71                       |\n",
       "| GarageCars     | 0.64                       |\n",
       "| GarageArea     | 0.62                       |\n",
       "| TotalBsmtSF    | 0.61                       |\n",
       "| 1stFlrSF       | 0.61                       |\n",
       "| FullBath       | 0.56                       |\n",
       "| TotRmsAbvGrd   | 0.53                       |\n",
       "| YearBuilt      | 0.52                       |\n",
       "| YearRemodAdd   | 0.51                       |\n",
       "\n",
       "Interpretation:\n",
       "- **OverallQual** is the single strongest linear predictor of price.\n",
       "- Size variables (**GrLivArea**, **TotalBsmtSF**, **1stFlrSF**, **GarageArea**) are all strongly positively related to price.\n",
       "- **Garage capacity**, **number of full baths**, and **total rooms** also matter.\n",
       "- Newer homes (**YearBuilt**, **YearRemodAdd**) sell for more, on average.\n",
       "\n",
       "---\n",
       "\n",
       "## 3. Suggested next steps\n",
       "\n",
       "If you’d like to go further, we can:\n",
       "- Visualize `SalePrice` vs. these top predictors (scatterplots, log-transform if needed).\n",
       "- Check correlations among predictors (to understand multicollinearity).\n",
       "- Start a simple baseline model using just the top few variables."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = agent.chat(\"\"\"Provide the summary statistics. \n",
    "    Also help me figure out which variables are highly correlated with SalePrice.\n",
    "    \"\"\")\n",
    "Markdown(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "S9qhD4n_lS2W",
    "outputId": "2a468cd7-74e9-4c37-e788-93baed6127b8"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Here’s a linear regression model for `SalePrice` using a focused set of strong, interpretable predictors.\n",
       "\n",
       "## 1. Predictors I chose\n",
       "\n",
       "Based on prior correlation analysis and standard practice for this dataset, I used these numeric predictors:\n",
       "\n",
       "- `OverallQual` – overall material/finish quality  \n",
       "- `GrLivArea` – above-ground living area (sq ft)  \n",
       "- `GarageCars` – number of car spaces in garage  \n",
       "- `GarageArea` – size of garage (sq ft)  \n",
       "- `TotalBsmtSF` – total basement area (sq ft)  \n",
       "- `1stFlrSF` – first floor area (sq ft)  \n",
       "- `FullBath` – number of full bathrooms above ground  \n",
       "- `TotRmsAbvGrd` – total rooms above ground  \n",
       "- `YearBuilt` – original construction year  \n",
       "- `YearRemodAdd` – remodel year  \n",
       "\n",
       "All observations were complete for these variables (n = 1,460).\n",
       "\n",
       "The model is an ordinary least squares (OLS) linear regression:\n",
       "\n",
       "\\[\n",
       "\\text{SalePrice} = \\beta_0 + \\sum \\beta_j X_j + \\varepsilon\n",
       "\\]\n",
       "\n",
       "## 2. Model performance\n",
       "\n",
       "- R² = 0.774  \n",
       "- Adjusted R² = 0.772  \n",
       "\n",
       "Interpretation: about 77% of the variation in `SalePrice` is explained by this set of predictors.\n",
       "\n",
       "## 3. Key coefficients (effects)\n",
       "\n",
       "Each coefficient is the *average* change in `SalePrice` when that predictor increases by 1 unit, holding the others fixed.\n",
       "\n",
       "Significant predictors (p < 0.05):\n",
       "\n",
       "- `OverallQual`: **+19,605** per 1-point increase (on 1–10 scale)  \n",
       "- `GrLivArea`: **+51** per additional sq ft  \n",
       "- `GarageCars`: **+10,418** per additional car space  \n",
       "- `TotalBsmtSF`: **+19.9** per additional sq ft  \n",
       "- `1stFlrSF`: **+14.2** per additional sq ft  \n",
       "- `FullBath`: **−6,791** per additional full bath (see note below)  \n",
       "- `YearBuilt`: **+268** per year  \n",
       "- `YearRemodAdd`: **+296** per year  \n",
       "\n",
       "Not statistically significant (in presence of the others):\n",
       "\n",
       "- `GarageArea` (p ≈ 0.15)  \n",
       "- `TotRmsAbvGrd` (p ≈ 0.98)\n",
       "\n",
       "Notes:\n",
       "- `FullBath` showing a negative coefficient is likely due to **multicollinearity** with `GrLivArea`, `TotRmsAbvGrd`, etc. Larger houses tend to have more bathrooms; once you hold the other size variables constant, “extra baths” may not add much or can flip sign.\n",
       "- The very large **condition number (~4.7e5)** further indicates multicollinearity among size-related variables.\n",
       "\n",
       "The intercept:\n",
       "- `const` ≈ −1,186,194  \n",
       "  This isn’t directly meaningful because it corresponds to an impossible combination of predictor values (e.g., year 0, zero area, etc.), so we typically ignore its standalone interpretation.\n",
       "\n",
       "## 4. How to use this model\n",
       "\n",
       "In plain form, your prediction equation is approximately:\n",
       "\n",
       "\\[\n",
       "\\begin{aligned}\n",
       "\\widehat{\\text{SalePrice}} &= -1{,}186{,}194 \\\\\n",
       "&+ 19{,}605 \\cdot \\text{OverallQual} \\\\\n",
       "&+ 51.30 \\cdot \\text{GrLivArea} \\\\\n",
       "&+ 10{,}418 \\cdot \\text{GarageCars} \\\\\n",
       "&+ 14.95 \\cdot \\text{GarageArea} \\\\\n",
       "&+ 19.87 \\cdot \\text{TotalBsmtSF} \\\\\n",
       "&+ 14.17 \\cdot \\text{1stFlrSF} \\\\\n",
       "&- 6{,}791 \\cdot \\text{FullBath} \\\\\n",
       "&+ 33.11 \\cdot \\text{TotRmsAbvGrd} \\\\\n",
       "&+ 268.24 \\cdot \\text{YearBuilt} \\\\\n",
       "&+ 296.48 \\cdot \\text{YearRemodAdd}\n",
       "\\end{aligned}\n",
       "\\]\n",
       "\n",
       "(Values in currency units are in the same units as `SalePrice` in your data.)\n",
       "\n",
       "## 5. Suggested refinements\n",
       "\n",
       "If you want to improve or simplify this model, next steps could be:\n",
       "\n",
       "- Drop weak predictors (`GarageArea`, `TotRmsAbvGrd`) and refit.\n",
       "- Reduce multicollinearity by:\n",
       "  - Choosing **one** or two core size measures (e.g., `GrLivArea`, `TotalBsmtSF`) instead of several overlapping ones, or  \n",
       "  - Creating composite features (e.g., total finished area).\n",
       "- Consider a log-transform of `SalePrice` (and maybe heavily-skewed predictors) to better handle skew and outliers.\n",
       "\n",
       "If you’d like, I can:\n",
       "- Refit a **simplified model** with fewer, more stable predictors, or  \n",
       "- Train a model on `log(SalePrice)` and show you the updated performance and coefficients."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = agent.chat(\"\"\"Help me predict the SalePrice with linear regression. \n",
    "    Choose the predictors you think are most relevant.\n",
    "    \"\"\")\n",
    "Markdown(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "vrwUFgkTlS2W",
    "outputId": "a972dd7b-92d5-4dfb-e281-dcdeb1e9e51f"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Here’s what I did and what worked best.\n",
       "\n",
       "## 1. Models trained and comparison\n",
       "\n",
       "I focused on predicting `SalePrice` using a strong numeric subset:\n",
       "\n",
       "**Predictors used** (selected via Boruta feature selection):\n",
       "- `OverallQual`\n",
       "- `GrLivArea`\n",
       "- `GarageCars`\n",
       "- `GarageArea`\n",
       "- `TotalBsmtSF`\n",
       "- `1stFlrSF`\n",
       "- `FullBath`\n",
       "- `YearBuilt`\n",
       "- `YearRemodAdd`\n",
       "\n",
       "Then I trained and tuned these regression models, with an internal train/test split (about 80/20: 1,168 train, 292 test):\n",
       "\n",
       "- Linear models: **OLS**, **Ridge**, **Lasso**, **ElasticNet**\n",
       "- Nonlinear: **Random Forest (RF)**, **XGBoost**\n",
       "- **SVM** (RBF kernel)\n",
       "\n",
       "**Test-set performance (the metric that really matters):**\n",
       "\n",
       "- OLS: R² = 0.797, RMSE ≈ 39,459\n",
       "- Ridge: R² = 0.797, RMSE ≈ 39,447\n",
       "- Lasso: R² = 0.797, RMSE ≈ 39,459\n",
       "- ElasticNet: R² = 0.797, RMSE ≈ 39,444\n",
       "- **Random Forest: R² = 0.876, RMSE ≈ 30,864**\n",
       "- XGBoost: R² = 0.866, RMSE ≈ 32,087\n",
       "- SVM: R² ≈ −0.038, RMSE ≈ 89,244 (performed very poorly)\n",
       "\n",
       "**Best model:**  \n",
       "➡️ **Random Forest** is clearly the best: highest R² (0.876) and lowest RMSE (~30.9k) on the hold-out test set, substantially better than any linear model (R² ≈ 0.80, RMSE ≈ 39.4k) and a bit better than XGBoost.\n",
       "\n",
       "Roughly, Random Forest reduces test RMSE by about:\n",
       "- 21–22% vs your OLS baseline (≈ 39.5k → ≈ 30.9k)\n",
       "\n",
       "## 2. How the best model (Random Forest) was trained\n",
       "\n",
       "**Data & features**\n",
       "\n",
       "- Target: `SalePrice` (untransformed).\n",
       "- Features:  \n",
       "  `OverallQual, GrLivArea, GarageCars, GarageArea, TotalBsmtSF, 1stFlrSF, FullBath, YearBuilt, YearRemodAdd`\n",
       "- Only complete cases were used (no missingness for these variables in this dataset).\n",
       "\n",
       "**Train/test split**\n",
       "\n",
       "- Train size: 1,168 observations  \n",
       "- Test size: 292 observations  \n",
       "- The metrics above are evaluated on this unseen test set.\n",
       "\n",
       "**Model type**\n",
       "\n",
       "- Ensemble of decision trees: **Random Forest Regressor**.\n",
       "\n",
       "**Hyperparameter tuning**\n",
       "\n",
       "I did automated hyperparameter search with cross-validation:\n",
       "\n",
       "- Search space included:\n",
       "  - `n_estimators`: {50, 100, 200, 400}\n",
       "  - `max_depth`: integers from 3 to 15 (step 2)\n",
       "  - `min_samples_split`: {2, 5, 10}\n",
       "  - `min_samples_leaf`: {1, 2, 4}\n",
       "  - `max_features`: {'sqrt', 'log2'}\n",
       "- 5-fold cross-validation inside the training set.\n",
       "- 100 optimization trials to find hyperparameters that best minimized cross-validated error.\n",
       "\n",
       "**Best-found hyperparameters**\n",
       "\n",
       "The final (best) Random Forest used:\n",
       "\n",
       "- `n_estimators`: **200**\n",
       "- `max_depth`: **13**\n",
       "- `min_samples_split`: **10**\n",
       "- `min_samples_leaf`: **1**\n",
       "- `max_features`: **'log2'**\n",
       "\n",
       "This configuration balances model complexity and overfitting fairly well (high training R² but also high test R²).\n",
       "\n",
       "**Feature importance (from Random Forest)**\n",
       "\n",
       "Relative importance of each predictor in the best model:\n",
       "\n",
       "- `OverallQual`: **0.298**\n",
       "- `GrLivArea`: **0.192**\n",
       "- `GarageCars`: **0.137**\n",
       "- `YearBuilt`: **0.090**\n",
       "- `TotalBsmtSF`: **0.082**\n",
       "- `1stFlrSF`: **0.062**\n",
       "- `GarageArea`: **0.071**\n",
       "- `FullBath`: **0.035**\n",
       "- `YearRemodAdd`: **0.034**\n",
       "\n",
       "Interpretation: `OverallQual` and `GrLivArea` are the dominant drivers of predictions, but garage capacity, house age, and basement/first-floor area all contribute meaningfully.\n",
       "\n",
       "## 3. Summary\n",
       "\n",
       "- Your original OLS linear regression explained ~77–80% of variance with RMSE around 39–40k.\n",
       "- A **tuned Random Forest** using 9 numeric predictors now explains **~88%** of variance with **RMSE ~30.9k** on a hold-out test set.\n",
       "- That Random Forest is the best-performing model I trained, and it was selected via cross-validated hyperparameter tuning over an ensemble of 200 depth-limited trees.\n",
       "\n",
       "If you’d like, I can:\n",
       "- Export the exact model formula/settings you’d need to re-implement it in Python or R, or\n",
       "- Try adding more features (including categorical variables via one-hot encoding) to see if we can squeeze out even better performance."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = agent.chat(\n",
    "    \"\"\"Fit some machine learning models to try to obtain better predictive performance.\n",
    "    Do whatever you think would be best for performance.\n",
    "    Let me know which model is best and how it performs.\n",
    "    Tell me also how you trained that model.\n",
    "    \"\"\"\n",
    ")\n",
    "Markdown(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k8Z2lazHlS2W"
   },
   "source": [
    "We hope you enjoy TableMage."
   ]
  }
 ],
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