{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "93780b6a",
      "metadata": {
        "id": "93780b6a"
      },
      "source": [
        "# ACCA7026 Artificial Intelligence  \n",
        "## Credit Risk Assessment Using Traditional Machine Learning\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7630c1f3",
      "metadata": {
        "id": "7630c1f3"
      },
      "source": [
        "## 1. Environment, configuration and reproducibility"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "00b1ee41",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00b1ee41",
        "outputId": "e871f7bb-f561-4c8a-fa3e-eb83b06dcd1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'FAST_MODE': False, 'CV_SPLITS': 5, 'CV_REPEATS': 2, 'RF_SEARCH_ITERATIONS': 8, 'N_JOBS': 2, 'BOOTSTRAP_ITERATIONS': 750}\n"
          ]
        }
      ],
      "source": [
        "# Final configuration\n",
        "FAST_MODE = False\n",
        "RANDOM_STATE = 42\n",
        "TEST_SIZE = 0.20\n",
        "N_JOBS = 2\n",
        "\n",
        "# FAST_MODE reduces runtime for debugging. Keep it False for the submitted run.\n",
        "CV_SPLITS = 3 if FAST_MODE else 5\n",
        "CV_REPEATS = 1 if FAST_MODE else 2\n",
        "RF_SEARCH_ITERATIONS = 5 if FAST_MODE else 8\n",
        "PERMUTATION_REPEATS = 5 if FAST_MODE else 8\n",
        "BOOTSTRAP_ITERATIONS = 300 if FAST_MODE else 750\n",
        "SHAP_SAMPLE_SIZE = 100 if FAST_MODE else 120\n",
        "\n",
        "# Leave as None when the CSV is beside the notebook or uploaded to Colab.\n",
        "DATA_PATH_OVERRIDE = None\n",
        "\n",
        "print({\n",
        "    \"FAST_MODE\": FAST_MODE,\n",
        "    \"CV_SPLITS\": CV_SPLITS,\n",
        "    \"CV_REPEATS\": CV_REPEATS,\n",
        "    \"RF_SEARCH_ITERATIONS\": RF_SEARCH_ITERATIONS,\n",
        "    \"N_JOBS\": N_JOBS,\n",
        "    \"BOOTSTRAP_ITERATIONS\": BOOTSTRAP_ITERATIONS,\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "c3f7a122",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3f7a122",
        "outputId": "d7321071-fa21-4316-a8d5-b907e6851c7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shap is already available.\n",
            "imbalanced-learn is already available.\n"
          ]
        }
      ],
      "source": [
        "# Install optional packages only when they are unavailable.\n",
        "import importlib.util\n",
        "import subprocess\n",
        "import sys\n",
        "\n",
        "required_packages = {\n",
        "    \"shap\": \"shap\",\n",
        "    \"imblearn\": \"imbalanced-learn\",\n",
        "}\n",
        "\n",
        "for module_name, package_name in required_packages.items():\n",
        "    if importlib.util.find_spec(module_name) is None:\n",
        "        print(f\"Installing {package_name}...\")\n",
        "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", package_name])\n",
        "    else:\n",
        "        print(f\"{package_name} is already available.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "525edc1c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "525edc1c",
        "outputId": "db588106-1e4e-446c-faf7-b239aa74badc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python: 3.12.13\n",
            "pandas: 2.2.2\n",
            "NumPy: 2.0.2\n",
            "SciPy: 1.16.3\n",
            "scikit-learn: 1.6.1\n"
          ]
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "import hashlib\n",
        "import json\n",
        "import platform\n",
        "import time\n",
        "import warnings\n",
        "\n",
        "import joblib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy\n",
        "import sklearn\n",
        "\n",
        "from IPython.display import Markdown, display\n",
        "from scipy.stats import randint\n",
        "\n",
        "from sklearn.base import clone\n",
        "from sklearn.calibration import calibration_curve\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.inspection import PartialDependenceDisplay, permutation_importance\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import (\n",
        "    ConfusionMatrixDisplay,\n",
        "    accuracy_score,\n",
        "    average_precision_score,\n",
        "    balanced_accuracy_score,\n",
        "    brier_score_loss,\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    f1_score,\n",
        "    precision_recall_curve,\n",
        "    precision_recall_fscore_support,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    roc_auc_score,\n",
        "    roc_curve,\n",
        ")\n",
        "from sklearn.model_selection import (\n",
        "    GridSearchCV,\n",
        "    RandomizedSearchCV,\n",
        "    RepeatedStratifiedKFold,\n",
        "    StratifiedKFold,\n",
        "    cross_val_predict,\n",
        "    cross_validate,\n",
        "    train_test_split,\n",
        ")\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler, label_binarize\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.pipeline import Pipeline as ImbalancedPipeline\n",
        "\n",
        "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
        "\n",
        "pd.set_option(\"display.max_columns\", 100)\n",
        "pd.set_option(\"display.max_rows\", 100)\n",
        "pd.set_option(\"display.float_format\", lambda value: f\"{value:,.4f}\")\n",
        "\n",
        "print(\"Python:\", platform.python_version())\n",
        "print(\"pandas:\", pd.__version__)\n",
        "print(\"NumPy:\", np.__version__)\n",
        "print(\"SciPy:\", scipy.__version__)\n",
        "print(\"scikit-learn:\", sklearn.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6fd48532",
      "metadata": {
        "id": "6fd48532"
      },
      "source": [
        "## 2. Load and verify the supplied Lending Club dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "4bd0f694",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "4bd0f694",
        "outputId": "bf360fd3-470f-48ed-e1b6-ae0e9924bdb8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset path: loans_full_schema.csv\n",
            "Dataset shape: (10000, 56)\n",
            "SHA-256: 52872e7d4f8a388b7b4891852ea9009fba002df63ad53d8dbc8ea97b982b2e29\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Unnamed: 0                emp_title  emp_length state homeownership  \\\n",
              "0           1  global config engineer       3.0000    NJ      MORTGAGE   \n",
              "1           2   warehouse office clerk     10.0000    HI          RENT   \n",
              "2           3                 assembly      3.0000    WI          RENT   \n",
              "3           4         customer service      1.0000    PA          RENT   \n",
              "4           5     security supervisor      10.0000    CA          RENT   \n",
              "\n",
              "   annual_income  verified_income  debt_to_income  annual_income_joint  \\\n",
              "0    90,000.0000         Verified         18.0100                  NaN   \n",
              "1    40,000.0000     Not Verified          5.0400                  NaN   \n",
              "2    40,000.0000  Source Verified         21.1500                  NaN   \n",
              "3    30,000.0000     Not Verified         10.1600                  NaN   \n",
              "4    35,000.0000         Verified         57.9600          57,000.0000   \n",
              "\n",
              "  verification_income_joint  debt_to_income_joint  delinq_2y  \\\n",
              "0                       NaN                   NaN          0   \n",
              "1                       NaN                   NaN          0   \n",
              "2                       NaN                   NaN          0   \n",
              "3                       NaN                   NaN          0   \n",
              "4                  Verified               37.6600          0   \n",
              "\n",
              "   months_since_last_delinq  earliest_credit_line  inquiries_last_12m  \\\n",
              "0                   38.0000                  2001                   6   \n",
              "1                       NaN                  1996                   1   \n",
              "2                   28.0000                  2006                   4   \n",
              "3                       NaN                  2007                   0   \n",
              "4                       NaN                  2008                   7   \n",
              "\n",
              "   total_credit_lines  open_credit_lines  total_credit_limit  \\\n",
              "0                  28                 10               70795   \n",
              "1                  30                 14               28800   \n",
              "2                  31                 10               24193   \n",
              "3                   4                  4               25400   \n",
              "4                  22                 16               69839   \n",
              "\n",
              "   total_credit_utilized  num_collections_last_12m  \\\n",
              "0                  38767                         0   \n",
              "1                   4321                         0   \n",
              "2                  16000                         0   \n",
              "3                   4997                         0   \n",
              "4                  52722                         0   \n",
              "\n",
              "   num_historical_failed_to_pay  months_since_90d_late  \\\n",
              "0                             0                38.0000   \n",
              "1                             1                    NaN   \n",
              "2                             0                28.0000   \n",
              "3                             1                    NaN   \n",
              "4                             0                    NaN   \n",
              "\n",
              "   current_accounts_delinq  total_collection_amount_ever  \\\n",
              "0                        0                          1250   \n",
              "1                        0                             0   \n",
              "2                        0                           432   \n",
              "3                        0                             0   \n",
              "4                        0                             0   \n",
              "\n",
              "   current_installment_accounts  accounts_opened_24m  \\\n",
              "0                             2                    5   \n",
              "1                             0                   11   \n",
              "2                             1                   13   \n",
              "3                             1                    1   \n",
              "4                             1                    6   \n",
              "\n",
              "   months_since_last_credit_inquiry  num_satisfactory_accounts  \\\n",
              "0                            5.0000                         10   \n",
              "1                            8.0000                         14   \n",
              "2                            7.0000                         10   \n",
              "3                           15.0000                          4   \n",
              "4                            4.0000                         16   \n",
              "\n",
              "   num_accounts_120d_past_due  num_accounts_30d_past_due  \\\n",
              "0                      0.0000                          0   \n",
              "1                      0.0000                          0   \n",
              "2                      0.0000                          0   \n",
              "3                      0.0000                          0   \n",
              "4                      0.0000                          0   \n",
              "\n",
              "   num_active_debit_accounts  total_debit_limit  num_total_cc_accounts  \\\n",
              "0                          2              11100                     14   \n",
              "1                          3              16500                     24   \n",
              "2                          3               4300                     14   \n",
              "3                          2              19400                      3   \n",
              "4                         10              32700                     20   \n",
              "\n",
              "   num_open_cc_accounts  num_cc_carrying_balance  num_mort_accounts  \\\n",
              "0                     8                        6                  1   \n",
              "1                    14                        4                  0   \n",
              "2                     8                        6                  0   \n",
              "3                     3                        2                  0   \n",
              "4                    15                       13                  0   \n",
              "\n",
              "   account_never_delinq_percent  tax_liens  public_record_bankrupt  \\\n",
              "0                       92.9000          0                       0   \n",
              "1                      100.0000          0                       1   \n",
              "2                       93.5000          0                       0   \n",
              "3                      100.0000          1                       0   \n",
              "4                      100.0000          0                       0   \n",
              "\n",
              "         loan_purpose application_type  loan_amount  term  interest_rate  \\\n",
              "0              moving       individual        28000    60        14.0700   \n",
              "1  debt_consolidation       individual         5000    36        12.6100   \n",
              "2               other       individual         2000    36        17.0900   \n",
              "3  debt_consolidation       individual        21600    36         6.7200   \n",
              "4         credit_card            joint        23000    36        14.0700   \n",
              "\n",
              "   installment grade sub_grade issue_month loan_status initial_listing_status  \\\n",
              "0     652.5300     C        C3    Mar-2018     Current                  whole   \n",
              "1     167.5400     C        C1    Feb-2018     Current                  whole   \n",
              "2      71.4000     D        D1    Feb-2018     Current             fractional   \n",
              "3     664.1900     A        A3    Jan-2018     Current                  whole   \n",
              "4     786.8700     C        C3    Mar-2018     Current                  whole   \n",
              "\n",
              "  disbursement_method     balance  paid_total  paid_principal  paid_interest  \\\n",
              "0                Cash 27,015.8600  1,999.3300        984.1400     1,015.1900   \n",
              "1                Cash  4,651.3700    499.1200        348.6300       150.4900   \n",
              "2                Cash  1,824.6300    281.8000        175.3700       106.4300   \n",
              "3                Cash 18,853.2600  3,312.8900      2,746.7400       566.1500   \n",
              "4                Cash 21,430.1500  2,324.6500      1,569.8500       754.8000   \n",
              "\n",
              "   paid_late_fees  \n",
              "0          0.0000  \n",
              "1          0.0000  \n",
              "2          0.0000  \n",
              "3          0.0000  \n",
              "4          0.0000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad854bbb-3ece-4af1-b647-efcf853cb7a0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>state</th>\n",
              "      <th>homeownership</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>verified_income</th>\n",
              "      <th>debt_to_income</th>\n",
              "      <th>annual_income_joint</th>\n",
              "      <th>verification_income_joint</th>\n",
              "      <th>debt_to_income_joint</th>\n",
              "      <th>delinq_2y</th>\n",
              "      <th>months_since_last_delinq</th>\n",
              "      <th>earliest_credit_line</th>\n",
              "      <th>inquiries_last_12m</th>\n",
              "      <th>total_credit_lines</th>\n",
              "      <th>open_credit_lines</th>\n",
              "      <th>total_credit_limit</th>\n",
              "      <th>total_credit_utilized</th>\n",
              "      <th>num_collections_last_12m</th>\n",
              "      <th>num_historical_failed_to_pay</th>\n",
              "      <th>months_since_90d_late</th>\n",
              "      <th>current_accounts_delinq</th>\n",
              "      <th>total_collection_amount_ever</th>\n",
              "      <th>current_installment_accounts</th>\n",
              "      <th>accounts_opened_24m</th>\n",
              "      <th>months_since_last_credit_inquiry</th>\n",
              "      <th>num_satisfactory_accounts</th>\n",
              "      <th>num_accounts_120d_past_due</th>\n",
              "      <th>num_accounts_30d_past_due</th>\n",
              "      <th>num_active_debit_accounts</th>\n",
              "      <th>total_debit_limit</th>\n",
              "      <th>num_total_cc_accounts</th>\n",
              "      <th>num_open_cc_accounts</th>\n",
              "      <th>num_cc_carrying_balance</th>\n",
              "      <th>num_mort_accounts</th>\n",
              "      <th>account_never_delinq_percent</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>public_record_bankrupt</th>\n",
              "      <th>loan_purpose</th>\n",
              "      <th>application_type</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>issue_month</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>initial_listing_status</th>\n",
              "      <th>disbursement_method</th>\n",
              "      <th>balance</th>\n",
              "      <th>paid_total</th>\n",
              "      <th>paid_principal</th>\n",
              "      <th>paid_interest</th>\n",
              "      <th>paid_late_fees</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>global config engineer</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>NJ</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>90,000.0000</td>\n",
              "      <td>Verified</td>\n",
              "      <td>18.0100</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0000</td>\n",
              "      <td>2001</td>\n",
              "      <td>6</td>\n",
              "      <td>28</td>\n",
              "      <td>10</td>\n",
              "      <td>70795</td>\n",
              "      <td>38767</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>1250</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11100</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>92.9000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>moving</td>\n",
              "      <td>individual</td>\n",
              "      <td>28000</td>\n",
              "      <td>60</td>\n",
              "      <td>14.0700</td>\n",
              "      <td>652.5300</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>27,015.8600</td>\n",
              "      <td>1,999.3300</td>\n",
              "      <td>984.1400</td>\n",
              "      <td>1,015.1900</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>warehouse office clerk</td>\n",
              "      <td>10.0000</td>\n",
              "      <td>HI</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40,000.0000</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>5.0400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1996</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>14</td>\n",
              "      <td>28800</td>\n",
              "      <td>4321</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>16500</td>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>100.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>5000</td>\n",
              "      <td>36</td>\n",
              "      <td>12.6100</td>\n",
              "      <td>167.5400</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>4,651.3700</td>\n",
              "      <td>499.1200</td>\n",
              "      <td>348.6300</td>\n",
              "      <td>150.4900</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>assembly</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>WI</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40,000.0000</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>21.1500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>2006</td>\n",
              "      <td>4</td>\n",
              "      <td>31</td>\n",
              "      <td>10</td>\n",
              "      <td>24193</td>\n",
              "      <td>16000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>432</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4300</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>93.5000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>other</td>\n",
              "      <td>individual</td>\n",
              "      <td>2000</td>\n",
              "      <td>36</td>\n",
              "      <td>17.0900</td>\n",
              "      <td>71.4000</td>\n",
              "      <td>D</td>\n",
              "      <td>D1</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>fractional</td>\n",
              "      <td>Cash</td>\n",
              "      <td>1,824.6300</td>\n",
              "      <td>281.8000</td>\n",
              "      <td>175.3700</td>\n",
              "      <td>106.4300</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>customer service</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>PA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30,000.0000</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>10.1600</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>25400</td>\n",
              "      <td>4997</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>19400</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>100.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>21600</td>\n",
              "      <td>36</td>\n",
              "      <td>6.7200</td>\n",
              "      <td>664.1900</td>\n",
              "      <td>A</td>\n",
              "      <td>A3</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>18,853.2600</td>\n",
              "      <td>3,312.8900</td>\n",
              "      <td>2,746.7400</td>\n",
              "      <td>566.1500</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>security supervisor</td>\n",
              "      <td>10.0000</td>\n",
              "      <td>CA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>35,000.0000</td>\n",
              "      <td>Verified</td>\n",
              "      <td>57.9600</td>\n",
              "      <td>57,000.0000</td>\n",
              "      <td>Verified</td>\n",
              "      <td>37.6600</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>16</td>\n",
              "      <td>69839</td>\n",
              "      <td>52722</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>16</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>32700</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>100.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>joint</td>\n",
              "      <td>23000</td>\n",
              "      <td>36</td>\n",
              "      <td>14.0700</td>\n",
              "      <td>786.8700</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>21,430.1500</td>\n",
              "      <td>2,324.6500</td>\n",
              "      <td>1,569.8500</td>\n",
              "      <td>754.8000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad854bbb-3ece-4af1-b647-efcf853cb7a0')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ad854bbb-3ece-4af1-b647-efcf853cb7a0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ad854bbb-3ece-4af1-b647-efcf853cb7a0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def locate_dataset(path_override=None):\n",
        "    # Search common local, Colab and assessment folders.\n",
        "    candidates = []\n",
        "\n",
        "    if path_override:\n",
        "        candidates.append(Path(path_override))\n",
        "\n",
        "    candidates.extend([\n",
        "        Path(\"loans_full_schema.csv\"),\n",
        "        Path(\"/content/loans_full_schema.csv\"),\n",
        "        Path(\"/mnt/data/loans_full_schema.csv\"),\n",
        "    ])\n",
        "\n",
        "    for candidate in candidates:\n",
        "        if candidate.exists():\n",
        "            return candidate\n",
        "\n",
        "    # Colab fallback: request an upload only if automatic discovery fails.\n",
        "    try:\n",
        "        from google.colab import files\n",
        "        print(\"Upload loans_full_schema.csv.\")\n",
        "        uploaded = files.upload()\n",
        "        if uploaded:\n",
        "            return Path(next(iter(uploaded)))\n",
        "    except ImportError:\n",
        "        pass\n",
        "\n",
        "    raise FileNotFoundError(\n",
        "        \"loans_full_schema.csv was not found. Put it beside this notebook \"\n",
        "        \"or set DATA_PATH_OVERRIDE to the complete file path.\"\n",
        "    )\n",
        "\n",
        "\n",
        "DATA_PATH = locate_dataset(DATA_PATH_OVERRIDE)\n",
        "data_raw = pd.read_csv(DATA_PATH)\n",
        "dataset_hash = hashlib.sha256(DATA_PATH.read_bytes()).hexdigest()\n",
        "\n",
        "print(\"Dataset path:\", DATA_PATH)\n",
        "print(\"Dataset shape:\", data_raw.shape)\n",
        "print(\"SHA-256:\", dataset_hash)\n",
        "display(data_raw.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "67c699a7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "67c699a7",
        "outputId": "f1274ace-9737-4c5d-ea9d-8b02ae6f1a7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows: 10,000\n",
            "Columns: 56\n",
            "Duplicate substantive rows: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                    dtype  missing_n  missing_pct  \\\n",
              "verification_income_joint          object       8545      85.4500   \n",
              "debt_to_income_joint              float64       8505      85.0500   \n",
              "annual_income_joint               float64       8505      85.0500   \n",
              "months_since_90d_late             float64       7715      77.1500   \n",
              "months_since_last_delinq          float64       5658      56.5800   \n",
              "months_since_last_credit_inquiry  float64       1271      12.7100   \n",
              "emp_title                          object        833       8.3300   \n",
              "emp_length                        float64        817       8.1700   \n",
              "num_accounts_120d_past_due        float64        318       3.1800   \n",
              "debt_to_income                    float64         24       0.2400   \n",
              "Unnamed: 0                          int64          0       0.0000   \n",
              "total_credit_utilized               int64          0       0.0000   \n",
              "total_credit_limit                  int64          0       0.0000   \n",
              "paid_total                        float64          0       0.0000   \n",
              "paid_interest                     float64          0       0.0000   \n",
              "paid_principal                    float64          0       0.0000   \n",
              "balance                           float64          0       0.0000   \n",
              "installment                       float64          0       0.0000   \n",
              "annual_income                     float64          0       0.0000   \n",
              "total_debit_limit                   int64          0       0.0000   \n",
              "\n",
              "                                  unique_values  \n",
              "verification_income_joint                     4  \n",
              "debt_to_income_joint                       1190  \n",
              "annual_income_joint                         597  \n",
              "months_since_90d_late                       107  \n",
              "months_since_last_delinq                     98  \n",
              "months_since_last_credit_inquiry             26  \n",
              "emp_title                                  4742  \n",
              "emp_length                                   12  \n",
              "num_accounts_120d_past_due                    2  \n",
              "debt_to_income                             3674  \n",
              "Unnamed: 0                                10000  \n",
              "total_credit_utilized                      9497  \n",
              "total_credit_limit                         9119  \n",
              "paid_total                                 7475  \n",
              "paid_interest                              7422  \n",
              "paid_principal                             5765  \n",
              "balance                                    5741  \n",
              "installment                                3540  \n",
              "annual_income                              1463  \n",
              "total_debit_limit                          1222  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1254eb2-1f74-4c03-b6e6-55659b3f2d9c\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>dtype</th>\n",
              "      <th>missing_n</th>\n",
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              "      <td>26</td>\n",
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              "      <th>emp_title</th>\n",
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              "    </tr>\n",
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              "      <th>emp_length</th>\n",
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              "      <td>8.1700</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_accounts_120d_past_due</th>\n",
              "      <td>float64</td>\n",
              "      <td>318</td>\n",
              "      <td>3.1800</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income</th>\n",
              "      <td>float64</td>\n",
              "      <td>24</td>\n",
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              "      <td>3674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
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              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_credit_utilized</th>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>9497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_credit_limit</th>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>9119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paid_total</th>\n",
              "      <td>float64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>7475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paid_interest</th>\n",
              "      <td>float64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>7422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paid_principal</th>\n",
              "      <td>float64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>balance</th>\n",
              "      <td>float64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>float64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_income</th>\n",
              "      <td>float64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_debit_limit</th>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1254eb2-1f74-4c03-b6e6-55659b3f2d9c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b1254eb2-1f74-4c03-b6e6-55659b3f2d9c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b1254eb2-1f74-4c03-b6e6-55659b3f2d9c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(audit_summary\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"dtype\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"object\",\n          \"float64\",\n          \"int64\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"missing_n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3428,\n        \"min\": 0,\n        \"max\": 8545,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          24,\n          8505,\n          833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"missing_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.2817857845671,\n        \"min\": 0.0,\n        \"max\": 85.45,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.24,\n          85.05,\n          8.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique_values\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3608,\n        \"min\": 2,\n        \"max\": 10000,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          4,\n          3540,\n          5765\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Structural data audit\n",
        "audit_summary = pd.DataFrame({\n",
        "    \"dtype\": data_raw.dtypes.astype(str),\n",
        "    \"missing_n\": data_raw.isna().sum(),\n",
        "    \"missing_pct\": data_raw.isna().mean().mul(100),\n",
        "    \"unique_values\": data_raw.nunique(dropna=False),\n",
        "}).sort_values([\"missing_pct\", \"unique_values\"], ascending=[False, False])\n",
        "\n",
        "duplicate_rows = (\n",
        "    data_raw.drop(columns=[\"Unnamed: 0\"], errors=\"ignore\")\n",
        "    .duplicated()\n",
        "    .sum()\n",
        ")\n",
        "\n",
        "print(f\"Rows: {data_raw.shape[0]:,}\")\n",
        "print(f\"Columns: {data_raw.shape[1]:,}\")\n",
        "print(f\"Duplicate substantive rows: {duplicate_rows:,}\")\n",
        "display(audit_summary.head(20))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "ef455661",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "ef455661",
        "outputId": "1be1ba83-745c-4096-96bc-135f885518a6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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UmppKkSJFWLFixX3L0+5T2gvcf/rpJ7777js2b95Mnz59mDVrFj/99NMjvXj9QXHeb39G16djx47Ur1+fb775hi1btvD+++/z3nvvsWbNGpo3bw7ArFmz6NWrF99++y1btmxhyJAhTJs2jZ9++okSJUo8VKyPct8y+51bvnw5np6e99S7c3XRYcOG0apVK9auXcvmzZsZN24c06ZN44cffiAgICBH76NIZijJExGRXNGtWzfGjRvHoUOHWLlyJeXKlaNWrVpG+erVq3nuuef49NNPLY67evUqhQsXfqg+Dx8+zPHjx1m6dKnFohJbt269b/3U1FR+++03Y/QO4Pjx4wD3rMaYxt3dHWdnZ1JSUmjatGmGMTk6OtKpUyc6derEjRs3aNeuHVOmTGHMmDHpvu6gfv36vPfee2zbto3ChQvj5+eHyWSiYsWK7Nixgx07dvDiiy9m2P+d09ey06O2m7YAjIuLS6auY3YqVaoUYWFhJCUlWYzm3W/1z8wqW7Ys27ZtIzAwMFP/IPHMM8/wzDPPMGXKFFauXEm3bt1YtWoVr776ao7ds6wqWrQoAwYMYMCAAVy8eJHq1aszZcoUI8kDqFy5MpUrV2bs2LHs2rWLwMBAPvroI959991H6rtUqVLA7Xty5yj+pUuXHnqUOe07V6RIkUx958qWLcubb77Jm2++yYkTJ6hWrRqzZs3if//7n1EnvfsokpM0XVNERHJF2qjd+PHjiYyMvOfdeNbW1vf8q/1XX33FuXPnHrrPtH/Jv7Nds9nM3LlzH3jMggULLOouWLAAGxsb49nB+/XRvn17vv76a44cOXJP+Z1Ltl+6dMmizNbWlgoVKmA2mzN8jqd+/fokJyczZ84cnn32WeOHf/369Vm+fDnnz5/P1PN4jo6OFq9dyC6Ojo6Znpp3PzVq1KBs2bLMnDnTeAXEne639H12CQoK4ubNmxYv6U5NTeW///3vQ7fZsWNHUlJSmDx58j1lt27dMu7BlStX7vnep41kpk31S0s8c+K+ZUZKSso997ZIkSIUK1bMiDEhIYFbt25Z1KlcuTJWVlbZMmWxSZMm5MuXj4ULF1rsv/O/16wKCgrCxcWFqVOn3ve/v7TvXFJS0j0reJYtWxZnZ2fj3DJzH0VykkbyREQkV5QuXZp69erx7bffAtyT5L344otMmjSJ3r17U69ePQ4fPsyKFSvueUYuK/z8/ChbtiwjR47k3LlzuLi48PXXXz/wX/7z58/Ppk2b6NmzJ3Xq1GHjxo1s2LCB//znP+lOg5w+fTphYWHUqVOHfv36UaFCBS5fvsyBAwfYtm0bly9fBuCFF17A09OTwMBAPDw8iI6OZsGCBbRs2RJnZ+d0z6Vu3brky5ePY8eOGa8/AGjQoIHxwzczSV6NGjXYtm0bH3zwAcWKFaN06dLUqVMnw+My0+4XX3zBiBEjqFWrFk5OTrRq1SrTx1tZWfHJJ5/QvHlzKlasSO/evSlevDjnzp0jLCwMFxcXvvvuu0eO837atGlD7dq1efPNNzl58iR+fn6sW7fOuG8PM5LWsGFD+vfvz7Rp04iMjOSFF17AxsaGEydO8NVXXzF37lxefvllli5dyocffkjbtm0pW7Ys165dY/Hixbi4uNCiRQvg9tTkChUq8MUXX+Dr60vBggWpVKkSlSpVytbr8CDXrl2jRIkSvPzyy1StWhUnJye2bdvGvn37mDVrFnD79ReDBg2iQ4cO+Pr6cuvWLZYvX278I8ij8vDwYOjQocyaNYuXXnqJZs2aERUVxcaNGylcuPBD3SMXFxcWLlxI9+7dqV69Op07d8bd3Z3Y2Fg2bNhAYGAgCxYs4Pjx4zRp0oSOHTtSoUIF8uXLxzfffMMff/xB586dATJ1H0VykpI8ERHJNd26dWPXrl3Url37ngUU/vOf/3D9+nVWrlzJF198QfXq1dmwYQNvv/32Q/dnY2PDd999ZzwblD9/ftq2bcugQYOoWrXqPfWtra3ZtGkTb7zxBm+99RbOzs5MmDCB8ePHp9uPh4cHe/fuZdKkSaxZs4YPP/yQQoUKUbFiRYv3y/Xv358VK1bwwQcfkJiYSIkSJRgyZAhjx47N8FwcHR0JCAhg3759FourpCV2Xl5expS29HzwwQe89tprjB07lr///ttIaB/VgAEDiIyMZMmSJcyePZtSpUplKcmD2y9V3717N5MnT2bBggUkJibi6elJnTp16N+//yPH+CDW1tZs2LCBoUOHsnTpUqysrGjbti0TJkwgMDAw3Wm06fnoo4+oUaMGixYt4j//+Q/58uXD29ubV155hcDAQOB2Mrh3715WrVrFH3/8gaurK7Vr12bFihUW0xI/+eQTBg8ezPDhw7lx4wYTJkx4bEmeg4MDAwYMYMuWLaxZs4bU1FR8fHz48MMPjfcCVq1alaCgIL777jvOnTuHg4MDVatWZePGjTzzzDPZEsd7772Hg4MDixcvZtu2bdStW5ctW7bw7LPPPvQ96tq1K8WKFWP69Om8//77JCcnU7x4cerXr28snOTl5UWXLl0IDQ1l+fLl5MuXDz8/P7788ksjgc3sfRTJKSZzdj55LCIiIpJHrV27lrZt27Jz504jKZMny9WrVylQoADvvvsu77zzTm6HI5Jr9EyeiIiIyF3+/vtvi+2UlBTmz5+Pi4sL1atXz6Wo5E533yOAOXPmALdHgUX+zTRdU0REROQugwcP5u+//6Zu3bokJyezZs0adu3axdSpUzO1OqbkvC+++IKQkBBatGiBk5MTO3fu5PPPP+eFF17QSKv86ynJExEREblL48aNmTVrFuvXr+eff/7Bx8eH+fPnM2jQoNwOTf6/KlWqkC9fPmbMmEFCQoKxGMujvp5BJC/QM3kiIiIiIiJ5iJ7JExERERERyUOU5ImIiIiIiOQheiZPRDIlNTWV8+fP4+zs/FAvmRURERGRR2M2m7l27RrFihXDyurB43VK8kQkU86fP4+Xl1duhyEiIiLyr3f27FlKlCjxwHIleSKSKc7OzsDt/1FxcXHJ5WhERERE/n0SEhLw8vIyfpc9iJI8EcmUtCmaLi4uSvJEREREclFGj85o4RUREREREZE8REmeiIiIiIhIHqIkT0REREREJA9RkiciIiIiIpKHKMkTERERERHJQ5TkiYiIiIiI5CFK8kRERERERPIQJXkiIiIiIiJ5iJI8ERERERGRPERJnoiIiIiISB6iJE9ERERERCQPUZInIiIiIiKShyjJExERERERyUOU5ImIiIiIiOQhSvJERERERETyECV5IiIiIiIieYiSPBERERERkTxESZ6IiIiIiEgeoiRPREREREQkD1GSJyIiIiIikofky+0AROTpUmnCZqzsHHI7DBEREZFcd3p6y9wO4b40kiciIiIiIpKHKMkTERERERHJQ5TkiYiIiIiI5CFK8kRERERERPIQJXkiIiIiIiJ5iJI8ERERERGRPERJ3lMmPDwck8nE1atXjX1r167Fx8cHa2trhg0bRkhICG5ubjkeS6NGjRg2bFiO95OmV69etGnT5rH1l1uyel0f1/0WERERkaeDyWw2m3M7CMm8GzducPnyZTw8PDCZTAB4eHjQu3dvhgwZgrOzM/ny5ePatWsUKVIkW/oMDw/nueee48qVKxbJxOXLl7GxscHZ2Tlb+slIfHw8ZrM5zyc0Wb2uf//9d5bvd6NGjahWrRpz5szJ9DEJCQm4urriNexLvSdPREREhMf/nry032Px8fG4uLg8sJ5ehv4UuXnzJra2tnh6ehr7EhMTuXjxIkFBQRQrVszYb29vn+PxFCxYMMf7uJOrq+tj7S+3ZPW62tvbP5b7LSIiIiJPB03XzCEff/wxxYoVIzU11WJ/69at6dOnDwDffvst1atXJ3/+/JQpU4aJEydy69Yto67JZGLhwoW89NJLODo6MmXKFIvpmuHh4cZoT+PGjTGZTISHh993+t53331HrVq1yJ8/P4ULF6Zt27ZG2fLly6lZsybOzs54enrStWtXLl68CMDp06d57rnnAChQoAAmk4levXoB904rvHLlCj169KBAgQI4ODjQvHlzTpw4YZSnxbV582b8/f1xcnKiWbNmxMXFZeqa3j1ds1GjRgwZMoRRo0ZRsGBBPD09CQ4Otjjm6tWr9O/fHw8PD/Lnz0+lSpVYv369Uf71119TsWJF7Ozs8Pb2ZtasWRbHe3t78+6779KjRw+cnJwoVaoU69at488//6R169Y4OTlRpUoV9u/fb3Hczp07qV+/Pvb29nh5eTFkyBCuX7+eqfN82OuaJjg4mGrVqrF8+XK8vb1xdXWlc+fOXLt2zbiO27dvZ+7cuZhMJkwmE6dPn85UbCIiIiLy5FOSl0M6dOjApUuXCAsLM/ZdvnyZTZs20a1bN3bs2EGPHj0YOnQov/zyC4sWLSIkJIQpU6ZYtBMcHEzbtm05fPiwkRymqVevHseOHQNuJytxcXHUq1fvnlg2bNhA27ZtadGiBQcPHiQ0NJTatWsb5Tdv3mTy5MlERUWxdu1aTp8+bSRyXl5efP311wAcO3aMuLg45s6de99z7tWrF/v372fdunXs3r0bs9lMixYtuHnzplEnKSmJmTNnsnz5cn788UdiY2MZOXJkFq6spaVLl+Lo6MiePXuYMWMGkyZNYuvWrQCkpqbSvHlzIiIi+N///scvv/zC9OnTsba2BuDnn3+mY8eOdO7cmcOHDxMcHMy4ceMICQmx6GP27NkEBgZy8OBBWrZsSffu3enRowevvPIKBw4coGzZsvTo0YO0mc8xMTE0a9aM9u3bc+jQIb744gt27tzJoEGDHuocM3Nd7xYTE8PatWtZv34969evZ/v27UyfPh2AuXPnUrduXfr160dcXBxxcXF4eXk9VGwiIiIi8uTRdM0cUqBAAZo3b87KlStp0qQJAKtXr6Zw4cI899xzvPDCC7z99tv07NkTgDJlyjB58mRGjRrFhAkTjHa6du1K7969je3ffvvN+NvW1tZ4DittJOt+pkyZQufOnZk4caKxr2rVqsbfdyaPZcqUYd68edSqVYvExEScnJyM6YNFihR54PNwJ06cYN26dURERBiJ5ooVK/Dy8mLt2rV06NABuJ1QfvTRR5QtWxaAQYMGMWnSpPQuZbqqVKliXK9y5cqxYMECQkNDef7559m2bRt79+4lOjoaX19f4/zSfPDBBzRp0oRx48YB4Ovryy+//ML7779vJLkALVq0oH///gCMHz+ehQsXUqtWLeOcRo8eTd26dfnjjz/w9PRk2rRpdOvWzRiNK1euHPPmzaNhw4YsXLiQ/PnzZ/r8Mntd75aamkpISIgx0tu9e3dCQ0OZMmUKrq6u2Nra4uDg8MDvDEBycjLJycnGdkJCQqbjFhEREZHco5G8HNStWze+/vpr44fyihUr6Ny5M1ZWVkRFRTFp0iScnJyMT9rISlJSktFGzZo1HzmOyMhII9G8n59//plWrVpRsmRJnJ2dadiwIQCxsbGZ7iM6Opp8+fJRp04dY1+hQoUoX7480dHRxj4HBwcjwQMoWrSoMTX0YVSpUsVi+872IiMjKVGihJHg3S/mwMBAi32BgYGcOHGClJSU+/bh4eEBQOXKle/Zl9ZvVFQUISEhFvc2KCiI1NRUTp06laXzy+x1vZu3t7fFwi0Pc52nTZuGq6ur8dFon4iIiMjTQSN5OahVq1aYzWY2bNhArVq12LFjB7NnzwZuL5gyceJE2rVrd89xd470ODo6PnIc6S3Kcf36dYKCgggKCmLFihW4u7sTGxtLUFAQN27ceOS+72ZjY2OxbTKZeJQFXu/XXtpzkNm1GMmdfaStaHq/fWn9JiYm0r9/f4YMGXJPWyVLlsyWmDKS3nXJrDFjxjBixAhjOyEhQYmeiIiIyFNASV4Oyp8/P+3atWPFihWcPHmS8uXLU716dQCqV6/OsWPH8PHxyfE4qlSpQmhoqMW0zzS//vorly5dYvr06cYP+LsXEbG1tQWwGN26m7+/P7du3WLPnj3GtMJLly5x7NgxKlSokF2nkiVVqlTh999/5/jx4/cdzfP39yciIsJiX0REBL6+vsZzew+jevXq/PLLL9lyb3Pqutra2qZ7PwHs7Oyws7N76D5EREREJHdoumYO69atGxs2bOCzzz6jW7duxv7x48ezbNkyJk6cyNGjR4mOjmbVqlWMHTs222OYMGECn3/+ORMmTCA6OprDhw/z3nvvAbdHlmxtbZk/fz6//fYb69atY/LkyRbHlypVCpPJxPr16/nzzz9JTEy8p49y5crRunVr+vXrx86dO4mKiuKVV16hePHitG7dOtvPKTMaNmxIgwYNaN++PVu3buXUqVNs3LiRTZs2AfDmm28SGhrK5MmTOX78OEuXLmXBggWPtBAM3H5Gb9euXQwaNIjIyEhOnDjBt99++1ALr+TUdfX29mbPnj2cPn2av/76K8ujfCIiIiLy5FKSl8MaN25MwYIFOXbsGF27djX2BwUFsX79erZs2UKtWrV45plnmD17NqVKlcr2GBo1asRXX33FunXrqFatGo0bN2bv3r0AuLu7ExISwldffUWFChWYPn06M2fOtDi+ePHiTJw4kbfffhsPD48HJitLliyhRo0avPjii9StWxez2cz3339/z9TBx+nrr7+mVq1adOnShQoVKjBq1ChjBKt69ep8+eWXrFq1ikqVKjF+/HgmTZpksejKw6hSpQrbt2/n+PHj1K9fn4CAAMaPH2/xHsOsyInrOnLkSKytralQoYIxRVdERERE8gaT+VEeiBKRbFe3bl2aNGnCu+++m9uhWEhISLi9AMuwL7Gyc8jtcERERERy3enpLR9rf2m/x+Lj43FxcXlgPY3kiTwhkpOT2b9/P0ePHqVixYq5HY6IiIiIPKW08Io8MZycnB5YtnHjRurXr/8Yo8kZO3bsoHnz5g8st7Ky4qWXXuLll19+jFGJiIiISF6iJE+eGJGRkQ8sK168+OMLJAfVrFkz3fN8HKutioiIiEjepiRPnhj/hgTH3t7+X3GeIiIiIpJ79EyeiIiIiIhIHqIkT0REREREJA/RdE0RyZIjE4PSXbJXRERERHKXRvJERERERETyECV5IiIiIiIieYiSPBERERERkTxESZ6IiIiIiEgeoiRPREREREQkD9HqmiKSJZUmbMbKziG3wxARERHJFaent8ztEDKkkTwREREREZE8REmeiIiIiIhIHqIkT0REREREJA9RkiciIiIiIpKHKMkTERERERHJQ5TkiYiIiIiI5CFK8vKgRo0aMWzYsEzVDQkJwc3NLUfjyS4mk4m1a9fmdhg56vTp05hMJiIjIzN9TFbut4iIiIjkfUryJEPBwcFUq1YtS8fkREIWFxdH8+bNs7XNJ42XlxdxcXFUqlQp08esWbOGyZMnZ7r+wySSIiIiIvL00MvQ5anh6emZ2yHkOGtr6yyfZ8GCBXMoGhERERF5Gmkk7yl3/fp1evTogZOTE0WLFmXWrFkW5cnJyYwcOZLixYvj6OhInTp1CA8Pv6edtWvXUq5cOfLnz09QUBBnz54Fbk/nnDhxIlFRUZhMJkwmEyEhIenG5O3tDUDbtm0xmUzGNsDChQspW7Ystra2lC9fnuXLl2f6XO8cHUwbjVqzZg3PPfccDg4OVK1ald27d1scExERQaNGjXBwcKBAgQIEBQVx5coV49oMGTKEIkWKkD9/fp599ln27dtnHBseHo7JZGLz5s0EBARgb29P48aNuXjxIhs3bsTf3x8XFxe6du1KUlKScVxqairTpk2jdOnS2NvbU7VqVVavXp2pc7zfKNv27dupXbs2dnZ2FC1alLfffptbt24Z5XdP1/T29mbq1Kn06dMHZ2dnSpYsyccff2yUly5dGoCAgABMJhONGjXKVGwiIiIi8nRQkveUe+utt9i+fTvffvstW7ZsITw8nAMHDhjlgwYNYvfu3axatYpDhw7RoUMHmjVrxokTJ4w6SUlJTJkyhWXLlhEREcHVq1fp3LkzAJ06deLNN9+kYsWKxMXFERcXR6dOndKNKS1RWrJkCXFxccb2N998w9ChQ3nzzTc5cuQI/fv3p3fv3oSFhT30+b/zzjuMHDmSyMhIfH196dKli5EARUZG0qRJEypUqMDu3bvZuXMnrVq1IiUlBYBRo0bx9ddfs3TpUg4cOICPjw9BQUFcvnzZoo/g4GAWLFjArl27OHv2LB07dmTOnDmsXLmSDRs2sGXLFubPn2/UnzZtGsuWLeOjjz7i6NGjDB8+nFdeeYXt27dn+fzOnTtHixYtqFWrFlFRUSxcuJBPP/2Ud999N93jZs2aRc2aNTl48CADBgzgjTfe4NixYwDs3bsXgG3bthEXF8eaNWuyHJeIiIiIPLk0XfMplpiYyKeffsr//vc/mjRpAsDSpUspUaIEALGxsSxZsoTY2FiKFSsGwMiRI9m0aRNLlixh6tSpANy8eZMFCxZQp04dow1/f3/27t1L7dq1cXJyIl++fJmeRuju7g6Am5ubxTEzZ86kV69eDBgwAIARI0bw008/MXPmTJ577rmHugYjR46kZcuWAEycOJGKFSty8uRJ/Pz8mDFjBjVr1uTDDz806lesWBG4PQK6cOFCQkJCjOf8Fi9ezNatW/n000956623jGPeffddAgMDAejbty9jxowhJiaGMmXKAPDyyy8TFhbG6NGjSU5OZurUqWzbto26desCUKZMGXbu3MmiRYto2LBhls7vww8/xMvLiwULFmAymfDz8+P8+fOMHj2a8ePHY2V1/3+nadGihXGdR48ezezZswkLC6N8+fLG/SlUqFC69zQ5OZnk5GRjOyEhIUuxi4iIiEju0EjeUywmJoYbN24YyRncfj6rfPnyABw+fJiUlBR8fX1xcnIyPtu3bycmJsY4Jl++fNSqVcvY9vPzw83Njejo6GyNNzo62kiW0gQGBj5SP1WqVDH+Llq0KAAXL14E/m8k735iYmK4efOmRTw2NjbUrl37nnju7MPDwwMHBwcjwUvbl9bnyZMnSUpK4vnnn7e45suWLbO45pkVHR1N3bp1MZlMxr7AwEASExP5/fffH3jcnTGbTCY8PT2NGDNr2rRpuLq6Gh8vL68sxy8iIiIij59G8vKwxMRErK2t+fnnn7G2trYoc3JyyqWospeNjY3xd1oilJqaCoC9vX2O9HHndtq+tD4TExMB2LBhA8WLF7eoZ2dnly3xZEZ6MWbWmDFjGDFihLGdkJCgRE9ERETkKaCRvKdY2bJlsbGxYc+ePca+K1eucPz4ceD2whopKSlcvHgRHx8fi8+d0/Ru3brF/v37je1jx45x9epV/P39AbC1tTWeY8ssGxube47x9/cnIiLCYl9ERAQVKlTIUtuZVaVKFUJDQ+9blrb4y53x3Lx5k3379j1SPBUqVMDOzo7Y2Nh7rvnDJEj+/v7s3r0bs9ls7IuIiMDZ2dmYlptVtra2ABneUzs7O1xcXCw+IiIiIvLk00jeU8zJyYm+ffvy1ltvUahQIYoUKcI777xjPKfl6+tLt27d6NGjB7NmzSIgIIA///yT0NBQqlSpYjzLZmNjw+DBg5k3bx758uVj0KBBPPPMM9SuXRu4vVrjqVOniIyMpESJEjg7O2c4KuXt7U1oaCiBgYHY2dlRoEAB3nrrLTp27EhAQABNmzblu+++Y82aNWzbti1Hrs+YMWOoXLkyAwYM4PXXX8fW1pawsDA6dOhA4cKFeeONN3jrrbcoWLAgJUuWZMaMGSQlJdG3b9+H7tPZ2ZmRI0cyfPhwUlNTefbZZ4mPjyciIgIXFxd69uyZpfYGDBjAnDlzGDx4MIMGDeLYsWNMmDCBESNGPPB5vIwUKVIEe3t7Nm3aRIkSJcifPz+urq4P1ZaIiIiIPHk0kveUe//996lfvz6tWrWiadOmPPvss9SoUcMoX7JkCT169ODNN9+kfPnytGnThn379lGyZEmjjoODA6NHj6Zr164EBgbi5OTEF198YZS3b9+eZs2a8dxzz+Hu7s7nn3+eYVyzZs1i69ateHl5ERAQAECbNm2YO3cuM2fOpGLFiixatIglS5bk2BL+vr6+bNmyhaioKGrXrk3dunX59ttvyZfv9r9tTJ8+nfbt29O9e3eqV6/OyZMn2bx5MwUKFHikfidPnsy4ceOYNm0a/v7+NGvWjA0bNhivLsiK4sWL8/3337N3716qVq3K66+/Tt++fRk7duxDx5cvXz7mzZvHokWLKFasGK1bt37otkRERETkyWMy3zkPTERy1bFjx/Dz8+PEiRP4+PjkdjgWEhISbi/AMuxLrOwccjscERERkVxxenrLXOs77fdYfHx8uo/SaCRP5Alx+fJlVq9ejYuLixY4EREREZGHpiRPsmzFihUWrwe485P2Hronoc0n0euvv/7A82zfvj2LFi1i4cKFj3UlThERERHJWzRdU7Ls2rVr/PHHH/cts7GxoVSpUk9Em0+iixcvPvCl4i4uLhQpUuQxR5R5mq4pIiIi8nRM19TqmpJlzs7OODs7P/FtPomKFCnyRCdyIiIiIvL003RNERERERGRPERJnoiIiIiISB6i6ZoikiVHJgalOwdcRERERHKXRvJERERERETyECV5IiIiIiIieYiSPBERERERkTxESZ6IiIiIiEgeoiRPREREREQkD9HqmiKSJZUmbMbKziG3wxARERHJFaent8ztEDKkkTwREREREZE8REmeiIiIiIhIHqIkT0REREREJA9RkiciIiIiIpKHKMkTERERERHJQ5TkiYiIiIiI5CFK8iRXhYeHYzKZuHr1aoZ1Q0JCcHNzy/GYcltwcDDVqlXLdP3Tp09jMpmIjIzMsZhERERE5OmhJE+eGp06deL48eO5HUaOGzlyJKGhoZmu7+XlRVxcHJUqVcr0MVlNJEVERETk6aGXoctTw97eHnt7+9wOI8c5OTnh5OSU6frW1tZ4enrmYEQiIiIi8jTRSF4etWnTJp599lnc3NwoVKgQL774IjExMcD/Te9bs2YNzz33HA4ODlStWpXdu3cbx6dNjdy8eTP+/v44OTnRrFkz4uLijDqNGjVi2LBhFv22adOGXr16GdvLly+nZs2aODs74+npSdeuXbl48eJDndPd0zXTRqOWL1+Ot7c3rq6udO7cmWvXrhl1UlNTmTFjBj4+PtjZ2VGyZEmmTJlilB8+fJjGjRtjb29PoUKFeO2110hMTDTKe/XqRZs2bZg6dSoeHh64ubkxadIkbt26xVtvvUXBggUpUaIES5YssYj17NmzdOzYETc3NwoWLEjr1q05ffp0ps7z7lG21NRUJk2aRIkSJbCzs6NatWps2rTJKL97umbaFNjQ0FBq1qyJg4MD9erV49ixY8Z1nDhxIlFRUZhMJkwmEyEhIZmKTURERESefEry8qjr168zYsQI9u/fT2hoKFZWVrRt25bU1FSjzjvvvMPIkSOJjIzE19eXLl26cOvWLaM8KSmJmTNnsnz5cn788UdiY2MZOXJkluK4efMmkydPJioqirVr13L69GmLJPBRxcTEsHbtWtavX8/69evZvn0706dPN8rHjBnD9OnTGTduHL/88gsrV67Ew8MDuH2NgoKCKFCgAPv27eOrr75i27ZtDBo0yKKPH374gfPnz/Pjjz/ywQcfMGHCBF588UUKFCjAnj17eP311+nfvz+///67cc5BQUE4OzuzY8cOIiIijCT5xo0bWT7HuXPnMmvWLGbOnMmhQ4cICgripZde4sSJE+ke98477zBr1iz2799Pvnz56NOnD3B72uubb75JxYoViYuLIy4ujk6dOmU5LhERERF5Mmm6Zh7Vvn17i+3PPvsMd3d3fvnlF2Mq4MiRI2nZsiUAEydOpGLFipw8eRI/Pz/gdrLy0UcfUbZsWQAGDRrEpEmTshRHWmIBUKZMGebNm0etWrVITEzM0pTEB0lNTSUkJARnZ2cAunfvTmhoKFOmTOHatWvMnTuXBQsW0LNnTwDKli3Ls88+C8DKlSv5559/WLZsGY6OjgAsWLCAVq1a8d577xnJYMGCBZk3bx5WVlaUL1+eGTNmkJSUxH/+8x/g/xLJnTt30rlzZ7744gtSU1P55JNPMJlMACxZsgQ3NzfCw8N54YUXsnSOM2fOZPTo0XTu3BmA9957j7CwMObMmcN///vfBx43ZcoUGjZsCMDbb79Ny5Yt+eeff7C3t8fJyYl8+fKlO80zOTmZ5ORkYzshISFLcYuIiIhI7tBIXh514sQJunTpQpkyZXBxccHb2xuA2NhYo06VKlWMv4sWLQpgMZXSwcHBSPDS6mR1quXPP/9Mq1atKFmyJM7OzkbScWccj8Lb29tI8O6OMTo6muTkZJo0aXLfY6Ojo6lataqR4AEEBgaSmppqTG0EqFixIlZW//efioeHB5UrVza2ra2tKVSokNFvVFQUJ0+exNnZ2Xi+rmDBgvzzzz/GlNnMSkhI4Pz58wQGBlrsDwwMJDo6Ot1jM7q/GZk2bRqurq7Gx8vLKwuRi4iIiEhu0UheHtWqVStKlSrF4sWLKVasGKmpqVSqVMliuqCNjY3xd9qI053TOe8sT6tjNpuNbSsrK4ttuD36lyZtOmRQUBArVqzA3d2d2NhYgoKCHmra4v3cL8a0c8iuRVru10d6/SYmJlKjRg1WrFhxT1vu7u7ZElNmZHR/MzJmzBhGjBhhbCckJCjRExEREXkKaCQvD7p06RLHjh1j7NixNGnSBH9/f65cuZLt/bi7u1ssxJKSksKRI0eM7V9//ZVLly4xffp06tevj5+f30MvuvIwypUrh729/QNfR+Dv709UVBTXr1839kVERBjTMh9W9erVOXHiBEWKFMHHx8fi4+rqmqW2XFxcKFasGBERERb7IyIiqFChwkPHaGtrS0pKSrp17OzscHFxsfiIiIiIyJNPSV4eVKBAAQoVKsTHH3/MyZMn+eGHHyxGZLJL48aN2bBhAxs2bODXX3/ljTfesHipecmSJbG1tWX+/Pn89ttvrFu3jsmTJ2d7HA+SP39+Ro8ezahRo1i2bBkxMTH89NNPfPrppwB069aN/Pnz07NnT44cOUJYWBiDBw+me/fuxvN4D6Nbt24ULlyY1q1bs2PHDk6dOkV4eDhDhgwxFmfJirfeeov33nuPL774gmPHjvH2228TGRnJ0KFDHzpGb29vTp06RWRkJH/99ZfFs3ciIiIi8nRTkpcHWVlZsWrVKn7++WcqVarE8OHDef/997O9nz59+tCzZ0969OhBw4YNKVOmDM8995xR7u7uTkhICF999RUVKlRg+vTpzJw5M9vjSM+4ceN48803GT9+PP7+/nTq1MkYTXRwcGDz5s1cvnyZWrVq8fLLL9OkSRMWLFjwSH06ODjw448/UrJkSdq1a4e/vz99+/bln3/+eajRsCFDhjBixAjefPNNKleuzKZNm1i3bh3lypV76Bjbt29Ps2bNeO6553B3d+fzzz9/6LZERERE5MliMt/9UJWI5KoxY8awY8cOdu7cmduhWEhISLi9AMuwL7Gyc8jtcERERERyxenpLXOt77TfY/Hx8ekOHmgkT+QJYTabiYmJITQ0lIoVK+Z2OCIiIiLylFKSJ0+M5s2bG68cuPszderU3A4v2zzoHJ2dnfH19cXW1tZ4B5+IiIiISFbpFQryxPjkk0/4+++/71tWsGDBxxxNzomMjHxgWfHixbPt1Q8iIiIi8u+kJE+eGMWLF8/tEB4LHx+f3A5BRERERPIwTdcUERERERHJQ5TkiYiIiIiI5CGarikiWXJkYtBDve9PRERERB4PjeSJiIiIiIjkIUryRERERERE8hAleSIiIiIiInmIkjwREREREZE8REmeiIiIiIhIHqLVNUUkSypN2IyVnUNuhyEiIiJPuNPTW+Z2CP9aGskTERERERHJQ5TkiYiIiIiI5CFK8kRERERERPIQJXkiIiIiIiJ5iJI8ERERERGRPERJnoiIiIiISB6iJE/uERwcTLVq1f41/T5up0+fxmQyERkZmWHd8PBwTCYTV69ezfG4RERERCRvUJL3L2cymVi7dm1uhwHAyJEjCQ0Nze0wDNeuXWPYsGGUKlUKe3t76tWrx759+yzqmM1mxo8fT9GiRbG3t6dp06acOHEilyK+rVGjRgwbNixXYxARERGR3KMkT54YTk5OFCpUKLfDMLz66qts3bqV5cuXc/jwYV544QWaNm3KuXPnjDozZsxg3rx5fPTRR+zZswdHR0eCgoL4559/cjFyEREREfk3U5L3hGjUqBGDBw9m2LBhFChQAA8PDxYvXsz169fp3bs3zs7O+Pj4sHHjRuOY7du3U7t2bezs7ChatChvv/02t27dsmhzyJAhjBo1ioIFC+Lp6UlwcLBR7u3tDUDbtm0xmUzGdprly5fj7e2Nq6srnTt35tq1a0bZ6tWrqVy5Mvb29hQqVIimTZty/fr1DM8zPDyc2rVr4+joiJubG4GBgZw5cwa4d7pmr169aNOmDTNnzqRo0aIUKlSIgQMHcvPmTaNOcnIyo0ePxsvLCzs7O3x8fPj000+N8iNHjtC8eXOcnJzw8PCge/fu/PXXXxnG+ffff/P1118zY8YMGjRogI+PD8HBwfj4+LBw4ULg9ijenDlzGDt2LK1bt6ZKlSosW7aM8+fPW4yO7t27l4CAAPLnz0/NmjU5ePBghv0/yKVLl+jSpQvFixfHwcGBypUr8/nnn1tcs+3btzN37lxMJhMmk4nTp08/0rUQERERkaeLkrwnyNKlSylcuDB79+5l8ODBvPHGG3To0IF69epx4MABXnjhBbp3705SUhLnzp2jRYsW1KpVi6ioKBYuXMinn37Ku+++e0+bjo6O7NmzhxkzZjBp0iS2bt0KYEw9XLJkCXFxcRZTEWNiYli7di3r169n/fr1bN++nenTpwMQFxdHly5d6NOnD9HR0YSHh9OuXTvMZnO653fr1i3atGlDw4YNOXToELt37+a1117DZDI98JiwsDBiYmIICwtj6dKlhISEEBISYpT36NGDzz//nHnz5hEdHc2iRYtwcnIC4OrVqzRu3JiAgAD279/Ppk2b+OOPP+jYsWOG9+LWrVukpKSQP39+i/329vbs3LkTgFOnTnHhwgWaNm1qlLu6ulKnTh12794NQGJiIi+++CIVKlTg559/Jjg4mJEjR2bY/4P8888/1KhRgw0bNnDkyBFee+01unfvzt69ewGYO3cudevWpV+/fsTFxREXF4eXl9cjXQsRERERebrky+0A5P9UrVqVsWPHAjBmzBimT59O4cKF6devHwDjx49n4cKFHDp0iO+++w4vLy8WLFiAyWTCz8+P8+fPM3r0aMaPH4+V1e38vUqVKkyYMAGAcuXKsWDBAkJDQ3n++edxd3cHwM3NDU9PT4tYUlNTCQkJwdnZGYDu3bsTGhrKlClTiIuL49atW7Rr145SpUoBULly5QzPLyEhgfj4eF588UXKli0LgL+/f7rHFChQgAULFmBtbY2fnx8tW7YkNDSUfv36cfz4cb788ku2bt1qJFplypQxjl2wYAEBAQFMnTrV2PfZZ5/h5eXF8ePH8fX1fWC/zs7O1K1bl8mTJ+Pv74+Hhweff/45u3fvxsfHB4ALFy4A4OHhYXGsh4eHUbZy5UpSU1P59NNPyZ8/PxUrVuT333/njTfeyPB63U/x4sUtksTBgwezefNmvvzyS2rXro2rqyu2trY4ODhY3NOHuRbJyckkJycb2wkJCQ8Vs4iIiIg8XhrJe4JUqVLF+Nva2ppChQpZJE9pycTFixeJjo6mbt26FqNggYGBJCYm8vvvv9+3TYCiRYty8eLFDGPx9vY2Ery7j6tatSpNmjShcuXKdOjQgcWLF3PlypUM2yxYsCC9evUiKCiIVq1aMXfuXOLi4tI9pmLFilhbW983jsjISKytrWnYsOF9j42KiiIsLAwnJyfj4+fnB9weqczI8uXLMZvNFC9eHDs7O+bNm0eXLl2MBDozoqOjqVKlisWIYN26dTN9/N1SUlKYPHkylStXpmDBgjg5ObF582ZiY2PTPe5hrsW0adNwdXU1Pl5eXg8dt4iIiIg8PkryniA2NjYW2yaTyWJfWkKXmpr6SG1m5vj0jrO2tmbr1q1s3LiRChUqMH/+fMqXL8+pU6cybHfJkiXs3r2bevXq8cUXX+Dr68tPP/30UHHY29un21diYiKtWrUiMjLS4nPixAkaNGiQYaxly5Zl+/btJCYmcvbsWfbu3cvNmzeN0cK0kbI//vjD4rg//vjjnpHR7PL+++8zd+5cRo8eTVhYGJGRkQQFBXHjxo10j3uYazFmzBji4+ONz9mzZ3PilEREREQkmynJe0r5+/uze/dui+fgIiIicHZ2pkSJEplux8bGhpSUlCz3bzKZCAwMZOLEiRw8eBBbW1u++eabTB0bEBDAmDFj2LVrF5UqVWLlypVZ7h9uTxFNTU1l+/bt9y2vXr06R48exdvbGx8fH4uPo6NjpvtxdHSkaNGiXLlyhc2bN9O6dWsASpcujaenp8VrHxISEtizZ48xWufv78+hQ4csVttML6nNSEREBK1bt+aVV16hatWqlClThuPHj1vUsbW1veeePsy1sLOzw8XFxeIjIiIiIk8+JXlPqQEDBnD27FkGDx7Mr7/+yrfffsuECRMYMWJElqYTent7ExoayoULFzI15RJgz549TJ06lf379xMbG8uaNWv4888/M3y+7tSpU4wZM4bdu3dz5swZtmzZwokTJzI8Lr3Ye/bsSZ8+fVi7di2nTp0iPDycL7/8EoCBAwdy+fJlunTpwr59+4iJiWHz5s307t07U4nt5s2b2bRpE6dOnWLr1q0899xz+Pn50bt3b+B2ojts2DDeffdd1q1bx+HDh+nRowfFihWjTZs2AHTt2hWTyUS/fv345Zdf+P7775k5c+ZDnS/cfq5y69at7Nq1i+joaPr373/PSKK3tzd79uzh9OnT/PXXX6Smpj7ytRARERGRp4eSvKdU8eLF+f7779m7dy9Vq1bl9ddfp2/fvsbCLZk1a9Ystm7dipeXFwEBAZk6xsXFhR9//JEWLVrg6+vL2LFjmTVrFs2bN0/3OAcHB3799Vfat2+Pr68vr732GgMHDqR///5ZivlOCxcu5OWXX2bAgAH4+fnRr18/41UOxYoVIyIigpSUFF544QUqV67MsGHDcHNzy1QiHB8fz8CBA/Hz86NHjx48++yzbN682WIK6ahRoxg8eDCvvfYatWrVIjExkU2bNhnP4Dk5OfHdd99x+PBhAgICeOedd3jvvfce+nzHjh1L9erVCQoKolGjRnh6ehoJZZqRI0dibW1NhQoVcHd3JzY29pGvhYiIiIg8PUzmjNa9FxHh9lRUV1dXvIZ9iZWdQ26HIyIiIk+409Nb5nYIeU7a77H4+Ph0H6XRP+GLiIiIiIjkIUryJFvduUT/3Z8dO3bkdniG2NjYdGPN6JUE2eX1119/YAyvv/76Y4lBRERERPIWvQxdslVkZOQDy4oXL/74AslAsWLF0o21WLFijyWOSZMmWbzc/E5azVJEREREHoaSPMlWPj4+uR1CpuTLl++JiLVIkSIUKVIkt8MQERERkTxE0zVFRERERETyECV5IiIiIiIieYima4pIlhyZGKTnBUVERESeYBrJExERERERyUOU5ImIiIiIiOQhSvJERERERETyECV5IiIiIiIieYiSPBERERERkTxEq2uKSJZUmrAZKzuH3A5DRERywenpLXM7BBHJBI3kiYiIiIiI5CFK8kRERERERPIQJXkiIiIiIiJ5iJI8ERERERGRPERJnoiIiIiISB6iJE9ERERERCQPUZKXRwUHB1OtWrV/Tb8P0qhRI4YNG5bbYVjo1asXbdq0yXT98PBwTCYTV69eBSAkJAQ3N7cciU1EREREnn5K8vIAk8nE2rVrczsMAEaOHEloaGhuh5EjTp8+jclkIjIyMlfj6NSpE8ePH8/VGERERETkyaWXoUu2cnJywsnJKbfDyNPs7e2xt7fP7TBERERE5Amlkbxs1KhRIwYPHsywYcMoUKAAHh4eLF68mOvXr9O7d2+cnZ3x8fFh48aNxjHbt2+ndu3a2NnZUbRoUd5++21u3bpl0eaQIUMYNWoUBQsWxNPTk+DgYKPc29sbgLZt22IymYztNMuXL8fb2xtXV1c6d+7MtWvXjLLVq1dTuXJl7O3tKVSoEE2bNuX69esZnmd4eDi1a9fG0dERNzc3AgMDOXPmDHDvdM20qYkzZ86kaNGiFCpUiIEDB3Lz5k2jTnJyMqNHj8bLyws7Ozt8fHz49NNPjfIjR47QvHlznJyc8PDwoHv37vz1118Zxnk/y5cvp2bNmjg7O+Pp6UnXrl25ePGiUX7lyhW6deuGu7s79vb2lCtXjiVLlgBQunRpAAICAjCZTDRq1CjD/lJSUhgxYgRubm4UKlSIUaNGYTabLeqkpqYybdo0Spcujb29PVWrVmX16tUPbPPu6Zpp1zy9e339+nV69OiBk5MTRYsWZdasWU/kVFYREREReXRK8rLZ0qVLKVy4MHv37mXw4MG88cYbdOjQgXr16nHgwAFeeOEFunfvTlJSEufOnaNFixbUqlWLqKgoFi5cyKeffsq77757T5uOjo7s2bOHGTNmMGnSJLZu3QrAvn37AFiyZAlxcXHGNkBMTAxr165l/fr1rF+/nu3btzN9+nQA4uLi6NKlC3369CE6Oprw8HDatWt3TwJyt1u3btGmTRsaNmzIoUOH2L17N6+99homk+mBx4SFhRETE0NYWBhLly4lJCSEkJAQo7xHjx58/vnnzJs3j+joaBYtWmSMBl69epXGjRsTEBDA/v372bRpE3/88QcdO3bM/E25w82bN5k8eTJRUVGsXbuW06dP06tXL6N83Lhx/PLLL2zcuJHo6GgWLlxI4cKFAdi7dy8A27ZtIy4ujjVr1mTY36xZswgJCeGzzz5j586dXL58mW+++caizrRp01i2bBkfffQRR48eZfjw4bzyyits37490+eV3r0GeOutt9i+fTvffvstW7ZsITw8nAMHDmS6fRERERF5emi6ZjarWrUqY8eOBWDMmDFMnz6dwoUL069fPwDGjx/PwoULOXToEN999x1eXl4sWLAAk8mEn58f58+fZ/To0YwfPx4rq9s5eJUqVZgwYQIA5cqVY8GCBYSGhvL888/j7u4OgJubG56enhaxpKamEhISgrOzMwDdu3cnNDSUKVOmEBcXx61bt2jXrh2lSpUCoHLlyhmeX0JCAvHx8bz44ouULVsWAH9//3SPKVCgAAsWLMDa2ho/Pz9atmxJaGgo/fr14/jx43z55Zds3bqVpk2bAlCmTBnj2AULFhAQEMDUqVONfZ999hleXl4cP34cX1/fDGO+U58+fYy/y5Qpw7x586hVqxaJiYk4OTkRGxtLQEAANWvWBLAYGU271oUKFbrnWj/InDlzGDNmDO3atQPgo48+YvPmzUZ5cnIyU6dOZdu2bdStW9eIa+fOnSxatIiGDRtmqp/07nViYiKffvop//vf/2jSpAlw+x8OSpQokW6bycnJJCcnG9sJCQmZikVEREREcpdG8rJZlSpVjL+tra0pVKiQRfLk4eEBwMWLF4mOjqZu3boWo2CBgYEkJiby+++/37dNgKJFi1pMMXwQb29v40f/3cdVrVqVJk2aULlyZTp06MDixYu5cuVKhm0WLFiQXr16ERQURKtWrZg7dy5xcXHpHlOxYkWsra3vG0dkZCTW1tYPTGaioqIICwsznvVzcnLCz88PuD16lVU///wzrVq1omTJkjg7Oxv9xsbGAvDGG2+watUqqlWrxqhRo9i1a1eW+0gTHx9PXFwcderUMfbly5fPSCABTp48SVJSEs8//7zFOS5btixL55fevY6JieHGjRsWcRQsWJDy5cun2+a0adNwdXU1Pl5eXpmOR0RERERyj5K8bGZjY2OxbTKZLPalJXSpqamP1GZmjk/vOGtra7Zu3crGjRupUKEC8+fPp3z58pw6dSrDdpcsWcLu3bupV68eX3zxBb6+vvz0008PFUdGC4gkJibSqlUrIiMjLT4nTpygQYMGGcZ6p+vXrxMUFISLiwsrVqxg3759xtTJGzduANC8eXPOnDnD8OHDOX/+PE2aNGHkyJFZ6icrEhMTAdiwYYPF+f3yyy/pPpd3t4f9jqRnzJgxxMfHG5+zZ88+UnsiIiIi8ngoyctF/v7+7N692+I5uIiICJydnTOcSncnGxsbUlJSsty/yWQiMDCQiRMncvDgQWxtbe95XuxBAgICGDNmDLt27aJSpUqsXLkyy/3D7SmiqampD3z+rHr16hw9ehRvb298fHwsPo6Ojlnq69dff+XSpUtMnz6d+vXr4+fnd98RUXd3d3r27Mn//vc/5syZw8cffwyAra0tQKavtaurK0WLFmXPnj3Gvlu3bvHzzz8b2xUqVMDOzo7Y2Nh7zi+7Rs7Kli2LjY2NRRxXrlzJ8DUMdnZ2uLi4WHxERERE5MmnJC8XDRgwgLNnzzJ48GB+/fVXvv32WyZMmMCIESOM5/Eyw9vbm9DQUC5cuJCpKZcAe/bsYerUqezfv5/Y2FjWrFnDn3/+meHzdadOnWLMmDHs3r2bM2fOsGXLFk6cOJHhcenF3rNnT/r06cPatWs5deoU4eHhfPnllwAMHDiQy5cv06VLF/bt20dMTAybN2+md+/eWU5sS5Ysia2tLfPnz+e3335j3bp1TJ482aLO+PHj+fbbbzl58iRHjx5l/fr1xrkVKVIEe3t7Y/GX+Pj4DPscOnQo06dPZ+3atfz6668MGDDAeKk5gLOzMyNHjmT48OEsXbqUmJgYDhw4wPz581m6dGmWzu9BnJyc6Nu3L2+99RY//PADR44coVevXln6jomIiIjI00O/8nJR8eLF+f7779m7dy9Vq1bl9ddfp2/fvsbCLZk1a9Ystm7dipeXFwEBAZk6xsXFhR9//JEWLVrg6+vL2LFjmTVrFs2bN0/3OAcHB3799Vfat2+Pr68vr732GgMHDqR///5ZivlOCxcu5OWXX2bAgAH4+fnRr18/41UOxYoVIyIigpSUFF544QUqV67MsGHDcHNzy3KS4u7uTkhICF999RUVKlRg+vTpzJw506KOra0tY8aMoUqVKjRo0ABra2tWrVoF3H6ebt68eSxatIhixYrRunXrDPt888036d69Oz179qRu3bo4OzvTtm1bizqTJ09m3LhxTJs2DX9/f5o1a8aGDRuMVzZkh/fff5/69evTqlUrmjZtyrPPPkuNGjWyrX0REREReXKYzBmtmS8ieVKjRo2oVq0ac+bMyVT9hISE2wuwDPsSKzuHnA1ORESeSKent8ztEET+1dJ+j8XHx6f7KI1G8kRERERERPIQJXlyjzuX8r/7s2PHjtwOzxAbG5turGmvRchJT8u1EhEREZF/D70MXe4RGRn5wLLixYs/vkAyUKxYsXRjLVasWI7H8LRcq/sJDw/P7RBEREREJAcoyZN7+Pj45HYImZIvX75cjzW3+xcRERERuZuma4qIiIiIiOQhSvJERERERETyEE3XFJEsOTIxKN0le0VEREQkd2kkT0REREREJA9RkiciIiIiIpKHKMkTERERERHJQ5TkiYiIiIiI5CFK8kRERERERPIQra4pIllSacJmrOwccjuMPOf09Ja5HYKIiIjkERrJExERERERyUOU5ImIiIiIiOQhSvJERERERETyECV5IiIiIiIieYiSPBERERERkTxESZ6IiIiIiEgeoiTv/wsODqZatWr/mn4fpFGjRgwbNiy3w3gsTCYTa9euBeD06dOYTCYiIyMfur3cunZP2ndIRERERHLXvzLJu/PHfW4bOXIkoaGhuR1GjsiOxOlx8fLyIi4ujkqVKgEQHh6OyWTi6tWrmW5jzZo1TJ48OYcifLC8/B0SERERkazTy9BzmZOTE05OTrkdxlPr5s2b2NjYPHI71tbWeHp6PlIbBQsWfOQ4HkZG36EbN25ga2v7GCMSERERkdyUqyN5jRo1YvDgwQwbNowCBQrg4eHB4sWLuX79Or1798bZ2RkfHx82btxoHLN9+3Zq166NnZ0dRYsW5e233+bWrVsWbQ4ZMoRRo0ZRsGBBPD09CQ4ONsq9vb0BaNu2LSaTydhOs3z5cry9vXF1daVz585cu3bNKFu9ejWVK1fG3t6eQoUK0bRpU65fv57heYaHh1O7dm0cHR1xc3MjMDCQM2fOAPdOtevVqxdt2rRh5syZFC1alEKFCjFw4EBu3rxp1ElOTmb06NF4eXlhZ2eHj48Pn376qVF+5MgRmjdvjpOTEx4eHnTv3p2//vorwzjvZ/ny5dSsWRNnZ2c8PT3p2rUrFy9eNMqvXLlCt27dcHd3x97ennLlyrFkyRIASpcuDUBAQAAmk4lGjRplqs/PPvuMihUrGvd40KBBRpnJZGLhwoW89NJLODo6MmXKFAC+/fZbqlevTv78+SlTpgwTJ060+F6cOHGCBg0akD9/fipUqMDWrVst+rxz1PH06dM899xzABQoUACTyUSvXr0yjPvu6Zre3t5MnTqVPn364OzsTMmSJfn4448tjtm7dy8BAQHkz5+fmjVr8s0331iMfoaEhODm5mZxzNq1azGZTMb2g75DU6ZMoVixYpQvX55JkyYZo5R3qlatGuPGjcvw3ERERETk6ZHr0zWXLl1K4cKF2bt3L4MHD+aNN96gQ4cO1KtXjwMHDvDCCy/QvXt3kpKSOHfuHC1atKBWrVpERUWxcOFCPv30U95999172nR0dGTPnj3MmDGDSZMmGT/q9+3bB8CSJUuIi4sztgFiYmJYu3Yt69evZ/369Wzfvp3p06cDEBcXR5cuXejTpw/R0dGEh4fTrl07zGZzuud369Yt2rRpQ8OGDTl06BC7d+/mtddes/iRfrewsDBiYmIICwtj6dKlhISEEBISYpT36NGDzz//nHnz5hEdHc2iRYuMkZyrV6/SuHFjAgIC2L9/P5s2beKPP/6gY8eOmb8pd7h58yaTJ08mKiqKtWvXcvr0aYuEZ9y4cfzyyy9s3LiR6OhoFi5cSOHChYHbCQzAtm3biIuLY82aNRn2t3DhQgYOHMhrr73G4cOHWbduHT4+PhZ1goODadu2LYcPH6ZPnz7s2LGDHj16MHToUH755RcWLVpESEiIkQCmpqbSrl07bG1t2bNnDx999BGjR49+YAxeXl58/fXXABw7doy4uDjmzp2bpeuWZtasWdSsWZODBw8yYMAA3njjDY4dOwZAYmIiL774IhUqVODnn38mODiYkSNHPlQ/dwsNDeXYsWNs3bqV9evXG9/bO7/vBw8e5NChQ/Tu3Ttb+hQRERGRJ0OuT9esWrUqY8eOBWDMmDFMnz6dwoUL069fPwDGjx/PwoULOXToEN999x1eXl4sWLAAk8mEn58f58+fZ/To0YwfPx4rq9s5a5UqVZgwYQIA5cqVY8GCBYSGhvL888/j7u4OgJub2z3T81JTUwkJCcHZ2RmA7t27ExoaypQpU4iLi+PWrVu0a9eOUqVKAVC5cuUMzy8hIYH4+HhefPFFypYtC4C/v3+6xxQoUIAFCxZgbW2Nn58fLVu2JDQ0lH79+nH8+HG+/PJLtm7dStOmTQEoU6aMceyCBQsICAhg6tSpxr7PPvsMLy8vjh8/jq+vb4Yx36lPnz7G32XKlGHevHnUqlWLxMREnJyciI2NJSAggJo1awJYjIymXetChQpleirku+++y5tvvsnQoUONfbVq1bKo07VrV4vEpE+fPrz99tv07NnTiHPy5MmMGjWKCRMmsG3bNn799Vc2b95MsWLFAJg6dSrNmze/bwzW1tbG1MsiRYrcM5KWFS1atGDAgAEAjB49mtmzZxMWFkb58uVZuXIlqampfPrpp+TPn5+KFSvy+++/88Ybbzx0f2kcHR355JNPLKZpBgUFsWTJEuN6LlmyhIYNG1p8f+6UnJxMcnKysZ2QkPDIcYmIiIhIzsv1kbwqVaoYf1tbW1OoUCGL5MnDwwOAixcvEh0dTd26dS1GwQIDA0lMTOT333+/b5sARYsWtZhi+CDe3t5Ggnf3cVWrVqVJkyZUrlyZDh06sHjxYq5cuZJhmwULFqRXr14EBQXRqlUr5s6dS1xcXLrHVKxYEWtr6/vGERkZibW1NQ0bNrzvsVFRUYSFhRnPaTk5OeHn5wfcHqnMqp9//plWrVpRsmRJnJ2djX5jY2MBeOONN1i1ahXVqlVj1KhR7Nq1K8t9pLl48SLnz5+nSZMm6dZLSyjTREVFMWnSJItz7tevH3FxcSQlJREdHY2Xl5eR4AHUrVv3oePMiju/iyaTCU9PT+NeRkdHU6VKFfLnz5/tcVWuXPme5/D69evH559/zj///MONGzdYuXKlRRJ/t2nTpuHq6mp8vLy8siU2EREREclZuZ7k3b1ohslkstiXltClpqY+UpuZOT6946ytrdm6dSsbN26kQoUKzJ8/n/Lly3Pq1KkM212yZAm7d++mXr16fPHFF/j6+vLTTz89VBz29vbp9pWYmEirVq2IjIy0+KQ9k5YV169fJygoCBcXF1asWMG+ffv45ptvgNuLeQA0b96cM2fOMHz4cCNBe9gphxmdWxpHR0eL7cTERCZOnGhxvocPH+bEiRMWCVRueNjvYhorK6t7pgTf+Xzmg9x9jQBatWqFnZ0d33zzDd999x03b97k5ZdffmAbY8aMIT4+3vicPXs203GLiIiISO7J9SQvK/z9/dm9e7fFj96IiAicnZ0pUaJEptuxsbEhJSUly/2bTCYCAwOZOHEiBw8exNbW1kh6MhIQEMCYMWPYtWsXlSpVYuXKlVnuH26P0KSmprJ9+/b7llevXp2jR4/i7e2Nj4+Pxed+P/zT8+uvv3Lp0iWmT59O/fr18fPzu++IqLu7Oz179uR///sfc+bMMRYXSRtJyuy1dnZ2xtvbO8uvA6hevTrHjh2753x9fHywsrLC39+fs2fPWoygppdkP0zsD8Pf359Dhw7xzz//PDAud3d3rl27ZrHAz8O+kiJfvnz07NmTJUuWsGTJEjp37pxuYm1nZ4eLi4vFR0RERESefE9VkjdgwADOnj3L4MGD+fXXX/n222+ZMGECI0aMMJ7Hy4y0ROLChQuZmnIJsGfPHqZOncr+/fuJjY1lzZo1/Pnnnxk+X3fq1CnGjBnD7t27OXPmDFu2bOHEiRMZHpde7D179qRPnz6sXbuWU6dOER4ezpdffgnAwIEDuXz5Ml26dGHfvn3ExMSwefNmevfuneWEpWTJktja2jJ//nx+++031q1bd8974MaPH8+3337LyZMnOXr0KOvXrzfOrUiRItjb2xuLv8THx2fYZ3BwMLNmzWLevHmcOHGCAwcOMH/+/HSPGT9+PMuWLWPixIkcPXqU6OhoVq1aZTzr2bRpU3x9fenZsydRUVHs2LGDd955J902S5UqhclkYv369fz5558kJiZmGHtWde3aFZPJRL9+/fjll1/4/vvvmTlzpkWdOnXq4ODgwH/+8x9iYmJYuXKlxSI8WfXqq6/yww8/sGnTpnSnaoqIiIjI0+upSvKKFy/O999/z969e6latSqvv/46ffv2NX7MZ9asWbPYunUrXl5eBAQEZOoYFxcXfvzxR1q0aIGvry9jx45l1qxZD1y8I42DgwO//vor7du3x9fXl9dee42BAwfSv3//LMV8p4ULF/Lyyy8zYMAA/Pz86NevnzHSU6xYMSIiIkhJSeGFF16gcuXKDBs2DDc3tywlwnB7FCkkJISvvvqKChUqMH369HuSEFtbW8aMGUOVKlVo0KAB1tbWrFq1Crg9cjRv3jwWLVpEsWLFaN26dYZ99uzZkzlz5vDhhx9SsWJFXnzxRU6cOJHuMUFBQaxfv54tW7ZQq1YtnnnmGWbPnm0skGNlZcU333zD33//Te3atXn11VeNlTcfpHjx4kycOJG3334bDw8Pi9c4ZBcnJye+++47Dh8+TEBAAO+88w7vvfeeRZ2CBQvyv//9j++//57KlSvz+eefW7wSJKvKlStHvXr18PPzo06dOo94BiIiIiLyJDKZM3oHgIg8NqdPn6Z06dIcPHjQ4t132cVsNlOuXDkGDBjAiBEjsnRsQkLC7QVYhn2JlZ1Dtsf2b3d6esvcDkFERESecGm/x+Lj49N9lCbXX6EgIo/Hn3/+yapVq7hw4YLejSciIiKShynJywZpLyK/n40bN1K/fv3HGM2DxcbGUqFChQeW//LLL5QsWTJHY3hartXdnoRr96iKFClC4cKF+fjjjylQoEBuhyMiIiIiOURJXjZIb7XD4sWLP75AMlCsWLF0Y73zPXI55Wm5Vnd7XNfO29v7nlcmZBfNzBYRERH5d1CSlw18fHxyO4RMyZcvX67Hmtv9P6wn4dqJiIiIiGTGU7W6poiIiIiIiKRPSZ6IiIiIiEgeoumaIpIlRyYGpbtkr4iIiIjkLo3kiYiIiIiI5CFK8kRERERERPIQJXkiIiIiIiJ5iJI8ERERERGRPERJnoiIiIiISB6i1TVFJEsqTdiMlZ1DboeRY05Pb5nbIYiIiIg8Eo3kiYiIiIiI5CFK8kRERERERPIQJXkiIiIiIiJ5iJI8ERERERGRPERJnoiIiIiISB6iJE9ERERERCQPUZIn8pQJCQnBzc0tw3omk4m1a9fmeDwiIiIi8mRRkifyBPP29mbOnDkW+zp16sTx48eN7eDgYKpVq/Z4AxMRERGRJ5Zehi7ylLG3t8fe3j63wxARERGRJ5RG8kT+v9TUVKZNm0bp0qWxt7enatWqrF69GoDw8HBMJhObN28mICAAe3t7GjduzMWLF9m4cSP+/v64uLjQtWtXkpKSjDYbNWrEoEGDGDRoEK6urhQuXJhx48ZhNpszjKdRo0acOXOG4cOHYzKZMJlMgOV0zZCQECZOnEhUVJRRJyQk5L7tnT17lo4dO+Lm5kbBggVp3bo1p0+ffqRrJiIiIiJPHiV5Iv/ftGnTWLZsGR999BFHjx5l+PDhvPLKK2zfvt2oExwczIIFC9i1a5eRNM2ZM4eVK1eyYcMGtmzZwvz58y3aXbp0Kfny5WPv3r3MnTuXDz74gE8++STDeNasWUOJEiWYNGkScXFxxMXF3VOnU6dOvPnmm1SsWNGo06lTp3vq3bx5k6CgIJydndmxYwcRERE4OTnRrFkzbty48RBXS0RERESeVJquKQIkJyczdepUtm3bRt26dQEoU6YMO3fuZNGiRbz22msAvPvuuwQGBgLQt29fxowZQ0xMDGXKlAHg5ZdfJiwsjNGjRxtte3l5MXv2bEwmE+XLl+fw4cPMnj2bfv36pRtTwYIFsba2xtnZGU9Pz/vWsbe3x8nJiXz58j2wDsAXX3xBamoqn3zyiTEiuGTJEtzc3AgPD+eFF1647zVJTk42thMSEtKNV0RERESeDBrJEwFOnjxJUlISzz//PE5OTsZn2bJlxMTEGPWqVKli/O3h4YGDg4OR4KXtu3jxokXbzzzzjJFYAdStW5cTJ06QkpKSg2dkKSoqipMnT+Ls7GycW8GCBfnnn38szu9O06ZNw9XV1fh4eXk9tnhFRERE5OFpJE8ESExMBGDDhg0UL17coszOzs5IhGxsbIz9JpPJYjttX2pqag5Hm3WJiYnUqFGDFStW3FPm7u5+32PGjBnDiBEjjO2EhAQleiIiIiJPASV5IkCFChWws7MjNjaWhg0b3lP+oNGuzNizZ4/F9k8//US5cuWwtrbO8FhbW9sMR/wyU6d69ep88cUXFClSBBcXl4yD5nZya2dnl6m6IiIiIvLk0HRNEcDZ2ZmRI0cyfPhwli5dSkxMDAcOHGD+/PksXbr0kdqOjY1lxIgRHDt2jM8//5z58+czdOjQTB3r7e3Njz/+yLlz5/jrr78eWOfUqVNERkby119/WTxHl6Zbt24ULlyY1q1bs2PHDk6dOkV4eDhDhgzh999/f6TzExEREZEni5I8kf9v8uTJjBs3jmnTpuHv70+zZs3YsGEDpUuXfqR2e/Towd9//03t2rUZOHAgQ4cONRZyycikSZM4ffo0ZcuWfeC0yvbt29OsWTOee+453N3d+fzzz++p4+DgwI8//kjJkiVp164d/v7+9O3bl3/++SfTI3siIiIi8nQwmTPzwq677Nixg0WLFhETE8Pq1aspXrw4y5cvp3Tp0jz77LM5EafIU6lRo0ZUq1aNOXPm5HYojywhIeH2AizDvsTKziG3w8kxp6e3zO0QRERERO4r7fdYfHx8uv9Qn+WRvK+//pqgoCDs7e05ePCgMTUsPj6eqVOnPnzEIiIiIiIi8siynOS9++67fPTRRyxevNhiZcHAwEAOHDiQrcGJ5GU7duyweF3D3R8RERERkYeR5dU1jx07RoMGDe7Z7+rqytWrV7MjJpE8Izw8/IFlNWvWJDIy8rHFIiIiIiL/DllO8jw9PTl58iTe3t4W+3fu3GnxUmgRSZ+9vT0+Pj65HYaIiIiI5DFZnq7Zr18/hg4dyp49ezCZTJw/f54VK1YwcuRI3njjjZyIUURERERERDIpyyN5b7/9NqmpqTRp0oSkpCQaNGiAnZ0dI0eOZPDgwTkRo4iIiIiIiGTSQ71CAeDGjRucPHmSxMREKlSooIUiRPK4zC7ZKyIiIiI5I7O/x7I8kpfG1taWChUqPOzhIiIiIiIikgMyleS1a9cu0w2uWbPmoYMRERERERGRR5OpJM/V1TWn4xAREREREZFskKkkb8mSJTkdh4iIiIiIiGSDh34m7+LFixw7dgyA8uXLU6RIkWwLSkRERERERB5OlpO8hIQEBg4cyKpVq0hJSQHA2tqaTp068d///ldTO0XyuEoTNmNl55DbYeSI09Nb5nYIIiIiIo/soV6GvmfPHtavX8/Vq1e5evUq69evZ//+/fTv3z8nYhQREREREZFMyvJI3vr169m8eTPPPvussS8oKIjFixfTrFmzbA1OREREREREsibLI3mFChW675RMV1dXChQokC1BiYiIiIiIyMPJcpI3duxYRowYwYULF4x9Fy5c4K233mLcuHHZGpyIiIiIiIhkTaamawYEBGAymYztEydOULJkSUqWLAlAbGwsdnZ2/Pnnn3ouT0REREREJBdlKslr06ZNDochIiIiIiIi2SFTSd6ECRNyOg6RPMfb25thw4YxbNiw3A6FkJAQhg0bxtWrV3M7FBERERHJYVl+Jk9Enmze3t7MmTMnt8MQERERkVyS5VcopKSkMHv2bL788ktiY2O5ceOGRfnly5ezLTgRERERERHJmiyP5E2cOJEPPviATp06ER8fz4gRI2jXrh1WVlYEBwfnQIgiGUtNTWXatGmULl0ae3t7qlatyurVqwEIDw/HZDKxefNmAgICsLe3p3Hjxly8eJGNGzfi7++Pi4sLXbt2JSkpyWizUaNGDBo0iEGDBuHq6krhwoUZN24cZrP5oWK8evUqr776Ku7u7ri4uNC4cWOioqKM8uDgYKpVq8by5cvx9vbG1dWVzp07c+3aNaPOtWvX6NatG46OjhQtWpTZs2fTqFEjY0poo0aNOHPmDMOHD8dkMlksmASwefNm/P39cXJyolmzZsTFxT3UuYiIiIjIkyvLSd6KFStYvHgxb775Jvny5aNLly588sknjB8/np9++iknYhTJ0LRp01i2bBkfffQRR48eZfjw4bzyyits377dqBMcHMyCBQvYtWsXZ8+epWPHjsyZM4eVK1eyYcMGtmzZwvz58y3aXbp0Kfny5WPv3r3MnTuXDz74gE8++eShYuzQoYORWP78889Ur16dJk2aWIx+x8TEsHbtWtavX8/69evZvn0706dPN8pHjBhBREQE69atY+vWrezYsYMDBw4Y5WvWrKFEiRJMmjSJuLg4iyQuKSmJmTNnsnz5cn788UdiY2MZOXLkQ52LiIiIiDy5sjxd88KFC1SuXBkAJycn4uPjAXjxxRf1njzJFcnJyUydOpVt27ZRt25dAMqUKcPOnTtZtGgRr732GgDvvvsugYGBAPTt25cxY8YQExNDmTJlAHj55ZcJCwtj9OjRRtteXl7Mnj0bk8lE+fLlOXz4MLNnz6Zfv35ZinHnzp3s3buXixcvYmdnB8DMmTNZu3Ytq1evNmJMTU0lJCQEZ2dnALp3705oaChTpkzh2rVrLF26lJUrV9KkSRMAlixZQrFixYx+ChYsiLW1Nc7Oznh6elrEcPPmTT766CPKli0LwKBBg5g0aVK61zU5OdnYTkhIyNI5i4iIiEjuyPJIXokSJYzRgbJly7JlyxYA9u3bZ/x4FXmcTp48SVJSEs8//zxOTk7GZ9myZcTExBj1qlSpYvzt4eGBg4ODkeCl7bt48aJF288884zFlMe6dety4sQJUlJSshRjVFQUiYmJFCpUyCLGU6dOWcTo7e1tJHgARYsWNWL67bffuHnzJrVr1zbKXV1dKV++fKZicHBwMBK8u9u+n2nTpuHq6mp8vLy8Mn2+IiIiIpJ7sjyS17ZtW0JDQ6lTpw6DBw/mlVde4dNPPyU2Npbhw4fnRIwi6UpMTARgw4YNFC9e3KLMzs7OSKJsbGyM/SaTyWI7bV9qamqOxVi0aFHCw8PvKXNzczP+zsmY7td2es8XjhkzhhEjRhjbCQkJSvREREREngJZTvLufD6oU6dOlCxZkt27d1OuXDlatWqVrcGJZEaFChWws7MjNjaWhg0b3lN+50hZVu3Zs8di+6effqJcuXJYW1tnqZ3q1atz4cIF8uXLh7e390PFUqZMGWxsbNi3bx8lS5YEID4+nuPHj9OgQQOjnq2tbZZHGu/Hzs5Oo/MiIiIiT6EsJ3l3q1u3rvEclEhucHZ2ZuTIkQwfPpzU1FSeffZZ4uPjiYiIwMXFhVKlSj1027GxsYwYMYL+/ftz4MAB5s+fz6xZs7LcTtOmTalbty5t2rRhxowZ+Pr6cv78eTZs2EDbtm2pWbNmhm04OzvTs2dP3nrrLQoWLEiRIkWYMGECVlZWFlNKvb29+fHHH+ncuTN2dnYULlw4y/GKiIiIyNMrU0neunXraN68OTY2Nqxbty7dui+99FK2BCaSFZMnT8bd3Z1p06bx22+/4ebmRvXq1fnPf/7zSNMde/Towd9//03t2rWxtrZm6NChxiIpWWEymfj+++9555136N27N3/++Seenp40aNAADw+PTLfzwQcf8Prrr/Piiy/i4uLCqFGjOHv2LPnz5zfqTJo0if79+1O2bFmSk5Mf+pUPIiIiIvJ0Mpkz8QvQysqKCxcuUKRIEaysHrxWi8lkypZpYiJPgkaNGlGtWjXmzJmT26E80PXr1ylevDizZs2ib9++OdpXQkLC7QVYhn2JlZ1DjvaVW05Pb5nbIYiIiIg8UNrvsfj4eFxcXB5YL1MjeXeOhOTUwhQikrGDBw/y66+/Urt2beLj441XILRu3TqXIxMRERGRJ0WWXqFw8+ZNmjRpwokTJ3IqHpGnwo4dOyxehXD3JyfNnDmTqlWr0rRpU65fv86OHTv03J2IiIiIGLK08IqNjQ2HDh3KqVhEnij3e91Bmpo1axIZGfnYYkkTEBDAzz///Nj7FREREZGnR5ZX10x7L96dr1IQ+bext7fHx8cnt8MQEREREblHlpO8W7du8dlnn7Ft2zZq1KiBo6OjRfkHH3yQbcGJiIiIiIhI1mQ5yTty5AjVq1cH4Pjx4xZld76rS0RERERERB6/TL1CQUQks0v2ioiIiEjOyOzvsSytrikiIiIiIiJPtixP1wTYv38/X375JbGxsdy4ccOibM2aNdkSmIiIiIiIiGRdlkfyVq1aRb169YiOjuabb77h5s2bHD16lB9++AFXV9eciFFEREREREQyKctJ3tSpU5k9ezbfffcdtra2zJ07l19//ZWOHTtSsmTJnIhRREREREREMinLSV5MTAwtW7YEwNbWluvXr2MymRg+fDgff/xxtgcoIiIiIiIimZflZ/IKFCjAtWvXAChevDhHjhyhcuXKXL16laSkpGwPUESeLJUmbMbKziFb2zw9vWW2ticiIiLyb5bpkbwjR44A0KBBA7Zu3QpAhw4dGDp0KP369aNLly40adIkZ6IUERERERGRTMn0SF6VKlWoVasWbdq0oUOHDgC888472NjYsGvXLtq3b8/YsWNzLFARERERERHJWKaTvO3bt7NkyRKmTZvGlClTaN++Pa+++ipvv/12TsYnIiIiIiIiWZDp6Zr169fns88+Iy4ujvnz53P69GkaNmyIr68v7733HhcuXMjJOEVERERERCQTsry6pqOjI71792b79u0cP36cDh068N///peSJUvy0ksv5USMIiIiIiIikklZTvLu5OPjw3/+8x/Gjh2Ls7MzGzZsyK64RERERERE5CE8dJL3448/0qtXLzw9PXnrrbdo164dERER2RmbSLbw9vZmzpw5uR1GtsqL5yQiIiIi2SNLSd758+eZOnUqvr6+NGrUiJMnTzJv3jzOnz/P4sWLeeaZZ3IqTnnMsjuJGDJkCDVq1MDOzo5q1ardUx4eHk7r1q0pWrQojo6OVKtWjRUrVtxT76uvvsLPz4/8+fNTuXJlvv/++2yL8XEICQnBzc0tt8MQERERkTws00le8+bNKVWqFPPnz6dt27ZER0ezc+dOevfujaOjY07GKHlEnz596NSp033Ldu3aRZUqVfj66685dOgQvXv3pkePHqxfv96iTpcuXejbty8HDx6kTZs2tGnTxniHo4iIiIiIZCHJs7GxYfXq1fz++++89957lC9fPifjypRGjRoxZMgQRo0aRcGCBfH09CQ4OBiA06dPYzKZiIyMNOpfvXoVk8lEeHg4cHv0yGQysXnzZgICArC3t6dx48ZcvHiRjRs34u/vj4uLC127diUpKSlTMW3atIlnn30WNzc3ChUqxIsvvkhMTIxFnd9//50uXbpQsGBBHB0dqVmzJnv27DHKv/vuO2rVqkX+/PkpXLgwbdu2NcquXLlCjx49KFCgAA4ODjRv3pwTJ04Y5cHBwfeMlM2ZMwdvb29ju1evXrRp04aZM2dStGhRChUqxMCBA7l586ZxXc+cOcPw4cMxmUyYTCYAzpw5Q6tWrShQoACOjo5UrFgx0yNp8+bNY+DAgZQpU+a+5f/5z3+YPHky9erVo2zZsgwdOpRmzZqxZs0ao87cuXNp1qwZb731Fv7+/kyePJnq1auzYMECo87Fixdp1aoV9vb2lC5d+r6jgekxmUwsXLiQ5s2bY29vT5kyZVi9erVFndGjR+Pr64uDgwNlypRh3LhxxrUDiIqK4rnnnsPZ2RkXFxdq1KjB/v37CQ8Pp3fv3sTHxxvXNe37mp6Mzikz33WAI0eO0Lx5c5ycnPDw8KB79+789ddfWbo+IiIiIvLky3SSt27dOlq3bo21tXVOxpNlS5cuxdHRkT179jBjxgwmTZrE1q1bs9RGcHAwCxYsYNeuXZw9e5aOHTsyZ84cVq5cyYYNG9iyZQvz58/PVFvXr19nxIgR7N+/n9DQUKysrGjbti2pqakAJCYm0rBhQ86dO8e6deuIiopi1KhRRvmGDRto27YtLVq04ODBg4SGhlK7dm2j/V69erF//37WrVvH7t27MZvNtGjRwiLJyIywsDBiYmIICwtj6dKlhISEEBISAsCaNWsoUaIEkyZNIi4ujri4OAAGDhxIcnIyP/74I4cPH+a9997DyckpS/1mRXx8PAULFjS2d+/eTdOmTS3qBAUFsXv3bmO7V69enD17lrCwMFavXs2HH37IxYsXs9TvuHHjaN++PVFRUXTr1o3OnTsTHR1tlDs7OxMSEsIvv/zC3LlzWbx4MbNnzzbKu3XrRokSJdi3bx8///wzb7/9NjY2NtSrV485c+bg4uJiXNeRI0dmGE92nNPVq1dp3LgxAQEB7N+/n02bNvHHH3/QsWPHLLUjIiIiIk++TL8M/UlVpUoVJkyYAEC5cuVYsGABoaGhlCtXLtNtvPvuuwQGBgLQt29fxowZQ0xMjDHq9PLLLxMWFsbo0aMzbKt9+/YW25999hnu7u788ssvVKpUiZUrV/Lnn3+yb98+I4Hx8fEx6k+ZMoXOnTszceJEY1/VqlUBOHHiBOvWrSMiIoJ69eoBsGLFCry8vFi7di0dOnTI9DkXKFCABQsWYG1tjZ+fHy1btiQ0NJR+/fpRsGBBrK2tcXZ2xtPT0zgmNjaW9u3bU7lyZYAHjsplhy+//JJ9+/axaNEiY9+FCxfw8PCwqOfh4WG8o/H48eNs3LiRvXv3UqtWLQA+/fRT/P39s9R3hw4dePXVVwGYPHkyW7duZf78+Xz44YcAjB071qjr7e3NyJEjWbVqFaNGjQJuX6e33noLPz8/AIvvoqurKyaTyeK6pie7zmnBggUEBAQwdepUY99nn32Gl5cXx48fx9fX955jkpOTSU5ONrYTEhKy1KeIiIiI5I5HeoXCk6BKlSoW20WLFs3yKMedbXh4eBjT8O7cl9k2T5w4QZcuXShTpgwuLi7GNMnY2FgAIiMjCQgIsBihulNkZCRNmjS5b1l0dDT58uWjTp06xr5ChQpRvnx5i5GmzKhYsaLFqGxmrtuQIUOMhHjChAkcOnQoS31mVlhYGL1792bx4sVUrFgx08elXZ8aNWoY+/z8/LK80EndunXv2b7z+n7xxRcEBgbi6emJk5MTY8eONe4vwIgRI3j11Vdp2rQp06dPv2e6blZk1zlFRUURFhaGk5OT8UlLQh8U37Rp03B1dTU+Xl5eD30eIiIiIvL4PPVJno2NjcW2yWQiNTUVK6vbp2Y2m42yB01pvLMNk8n0wDYzo1WrVly+fJnFixezZ88e41m7GzduAGBvb5/u8RmVZ8TKysrinOH+5/0w5/jqq6/y22+/0b17dw4fPkzNmjUzPY01s7Zv306rVq2YPXs2PXr0sCjz9PTkjz/+sNj3xx9/ZHpULDvs3r2bbt260aJFC9avX8/Bgwd55513jPsLt6f/Hj16lJYtW/LDDz9QoUIFvvnmmxyLKTPf9cTERFq1akVkZKTF58SJEzRo0OC+7Y4ZM4b4+Hjjc/bs2Rw7BxERERHJPk99kvcg7u7uAMbzZIDFwhQ54dKlSxw7doyxY8fSpEkT/P39uXLlikWdKlWqEBkZyeXLl+/bRpUqVQgNDb1vmb+/P7du3bJYpCWtzwoVKgC3z/vChQsWP/gf5rxtbW1JSUm5Z7+Xlxevv/46a9as4c0332Tx4sVZbvtBwsPDadmyJe+99x6vvfbaPeV169a959ps3brVGHnz8/Pj1q1b/Pzzz0b5sWPHuHr1apbi+Omnn+7ZTpseuWvXLkqVKsU777xDzZo1KVeuHGfOnLmnDV9fX4YPH86WLVto164dS5YsAR58XR8kM+eUme969erVOXr0KN7e3vj4+Fh8HrQ6rp2dHS4uLhYfEREREXny5dkkz97enmeeeYbp06cTHR3N9u3bLZ6lygkFChSgUKFCfPzxx5w8eZIffviBESNGWNTp0qULnp6etGnThoiICH777Te+/vprY/GQCRMm8PnnnzNhwgSio6ONBU7g9rNdrVu3pl+/fuzcuZOoqCheeeUVihcvTuvWrYHbK2P++eefzJgxg5iYGP773/+ycePGLJ+Lt7c3P/74I+fOnTNWYBw2bBibN2/m1KlTHDhwgLCwsEw/G3by5EkiIyO5cOECf//9tzGSlDYCFhYWRsuWLRkyZAjt27fnwoULXLhwwSIZHjp0KJs2bWLWrFn8+uuvBAcHs3//fgYNGgRA+fLladasGf3792fPnj38/PPPvPrqq1keHf3qq6/47LPPOH78OBMmTGDv3r1GH+XKlSM2NpZVq1YRExPDvHnzLEbp/v77bwYNGkR4eDhnzpwhIiKCffv2GdfJ29ubxMREQkND+euvvzJctTUz55SZ7/rAgQO5fPkyXbp0Yd++fcTExLB582Z69+6dpaRTRERERJ58eTbJg9sLS9y6dYsaNWowbNgw3n333Rztz8rKilWrVvHzzz9TqVIlhg8fzvvvv29Rx9bWli1btlCkSBFatGhB5cqVmT59uvF8XKNGjfjqq69Yt24d1apVo3Hjxuzdu9c4fsmSJdSoUYMXX3yRunXrYjab+f77743pl/7+/nz44Yf897//pWrVquzduzdTKzjebdKkSZw+fZqyZcsaI0UpKSkMHDgQf39/mjVrhq+vr7EYSUZeffVVAgICWLRoEcePHycgIICAgADOnz8P3F4lNSkpiWnTplG0aFHj065dO6ONevXqsXLlSj7++GOqVq3K6tWrWbt2LZUqVbK4PsWKFaNhw4a0a9eO1157jSJFimTp3CdOnMiqVauoUqUKy5Yt4/PPPzdGSl966SWGDx/OoEGDqFatGrt27WLcuHHGsdbW1ly6dIkePXrg6+tLx44dad68ubGQTr169Xj99dfp1KkT7u7uzJgxI8N4MnNOGX3XixUrRkREBCkpKbzwwgtUrlyZYcOG4ebmZkz3FBEREZG8wWS++wEukX8xk8nEN998Q5s2bXI7lCdOQkLC7QVYhn2JlZ1DtrZ9enrLbG1PREREJC9K+z0WHx+f7qM0+id8ERERERGRPERJXhbExsZaLEF/9+fOZfT/TV5//fUHXpPXX389t8MzrFix4oFxZuVVDdlpx44d6X6nRERERESyStM1s+DWrVucPn36geXe3t7ky/fUv18+yy5evPjAF2W7uLhk+Zm4nHLt2rV7XsGQxsbGhlKlSj3miG4v1HLu3LkHlvv4+DzGaNKn6ZoiIiIiuSuz0zX/fRnJI8iXL98T9aP7SVGkSJEnJpFLj7OzM87OzrkdhgV7e3t9p0REREQkW2m6poiIiIiISB6iJE9ERERERCQP0XRNEcmSIxOD0p0DLiIiIiK5SyN5IiIiIiIieYiSPBERERERkTxESZ6IiIiIiEgeoiRPREREREQkD1GSJyIiIiIikocoyRORLKk0YXNuhyAiIiIi6VCSJyIiIiIikocoyRMREREREclDlOSJiIiIiIjkIUryRERERERE8hAleSIiIiIiInmIkjwREREREZE8REmePDaNGjVi2LBhmaobEhKCm5tbjsaTXUwmE2vXrs3tMEREREREACV58hQLDg6mWrVqWTomJxKyuLg4mjdvnq1tioiIiIg8rHy5HYDI087T0zO3QxARERERMWgkT3LE9evX6dGjB05OThQtWpRZs2ZZlCcnJzNy5EiKFy+Oo6MjderUITw8/J521q5dS7ly5cifPz9BQUGcPXsWuD2dc+LEiURFRWEymTCZTISEhKQbk7e3NwBt27bFZDIZ2wALFy6kbNmy2NraUr58eZYvX57pc71zdPD06dOYTCbWrFnDc889h4ODA1WrVmX37t0Wx0RERNCoUSMcHBwoUKAAQUFBXLlyxbg2Q4YMoUiRIuTPn59nn32Wffv2GceGh4djMpnYvHkzAQEB2Nvb07hxYy5evMjGjRvx9/fHxcWFrl27kpSUZByXmprKtGnTKF26NPb29lStWpXVq1dn+jxFRERE5OmgJE9yxFtvvcX27dv59ttv2bJlC+Hh4Rw4cMAoHzRoELt372bVqlUcOnSIDh060KxZM06cOGHUSUpKYsqUKSxbtoyIiAiuXr1K586dAejUqRNvvvkmFStWJC4ujri4ODp16pRuTGmJ0pIlS4iLizO2v/nmG4YOHcqbb77JkSNH6N+/P7179yYsLOyhz/+dd95h5MiRREZG4uvrS5cuXbh16xYAkZGRNGnShAoVKrB792527txJq1atSElJAWDUqFF8/fXXLF26lAMHDuDj40NQUBCXL1+26CM4OJgFCxawa9cuzp49S8eOHZkzZw4rV65kw4YNbNmyhfnz5xv1p02bxrJly/joo484evQow4cP55VXXmH79u33PYfk5GQSEhIsPiIiIiLyFDCLZLNr166ZbW1tzV9++aWx79KlS2Z7e3vz0KFDzWfOnDFbW1ubz507Z3FckyZNzGPGjDGbzWbzkiVLzID5p59+Msqjo6PNgHnPnj1ms9lsnjBhgrlq1apZig0wf/PNNxb76tWrZ+7Xr5/Fvg4dOphbtGiR5TZPnTplBsyffPKJUX706FEzYI6OjjabzWZzly5dzIGBgfdtKzEx0WxjY2NesWKFse/GjRvmYsWKmWfMmGE2m83msLAwM2Detm2bUWfatGlmwBwTE2Ps69+/vzkoKMhsNpvN//zzj9nBwcG8a9cui/769u1r7tKly31jmTBhghm45+M17Mv71hcRERGRnBUfH28GzPHx8enW00ieZLuYmBhu3LhBnTp1jH0FCxakfPnyABw+fJiUlBR8fX1xcnIyPtu3bycmJsY4Jl++fNSqVcvY9vPzw83Njejo6GyNNzo6msDAQIt9gYGBj9RPlSpVjL+LFi0KwMWLF4H/G8m7n5iYGG7evGkRj42NDbVr174nnjv78PDwwMHBgTJlyljsS+vz5MmTJCUl8fzzz1tc82XLlllc8zuNGTOG+Ph445M2VVZEREREnmxaeEUeu8TERKytrfn555+xtra2KHNycsqlqLKXjY2N8bfJZAJuPxMHYG9vnyN93Lmdti+tz8TERAA2bNhA8eLFLerZ2dndt307O7sHlomIiIjIk0sjeZLtypYti42NDXv27DH2XblyhePHjwMQEBBASkoKFy9exMfHx+Jz50qVt27dYv/+/cb2sWPHuHr1Kv7+/gDY2toaz7Fllo2NzT3H+Pv7ExERYbEvIiKCChUqZKntzKpSpQqhoaH3LUtb/OXOeG7evMm+ffseKZ4K/6+9u4/vsf7//39/bWMndj5so82EjQk5b85jtTlZxjupVojOiRWSL96UnCQ6VaKyeUtW+qByFq1tSXKW0zAsTIWVk82c256/P1wcv17Oz8drt+vlclwuO57P5/F8Po6nw7wejudxvCIj5erqquzs7HPmPCQk5Kr7BQAAwK2HO3m47jw9PdWjRw/1799fAQEBKlu2rAYNGiQnp9P/pxAeHq6EhAR16dJF48aNU+3atfX3338rNTVVNWvWVNu2bSWdTsheeOEFvffee3JxcVGvXr10zz33qEGDBpJOvy1z+/btWrNmje644w55eXld8s5TWFiYUlNT1bhxY7m6usrPz0/9+/fXQw89pNq1ays6OlrffvutZs6cqe+///6GzM/AgQNVo0YNPf/883r22WdVsmRJpaWlqVOnTipdurSee+459e/fX/7+/goNDdWYMWN05MgR9ejR46rH9PLyUr9+/fTiiy+qsLBQTZo0UW5urpYsWSJvb2917dr1Op4hAAAAihJ38nBDvPnmm2ratKni4uIUHR2tJk2aqG7dulZ9UlKSunTpor59+yoiIkLx8fFasWKFQkNDrTYeHh4aMGCAHn30UTVu3Fienp764osvrPr//Oc/io2N1b333qsyZcpo+vTpl4xr3LhxWrRokUJCQlS7dm1JUnx8vN59912NHTtW1atX18SJE5WUlKQWLVpcvwn5l/DwcC1cuFBr165VgwYNFBUVpa+//louLqf/z2X06NH6z3/+o8cff1x16tTRtm3b9N1338nPz++axh0+fLiGDBmiUaNGqVq1aoqNjdXcuXNVsWLF63FaAAAAuEXYjDGmqIMAcOvLy8uTj4+PQhK/VPbbnYo6HAAAgGLnzOex3NxceXt7X7Add/IAAAAAwIGQ5MFhTJs2ze7rAf69Va9e/ZbpEwAAALiRePEKHMYDDzxg9918/3b21wsUZZ8AAADAjUSSB4fh5eUlLy+vW75PAAAA4EZiuSYAAAAAOBCSPAAAAABwICR5AK7IhldjijoEAAAAXARJHgAAAAA4EJI8AAAAAHAgJHkAAAAA4EBI8gAAAADAgZDkAQAAAIADIckDAAAAAAdCkgcAAAAADoQkDwAAAAAcCEkeAAAAADgQkjwAAAAAcCAkeQAAAADgQEjyAAAAAMCBkOQBZ0lPT5fNZtPBgwcv2TY5OVm+vr43PCYAAADgcpHkAdegc+fO2rJlS1GHAQAAAFhcijoA4Hbm7u4ud3f3og4DAAAAsHAnDzfNggUL1KRJE/n6+iogIEDt2rVTVlaWJGnHjh2y2WyaOXOm7r33Xnl4eKhWrVpaunSpdfyZpZHfffedqlWrJk9PT8XGxmr37t1WmxYtWigxMdFu3Pj4eHXr1s3anzp1qurVqycvLy8FBQXp0UcfVU5OzlWd09nLNYcNG6a7775bU6dOVVhYmHx8fPTwww/r0KFDVpvCwkKNGTNGlStXlqurq0JDQzVixAirfv369WrZsqXc3d0VEBCgp59+Wvn5+VZ9t27dFB8fr5EjRyowMFC+vr567bXXdOrUKfXv31/+/v664447lJSUZBfrrl279NBDD8nX11f+/v5q3769duzYcVXnDQAAgFsXSR5umsOHD+ull17SypUrlZqaKicnJ3Xo0EGFhYVWm0GDBqlfv35as2aNwsPD9cgjj+jUqVNW/ZEjRzR27FhNnTpVP/74o7Kzs9WvX78riuPkyZMaPny41q5dq9mzZ2vHjh12SeC1ysrK0uzZszVnzhzNmTNHGRkZGj16tFU/cOBAjR49WkOGDNHGjRv1+eefKzAwUNLpOYqJiZGfn59WrFihGTNm6Pvvv1evXr3sxvjhhx/0119/6ccff9Rbb72loUOHql27dvLz89OyZcv07LPP6plnntEff/xhnXNMTIy8vLy0ePFiLVmyxEqST5w4cd7zOH78uPLy8uw2AAAA3AYMUET+/vtvI8msX7/ebN++3Ugyn3zyiVX/22+/GUlm06ZNxhhjkpKSjCSzbds2q80HH3xgAgMDrf3mzZubPn362I3Tvn1707Vr1wvGsWLFCiPJHDp0yBhjTFpampFkDhw4cMlzSEpKMj4+Ptb+0KFDjYeHh8nLy7PK+vfvbxo2bGiMMSYvL8+4urqajz/++Lz9TZo0yfj5+Zn8/HyrbO7cucbJycns2bPHGGNM165dTYUKFUxBQYHVJiIiwjRt2tTaP3XqlClVqpSZPn26McaYqVOnmoiICFNYWGi1OX78uHF3dzfffffdeWMZOnSokXTOlpube8l5AQAAwPWXm5t7WZ/HuJOHm2br1q165JFHdOedd8rb21thYWGSpOzsbKtNzZo1rZ+Dg4MlyW4ppYeHhypVqmTX5kqXWq5atUpxcXEKDQ2Vl5eXmjdvfk4c1yIsLExeXl7njXHTpk06fvy4WrVqdd5jN23apFq1aqlUqVJWWePGjVVYWKjMzEyrrHr16nJy+v//+gYGBqpGjRrWvrOzswICAqxx165dq23btsnLy0uenp7y9PSUv7+/jh07Zi2ZPdvAgQOVm5trbbt27bqK2QAAAMDNxotXcNPExcWpQoUK+vjjj1WuXDkVFhbqrrvuslsuWKJECetnm80mSXbLOf9df6aNMcbad3JystuXTi9VPOPMcsiYmBhNmzZNZcqUUXZ2tmJiYi64bPFKnS/GM+dwvV7Scr4xLjZufn6+6tatq2nTpp3TV5kyZc47hqurq1xdXa9LvAAAALh5uJOHm2Lfvn3KzMzU4MGD1apVK1WrVk0HDhy47uOUKVPG7kUsBQUF2rBhg7W/efNm7du3T6NHj1bTpk1VtWrVq37pytWoUqWK3N3dlZqaet76atWqae3atTp8+LBVtmTJEjk5OSkiIuKqx61Tp462bt2qsmXLqnLlynabj4/PVfcLAACAWw9JHm4KPz8/BQQEaNKkSdq2bZt++OEHvfTSS9d9nJYtW2ru3LmaO3euNm/erOeee87uS81DQ0NVsmRJvf/++/r999/1zTffaPjw4dc9jgtxc3PTgAED9PLLL+t///ufsrKy9Msvv+jTTz+VJCUkJMjNzU1du3bVhg0blJaWphdeeEGPP/649XKWq5GQkKDSpUurffv2Wrx4sbZv36709HT17t3bejkLAAAAHANJHm4KJycnpaSkaNWqVbrrrrv04osv6s0337zu43Tv3l1du3ZVly5d1Lx5c91555269957rfoyZcooOTlZM2bMUGRkpEaPHq2xY8de9zguZsiQIerbt6/++9//qlq1aurcubN1N9HDw0Pfffed9u/fr/r16+vBBx9Uq1atNH78+Gsa08PDQz/++KNCQ0PVsWNHVatWTT169NCxY8fk7e19PU4LAAAAtwibOfsBJgA4j7y8PPn4+Cg3N5fEEAAAoAhc7ucx7uQBAAAAgAMhyQMuonXr1tZXDpy9jRw5sqjDAwAAAM7BVygAF/HJJ5/o6NGj563z9/e/ydEAAAAAl0aSB1xE+fLlizoEAAAA4IqwXBMAAAAAHAhJHgAAAAA4EJI8AAAAAHAgJHkAAAAA4EBI8gAAAADAgZDkAQAAAIADIckDAAAAAAdCkgcAAAAADoQkDwAAAAAcCEkeAAAAADgQkjwAAAAAcCAkeQAAAADgQEjyAAAAAMCBkOQBAAAAgAMhyQMAAAAAB0KSh+uuRYsWSkxMLOowHNawYcN09913X7QNfwYAAADFl0tRBwDg+ps5c6ZKlChR1GEAAACgCJDkAbeQgoIC2Ww2OTld2012f3//6xQRAAAAbjcs18QNUVhYqJdffln+/v4KCgrSsGHDrLrs7Gy1b99enp6e8vb21kMPPaS9e/da9WeWI06ePFmhoaHy9PTU888/r4KCAo0ZM0ZBQUEqW7asRowYYTfmwYMH9eSTT6pMmTLy9vZWy5YttXbtWrs2EyZMUKVKlVSyZElFRERo6tSpVl2/fv3Url07a/+dd96RzWbTggULrLLKlSvrk08+kSR169ZN8fHxGjt2rIKDgxUQEKCePXvq5MmTVvvjx4+rX79+Kl++vEqVKqWGDRsqPT3dqk9OTpavr6+++eYbRUZGytXVVdnZ2UpPT1eDBg1UqlQp+fr6qnHjxtq5c6fduUydOlVhYWHy8fHRww8/rEOHDll1Zy/XDAsL0/Dhw/XII4+oVKlSKl++vD744IOL/RECAADgNkWShxtiypQpKlWqlJYtW6YxY8botdde06JFi1RYWKj27dtr//79ysjI0KJFi/T777+rc+fOdsdnZWVp/vz5WrBggaZPn65PP/1Ubdu21R9//KGMjAy98cYbGjx4sJYtW2Yd06lTJ+Xk5Gj+/PlatWqV6tSpo1atWmn//v2SpFmzZqlPnz7q27evNmzYoGeeeUZPPPGE0tLSJEnNmzfXTz/9pIKCAklSRkaGSpcubSVlf/75p7KystSiRQtrzLS0NGVlZSktLU1TpkxRcnKykpOTrfpevXpp6dKlSklJ0bp169SpUyfFxsZq69atVpsjR47ojTfe0CeffKLffvtN/v7+io+PV/PmzbVu3TotXbpUTz/9tGw2m938zJ49W3PmzNGcOXOUkZGh0aNHX/TP5M0331StWrW0evVqvfLKK+rTp48WLVp0wfbHjx9XXl6e3QYAAIDbgAGus+bNm5smTZrYldWvX98MGDDALFy40Dg7O5vs7Gyr7rfffjOSzPLly40xxgwdOtR4eHiYvLw8q01MTIwJCwszBQUFVllERIQZNWqUMcaYxYsXG29vb3Ps2DG7cStVqmQmTpxojDGmUaNG5qmnnrKr79Spk2nTpo0xxpgDBw4YJycns2LFClNYWGj8/f3NqFGjTMOGDY0xxnz22WemfPny1rFdu3Y1FSpUMKdOnbLrr3PnzsYYY3bu3GmcnZ3Nn3/+aTdmq1atzMCBA40xxiQlJRlJZs2aNVb9vn37jCSTnp5+3vk93/z079/fitOY038Gffr0sfYrVKhgYmNj7frp3Lmzad269XnHODOOpHO23NzcCx4DAACAGyc3N/eyPo9xJw83RM2aNe32g4ODlZOTo02bNikkJEQhISFWXWRkpHx9fbVp0yarLCwsTF5eXtZ+YGCgIiMj7Z5VCwwMVE5OjiRp7dq1ys/PV0BAgDw9Pa1t+/btysrKkiRt2rRJjRs3tourcePG1ri+vr6qVauW0tPTtX79epUsWVJPP/20Vq9erfz8fGVkZKh58+Z2x1evXl3Ozs7nnKckrV+/XgUFBQoPD7eLKSMjw4pJkkqWLGk3X/7+/urWrZtiYmIUFxend999V7t377Yb9+z5+fe4FxIVFXXO/r/n/GwDBw5Ubm6ute3ateui/QMAAODWwItXcEOc/WZHm82mwsLCazr+Yn3m5+crODjY7nm3M3x9fS973BYtWig9PV2urq5q3ry5/P39Va1aNf3000/KyMhQ3759Lxnnv2NydnbWqlWr7BJBSfL09LR+dnd3t1uKKUlJSUnq3bu3FixYoC+++EKDBw/WokWLdM8991xy3OvF1dVVrq6u17VPAAAA3HgkebipqlWrpl27dmnXrl3W3byNGzfq4MGDioyMvOp+69Spoz179sjFxUVhYWEXHHvJkiXq2rWrVbZkyRK7cZs3b67JkyfLxcVFsbGxkk4nftOnT9eWLVvsnse7lNq1a6ugoEA5OTlq2rTpFZ9T7dq1Vbt2bQ0cOFBRUVH6/PPPrSTvavzyyy/n7FerVu2q+wMAAMCtieWauKmio6NVo0YNJSQk6Ndff9Xy5cvVpUsXNW/eXPXq1bumfqOiohQfH6+FCxdqx44d+vnnnzVo0CCtXLlSktS/f38lJydrwoQJ2rp1q9566y3NnDlT/fr1s/pp1qyZDh06pDlz5lgJXYsWLTRt2jQFBwcrPDz8smMKDw9XQkKCunTpopkzZ2r79u1avny5Ro0apblz517wuO3bt2vgwIFaunSpdu7cqYULF2rr1q3XnJAtWbJEY8aM0ZYtW/TBBx9oxowZ6tOnzzX1CQAAgFsPd/JwU9lsNn399dd64YUX1KxZMzk5OSk2Nlbvv//+Nfc7b948DRo0SE888YT+/vtvBQUFqVmzZgoMDJQkxcfH691339XYsWPVp08fVaxYUUlJSXZ35/z8/FSjRg3t3btXVatWlXQ68SssLDznebzLkZSUpNdff119+/bVn3/+qdKlS+uee+6x+6qGs3l4eGjz5s2aMmWK9u3bp+DgYPXs2VPPPPPMFY//b3379tXKlSv16quvytvbW2+99ZZiYmKuqU8AAADcemzGGFPUQQC4scLCwpSYmGj33XlXKi8vTz4+PsrNzZW3t/f1Cw4AAACX5XI/j7FcEwAAAAAcCEkeAAAAADgQnskDioEdO3YUdQgAAAC4SbiTBwAAAAAOhCQPAAAAABwISR4AAAAAOBCSPAAAAABwICR5AAAAAOBASPIAAAAAwIGQ5AEAAACAAyHJAwAAAAAHQpIHAAAAAA6EJA8AAAAAHAhJHgAAAAA4EJI8AAAAAHAgJHkAAAAA4EBI8gAAAADAgZDkAQAAAIADIckDAAAAAAdCkofbRnp6umw2mw4ePGiVzZ49W5UrV5azs7MSExOVnJwsX1/fax7LZrNp9uzZl2y3Y8cO2Ww2rVmz5prHBAAAAK4Hl6IOALhcjRo10u7du+Xj42OVPfPMM3riiSfUu3dveXl5ycXFRW3atLlpMYWEhGj37t0qXbr0TRsTAAAAuBiSPNwWTp48qZIlSyooKMgqy8/PV05OjmJiYlSuXDmr3N3d/abF5ezsbBcTAAAAUNRYronrbtKkSSpXrpwKCwvtytu3b6/u3btLkr7++mvVqVNHbm5uuvPOO/Xqq6/q1KlTVlubzaYJEybogQceUKlSpTRixAi75Zrp6eny8vKSJLVs2VI2m03p6ennXa55qbG2bt2qZs2ayc3NTZGRkVq0aNFln+vZyzXPxJiamqp69erJw8NDjRo1UmZmpt1x3377rerXry83NzeVLl1aHTp0sOoOHDigLl26yM/PTx4eHmrdurW2bt1q1Z85xzlz5igiIkIeHh568MEHdeTIEU2ZMkVhYWHy8/NT7969VVBQYB13/Phx9evXT+XLl1epUqXUsGFDpaenX/a5AgAA4PZAkofrrlOnTtq3b5/S0tKssv3792vBggVKSEjQ4sWL1aVLF/Xp00cbN27UxIkTlZycrBEjRtj1M2zYMHXo0EHr16+3ksMz/p04/d///Z92796tRo0anRPLpcYqLCxUx44dVbJkSS1btkwfffSRBgwYcM1zMGjQII0bN04rV66Ui4uLXfxz585Vhw4d1KZNG61evVqpqalq0KCBVd+tWzetXLlS33zzjZYuXSpjjNq0aaOTJ09abY4cOaL33ntPKSkpWrBggdLT09WhQwfNmzdP8+bN09SpUzVx4kR99dVX1jG9evXS0qVLlZKSonXr1qlTp06KjY21SyD/7fjx48rLy7PbAAAAcBswwA3Qvn170717d2t/4sSJply5cqagoMC0atXKjBw50q791KlTTXBwsLUvySQmJtq1SUtLM5LMgQMHjDHGHDhwwEgyaWlpVpukpCTj4+Nj7V9qrO+++864uLiYP//806qfP3++kWRmzZp1yfPcvn27kWRWr15tF+P3339vtZk7d66RZI4ePWqMMSYqKsokJCSct78tW7YYSWbJkiVW2T///GPc3d3Nl19+aZ2jJLNt2zarzTPPPGM8PDzMoUOHrLKYmBjzzDPPGGOM2blzp3F2drY7zzPzM3DgwPPGMnToUCPpnC03N/eS8wIAAIDrLzc397I+j/FMHm6IhIQEPfXUU/rwww/l6uqqadOm6eGHH5aTk5PWrl2rJUuW2N25Kygo0LFjx3TkyBF5eHhIkurVq3fNcVxqrE2bNikkJMTumb6oqKhrHrdmzZrWz8HBwZKknJwchYaGas2aNXrqqafOe9ymTZvk4uKihg0bWmUBAQGKiIjQpk2brDIPDw9VqlTJ2g8MDFRYWJg8PT3tynJyciRJ69evV0FBgcLDw+3GO378uAICAs4by8CBA/XSSy9Z+3l5eQoJCbnkuQMAAKBokeThhoiLi5MxRnPnzlX9+vW1ePFivf3225JOvzDl1VdfVceOHc85zs3Nzfq5VKlS1xzH5Y51vZUoUcL62WazSZL1jOL1eDHMv/s/M8b5ys6MmZ+fL2dnZ61atUrOzs527f6dGP6bq6urXF1drzlWAAAA3Fwkebgh3Nzc1LFjR02bNk3btm1TRESE6tSpI0mqU6eOMjMzVbly5Rsex6XGqlatmnbt2qXdu3dbd9x++eWXGxpTzZo1lZqaqieeeOK88Zw6dUrLli2znjHct2+fMjMzFRkZedVj1q5dWwUFBcrJyVHTpk2vuh8AAADc+kjycMMkJCSoXbt2+u233/TYY49Z5f/973/Vrl07hYaG6sEHH7SWcG7YsEGvv/76dY3hUmNFR0crPDxcXbt21Ztvvqm8vDwNGjTousZwtqFDh6pVq1aqVKmSHn74YZ06dUrz5s3TgAEDVKVKFbVv315PPfWUJk6cKC8vL73yyisqX7682rdvf9VjhoeHKyEhQV26dNG4ceNUu3Zt/f3330pNTVXNmjXVtm3b63iGAAAAKEq8XRM3TMuWLeXv76/MzEw9+uijVnlMTIzmzJmjhQsXqn79+rrnnnv09ttvq0KFCtc9hkuN5eTkpFmzZuno0aNq0KCBnnzyyXPe8nm9tWjRQjNmzNA333yju+++Wy1bttTy5cut+qSkJNWtW1ft2rVTVFSUjDGaN2/eOcsxr1RSUpK6dOmivn37KiIiQvHx8VqxYoVCQ0Ov9ZQAAABwC7EZY0xRBwHg1peXlycfHx/l5ubK29u7qMMBAAAodi738xh38gAAAADAgZDkARcxcuRIeXp6nndr3bp1UYcHAAAAnIPlmsBF7N+/X/v37z9vnbu7u8qXL3+TIyo6LNcEAAAoWpf7eYy3awIX4e/vL39//6IOAwAAALhsLNcEAAAAAAdCkgcAAAAADoQkDwAAAAAcCEkeAAAAADgQkjwAAAAAcCAkeQAAAADgQEjyAAAAAMCBkOQBAAAAgAMhyQMAAAAAB0KSBwAAAAAOhCQPAAAAABwISR4AAAAAOBCSPAAAAABwICR5AAAAAOBASPIAAAAAwIGQ5AEX0KJFCyUmJl5W2+TkZPn6+lr7w4YN0913331D4gIAAAAuhiQPuAH69eun1NTUmzbe/v379cILLygiIkLu7u4KDQ1V7969lZube9NiAAAAwK3BpagDAByRp6enPD09b9p4f/31l/766y+NHTtWkZGR2rlzp5599ln99ddf+uqrr25aHAAAACh63MkDJB0+fFhdunSRp6engoODNW7cOLv648ePq1+/fipfvrxKlSqlhg0bKj09/YL9nb1cs1u3boqPj9fYsWMVHBysgIAA9ezZUydPnrTa5OTkKC4uTu7u7qpYsaKmTZumsLAwvfPOO5eM/6677tL//d//KS4uTpUqVVLLli01YsQIffvttzp16pSMMapcubLGjh1rd9yaNWtks9m0bdu2y5onAAAA3PpI8gBJ/fv3V0ZGhr7++mstXLhQ6enp+vXXX636Xr16aenSpUpJSdG6devUqVMnxcbGauvWrZc9RlpamrKyspSWlqYpU6YoOTlZycnJVn23bt20a9cupaWl6auvvtKHH36onJycqz6n3NxceXt7y8XFRTabTd27d1dSUpJdm6SkJDVr1kyVK1c+5/jjx48rLy/PbgMAAMCtjyQPxV5+fr4+/fRTjR07Vq1atVKNGjU0ZcoUnTp1SpKUnZ2tpKQkzZgxQ02bNlWlSpXUr18/NWnS5Jyk6WL8/Pw0fvx4Va1aVe3atVPbtm2t5/a2bNmi+fPn6+OPP9Y999yjunXr6tNPP9XRo0ev6pz++ecfDR8+XE8//bRV1q1bN2VmZmr58uWSpJMnT+rzzz9X9+7dz9vHqFGj5OPjY20hISFXFQsAAABuLpI8FHtZWVk6ceKEGjZsaJX5+/srIiJCkrR+/XoVFBQoPDzcetbO09NTGRkZysrKuuxxqlevLmdnZ2s/ODjYulO3adMmubi4qG7dulZ91apV7d7Yebny8vLUtm1bRUZGatiwYVZ5uXLl1LZtW02ePFmS9O233+r48ePq1KnTefsZOHCgcnNzrW3Xrl1XHAsAAABuPl68AlxCfn6+nJ2dtWrVKrskTdIVvVylRIkSdvs2m02FhYXXJcYzDh06pNjYWHl5eWnWrFnnjPnkk0/q8ccf19tvv62kpCR17txZHh4e5+3L1dVVrq6u1zU+AAAA3HjcyUOxV6lSJZUoUULLli2zyg4cOKAtW7ZIkmrXrq2CggLl5OSocuXKdltQUNB1iaFq1ao6deqUVq1aZZVlZmbq4MGDl91HXl6e7r//fpUsWVLffPON3NzczmnTpk0blSpVShMmTNCCBQsuuFQTAAAAty/u5KHY8/T0VI8ePdS/f38FBASobNmyGjRokJycTv8fSHh4uBISEtSlSxeNGzdOtWvX1t9//63U1FTVrFlTbdu2veYYIiIiFBsbq2eeeUYTJkyQi4uLEhMT5e7uflnHn0nwjhw5os8++8zuRSllypSx7kA6OzurW7duGjhwoKpUqaKoqKhrjh0AAAC3Fu7kAZLefPNNNW3aVHFxcYqOjlaTJk3sno9LSkpSly5d1LdvX0VERCg+Pl4rVqxQaGjodYshKSlJ5cqVU/PmzdWxY0c9/fTTKlu27GUd++uvv2rZsmVav369KleurODgYGs7+1m6Hj166MSJE3riiSeuW+wAAAC4ddiMMaaogwBwfmFhYUpMTFRiYuJ163Px4sVq1aqVdu3apcDAwMs+Li8vTz4+PtZXMwAAAODmutzPYyzXBIqJ48eP6++//9awYcPUqVOnK0rwAAAAcPtguSZwG5g2bZrd1zf8e6tevfpl9TF9+nRVqFBBBw8e1JgxY25wxAAAACgqLNcEbgOHDh3S3r17z1tXokQJVahQ4YbHwHJNAACAosVyTcCBeHl5ycvLq6jDAAAAwG2A5ZoAAAAA4EBI8gAAAADAgZDkAQAAAIADIckDAAAAAAdCkgcAAAAADoQkDwAAAAAcCEkeAAAAADgQkjwAAAAAcCAkeQAAAADgQEjyAAAAAMCBkOQBAAAAgAMhyQMAAAAAB0KSBwAAAAAOhCQPAAAAABwISR4AAAAAOBCSPOA2NGzYMN19991FHQYAAABuQSR5uG21aNFCiYmJ55QnJyfL19f3psdzO5gxY4aqVq0qNzc31ahRQ/PmzSvqkAAAAHCdkeQBxcTPP/+sRx55RD169NDq1asVHx+v+Ph4bdiwoahDAwAAwHVEkgeH161bN8XHx2vs2LEKDg5WQECAevbsqZMnT1ptwsLCNHLkSHXv3l1eXl4KDQ3VpEmT7PoZMGCAwsPD5eHhoTvvvFNDhgyx6+PMEsrJkycrNDRUnp6eev7551VQUKAxY8YoKChIZcuW1YgRI+z6PXjwoJ588kmVKVNG3t7eatmypdauXWvXZvTo0QoMDJSXl5d69OihY8eOXfE8vPvuu4qNjVX//v1VrVo1DR8+XHXq1NH48eOvuC8AAADcukjyUCykpaUpKytLaWlpmjJlipKTk5WcnGzXZty4capXr55Wr16t559/Xs8995wyMzOtei8vLyUnJ2vjxo1699139fHHH+vtt9+26yMrK0vz58/XggULNH36dH366adq27at/vjjD2VkZOiNN97Q4MGDtWzZMuuYTp06KScnR/Pnz9eqVatUp04dtWrVSvv375ckffnllxo2bJhGjhyplStXKjg4WB9++KHduOnp6bLZbNqxY8cF52Dp0qWKjo62K4uJidHSpUvP2/748ePKy8uz2wAAAHDrI8lDseDn56fx48eratWqateundq2bavU1FS7Nm3atNHzzz+vypUra8CAASpdurTS0tKs+sGDB6tRo0YKCwtTXFyc+vXrpy+//NKuj8LCQk2ePFmRkZGKi4vTvffeq8zMTL3zzjuKiIjQE088oYiICKvfn376ScuXL9eMGTNUr149ValSRWPHjpWvr6+++uorSdI777yjHj16qEePHoqIiNDrr7+uyMhIu3E9PDwUERGhEiVKXHAO9uzZo8DAQLuywMBA7dmz57ztR40aJR8fH2sLCQm5xCwDAADgVkCSh2KhevXqcnZ2tvaDg4OVk5Nj16ZmzZrWzzabTUFBQXZtvvjiCzVu3FhBQUHy9PTU4MGDlZ2dbddHWFiYvLy8rP3AwEBFRkbKycnJruxMv2vXrlV+fr4CAgLk6elpbdu3b1dWVpYkadOmTWrYsKHdOFFRUXb7DRo00ObNm1W+fPkrmpeLGThwoHJzc61t165d161vAAAA3DguRR0AcLW8vb2Vm5t7TvnBgwfl4+NjV3b2HS6bzabCwsLLbrN06VIlJCTo1VdfVUxMjHx8fJSSkqJx48Zdso+L9Zufn6/g4GClp6efcx7X+w2hQUFB2rt3r13Z3r17FRQUdN72rq6ucnV1va4xAAAA4MbjTh5uWxEREfr111/PKf/1118VHh5+Xcf6+eefVaFCBQ0aNMhaVrlz585r7rdOnTras2ePXFxcVLlyZbutdOnSkqRq1arZPcMnSb/88ssVjxUVFXXOEtVFixadc1cQAAAAtzeSPNy2nnvuOW3ZskW9e/fWunXrlJmZqbfeekvTp09X3759r+tYVapUUXZ2tlJSUpSVlaX33ntPs2bNuuZ+o6OjFRUVpfj4eC1cuFA7duzQzz//rEGDBmnlypWSpD59+mjy5MlKSkrSli1bNHToUP322292/SxfvlxVq1bVn3/+ecGx+vTpowULFmjcuHHavHmzhg0bppUrV6pXr17XfB4AAAC4dZDk4bZ155136scff9TmzZsVHR2thg0b6ssvv9SMGTMUGxt7Xcd64IEH9OKLL6pXr166++679fPPP2vIkCHX3K/NZtO8efPUrFkzPfHEEwoPD9fDDz+snTt3Wi9J6dy5s4YMGaKXX35ZdevW1c6dO/Xcc8/Z9XPkyBFlZmbafaXD2Ro1aqTPP/9ckyZNUq1atfTVV19p9uzZuuuuu675PAAAAHDrsBljTFEHAeDWl5eXJx8fH+Xm5srb27uowwEAACh2LvfzGHfyAAAAAMCBkOQBAAAAgAMhyQMAAAAAB0KSBwAAAAAOhCQPAAAAABwISR4AAAAAOBCSPAAAAABwICR5AAAAAOBASPIAAAAAwIGQ5AEAAACAAyHJAwAAAAAHQpIHAAAAAA6EJA8AAAAAHAhJHgAAAAA4EJI8AAAAAHAgJHkAAAAA4EBI8gAAAADAgZDkAQAAAIADIckDAAAAAAdCkgcAAAAADoQkDwAAAAAcCEkeAAAAADgQkjwAAAAAcCAkeQAAAADgQEjyAAAAAMCBkOQBAAAAgAMhyQMAAAAAB0KSBwAAAAAOxKWoAwBwezDGSJLy8vKKOBIAAIDi6cznsDOfyy6EJA/AZdm3b58kKSQkpIgjAQAAKN4OHTokHx+fC9aT5AG4LP7+/pKk7Ozsi/5SKU7y8vIUEhKiXbt2ydvbu6jDKXLMhz3mwx7zYY/5sMd8nIs5scd8nGaM0aFDh1SuXLmLtiPJA3BZnJxOP8Lr4+NTrH+5no+3tzdz8i/Mhz3mwx7zYY/5sMd8nIs5scd86LL+s50XrwAAAACAAyHJAwAAAAAHQpIH4LK4urpq6NChcnV1LepQbhnMiT3mwx7zYY/5sMd82GM+zsWc2GM+rozNXOr9mwAAAACA2wZ38gAAAADAgZDkAQAAAIADIckDAAAAAAdCkgfgsnzwwQcKCwuTm5ubGjZsqOXLlxd1SDfFjz/+qLi4OJUrV042m02zZ8+2qzfG6L///a+Cg4Pl7u6u6Ohobd26tWiCvQlGjRql+vXry8vLS2XLllV8fLwyMzPt2hw7dkw9e/ZUQECAPD099Z///Ed79+4toohvrAkTJqhmzZrW9zZFRUVp/vz5Vn1xmovzGT16tGw2mxITE62y4jYnw4YNk81ms9uqVq1q1Re3+ZCkP//8U4899pgCAgLk7u6uGjVqaOXKlVZ9cfq9GhYWds71YbPZ1LNnT0nF7/ooKCjQkCFDVLFiRbm7u6tSpUoaPny4/v0KkeJ0fVwLkjwAl/TFF1/opZde0tChQ/Xrr7+qVq1aiomJUU5OTlGHdsMdPnxYtWrV0gcffHDe+jFjxui9997TRx99pGXLlqlUqVKKiYnRsWPHbnKkN0dGRoZ69uypX375RYsWLdLJkyd1//336/Dhw1abF198Ud9++61mzJihjIwM/fXXX+rYsWMRRn3j3HHHHRo9erRWrVqllStXqmXLlmrfvr1+++03ScVrLs62YsUKTZw4UTVr1rQrL45zUr16de3evdvafvrpJ6uuuM3HgQMH1LhxY5UoUULz58/Xxo0bNW7cOPn5+VltitPv1RUrVthdG4sWLZIkderUSVLxuz7eeOMNTZgwQePHj9emTZv0xhtvaMyYMXr//fetNsXp+rgmBgAuoUGDBqZnz57WfkFBgSlXrpwZNWpUEUZ180kys2bNsvYLCwtNUFCQefPNN62ygwcPGldXVzN9+vQiiPDmy8nJMZJMRkaGMeb0+ZcoUcLMmDHDarNp0yYjySxdurSowryp/Pz8zCeffFKs5+LQoUOmSpUqZtGiRaZ58+amT58+xpjieX0MHTrU1KpV67x1xXE+BgwYYJo0aXLB+uL+e7VPnz6mUqVKprCwsFheH23btjXdu3e3K+vYsaNJSEgwxnB9XAnu5AG4qBMnTmjVqlWKjo62ypycnBQdHa2lS5cWYWRFb/v27dqzZ4/d3Pj4+Khhw4bFZm5yc3MlSf7+/pKkVatW6eTJk3ZzUrVqVYWGhjr8nBQUFCglJUWHDx9WVFRUsZ6Lnj17qm3btnbnLhXf62Pr1q0qV66c7rzzTiUkJCg7O1tS8ZyPb775RvXq1VOnTp1UtmxZ1a5dWx9//LFVX5x/r544cUKfffaZunfvLpvNViyvj0aNGik1NVVbtmyRJK1du1Y//fSTWrduLal4Xx9XyqWoAwBwa/vnn39UUFCgwMBAu/LAwEBt3ry5iKK6NezZs0eSzjs3Z+ocWWFhoRITE9W4cWPdddddkk7PScmSJeXr62vX1pHnZP369YqKitKxY8fk6empWbNmKTIyUmvWrCl2cyFJKSkp+vXXX7VixYpz6orj9dGwYUMlJycrIiJCu3fv1quvvqqmTZtqw4YNxXI+fv/9d02YMEEvvfSS/t//+39asWKFevfurZIlS6pr167F+vfq7NmzdfDgQXXr1k1S8fz78sorrygvL09Vq1aVs7OzCgoKNGLECCUkJEji390rQZIHALgqPXv21IYNG+yeLyqOIiIitGbNGuXm5uqrr75S165dlZGRUdRhFYldu3apT58+WrRokdzc3Io6nFvCmTsQklSzZk01bNhQFSpU0Jdffil3d/cijKxoFBYWql69eho5cqQkqXbt2tqwYYM++ugjde3atYijK1qffvqpWrdurXLlyhV1KEXmyy+/1LRp0/T555+revXqWrNmjRITE1WuXLlif31cKZZrArio0qVLy9nZ+Zy3ee3du1dBQUFFFNWt4cz5F8e56dWrl+bMmaO0tDTdcccdVnlQUJBOnDihgwcP2rV35DkpWbKkKleurLp162rUqFGqVauW3n333WI5F6tWrVJOTo7q1KkjFxcXubi4KCMjQ++9955cXFwUGBhY7ObkbL6+vgoPD9e2bduK5TUSHBysyMhIu7Jq1apZS1iL6+/VnTt36vvvv9eTTz5plRXH66N///565ZVX9PDDD6tGjRp6/PHH9eKLL2rUqFGSiu/1cTVI8gBcVMmSJVW3bl2lpqZaZYWFhUpNTVVUVFQRRlb0KlasqKCgILu5ycvL07Jlyxx2bowx6tWrl2bNmqUffvhBFStWtKuvW7euSpQoYTcnmZmZys7Odtg5OVthYaGOHz9eLOeiVatWWr9+vdasWWNt9erVU0JCgvVzcZuTs+Xn5ysrK0vBwcHF8hpp3LjxOV+7smXLFlWoUEFS8fy9KklJSUkqW7as2rZta5UVx+vjyJEjcnKyT0+cnZ1VWFgoqfheH1elqN/8AuDWl5KSYlxdXU1ycrLZuHGjefrpp42vr6/Zs2dPUYd2wx06dMisXr3arF692kgyb731llm9erXZuXOnMcaY0aNHG19fX/P111+bdevWmfbt25uKFSuao0ePFnHkN8Zzzz1nfHx8THp6utm9e7e1HTlyxGrz7LPPmtDQUPPDDz+YlStXmqioKBMVFVWEUd84r7zyisnIyDDbt28369atM6+88oqx2Wxm4cKFxpjiNRcX8u+3axpT/Oakb9++Jj093Wzfvt0sWbLEREdHm9KlS5ucnBxjTPGbj+XLlxsXFxczYsQIs3XrVjNt2jTj4eFhPvvsM6tNcfu9WlBQYEJDQ82AAQPOqStu10fXrl1N+fLlzZw5c8z27dvNzJkzTenSpc3LL79stSlu18fVIskDcFnef/99ExoaakqWLGkaNGhgfvnll6IO6aZIS0szks7Zunbtaow5/TrnIUOGmMDAQOPq6mpatWplMjMzizboG+h8cyHJJCUlWW2OHj1qnn/+eePn52c8PDxMhw4dzO7du4su6Buoe/fupkKFCqZkyZKmTJkyplWrVlaCZ0zxmosLOTvJK25z0rlzZxMcHGxKlixpypcvbzp37my2bdtm1Re3+TDGmG+//dbcddddxtXV1VStWtVMmjTJrr64/V797rvvjKTznmNxuz7y8vJMnz59TGhoqHFzczN33nmnGTRokDl+/LjVprhdH1fLZsy/vkIeAAAAAHBb45k8AAAAAHAgJHkAAAAA4EBI8gAAAADAgZDkAQAAAIADIckDAAAAAAdCkgcAAAAADoQkDwAAAAAcCEkeAAAAADgQkjwAAHBBLVq0UGJi4jX3061bN8XHx19zPzdDWFiY3nnnnaIOQ0OGDNHTTz99TX288soreuGFF65TRABuFyR5AAAUI926dZPNZtOzzz57Tl3Pnj1ls9nUrVs3q2zmzJkaPnz4NY/77rvvKjk5+Zr7KS727Nmjd999V4MGDbLKpk2bppCQEPn5+emll16ya79jxw6Fh4crLy/Prrxfv36aMmWKfv/995sSN4BbA0keAADFTEhIiFJSUnT06FGr7NixY/r8888VGhpq19bf319eXl7XPKaPj498fX2vuZ/i4pNPPlGjRo1UoUIFSdI///yjJ598UmPHjtXChQv12Wefac6cOVb7559/XqNHj5a3t7ddP6VLl1ZMTIwmTJhwU+MHULRI8gAAKGbq1KmjkJAQzZw50yqbOXOmQkNDVbt2bbu2Zy/X/PDDD1WlShW5ubkpMDBQDz74oFX31VdfqUaNGnJ3d1dAQICio6N1+PBhSecu12zRooV69+6tl19+Wf7+/goKCtKwYcPsxt68ebOaNGkiNzc3RUZG6vvvv5fNZtPs2bPPe16TJk1SuXLlVFhYaFfevn17de/eXZKUlZWl9u3bKzAwUJ6enqpfv76+//77C87Vjh07ZLPZtGbNGqvs4MGDstlsSk9Pt8o2bNig1q1by9PTU4GBgXr88cf1zz//XNbcnE9KSori4uKs/d9//10+Pj7q3Lmz6tevr3vvvVebNm2SJE2fPl0lSpRQx44dz9tXXFycUlJSLjgWAMdDkgcAQDHUvXt3JSUlWfuTJ0/WE088cdFjVq5cqd69e+u1115TZmamFixYoGbNmkmSdu/erUceeUTdu3fXpk2blJ6ero4dO8oYc8H+pkyZolKlSmnZsmUaM2aMXnvtNS1atEiSVFBQoPj4eHl4eGjZsmWaNGmS3dLF8+nUqZP27duntLQ0q2z//v1asGCBEhISJEn5+flq06aNUlNTtXr1asXGxiouLk7Z2dkXn7CLOHjwoFq2bKnatWtr5cqVWrBggfbu3auHHnroquZm//792rhxo+rVq2eVValSRUeOHNHq1au1f/9+rVixQjVr1tSBAwc0ZMgQjR8//oLxNWjQQH/88Yd27Nhx1ecI4PbiUtQBAACAm++xxx7TwIEDtXPnTknSkiVLlJKSYnd36mzZ2dkqVaqU2rVrJy8vL1WoUMG687d7926dOnVKHTt2tJYY1qhR46Ix1KxZU0OHDpV0OokZP368UlNTdd9992nRokXKyspSenq6goKCJEkjRozQfffdd8H+/Pz81Lp1a33++edq1aqVpNN30EqXLq17771XklSrVi3VqlXLOmb48OGaNWuWvvnmG/Xq1eui8V7I+PHjVbt2bY0cOdIqmzx5skJCQrRlyxbl5+df0dxkZ2fLGKNy5crZnduUKVPUpUsXHT16VF26dFFMTIx69OihXr16afv27XrggQd08uRJDRs2zO4O65l+du7cqbCwsKs6RwC3F5I8AACKoTJlyqht27ZKTk6WMUZt27ZV6dKlL3rMfffdpwoVKujOO+9UbGysYmNj1aFDB3l4eKhWrVpq1aqVatSooZiYGN1///168MEH5efnd8H+atasabcfHBysnJwcSVJmZqZCQkKsBE86fUfqUhISEvTUU0/pww8/lKurq6ZNm6aHH35YTk6nFy/l5+dr2LBhmjt3rpWYHj169Jru5K1du1ZpaWny9PQ8py4rK0v333//Fc3NmWcl3dzc7Mo7dOigDh06WPsZGRlat26d3n//fVWuXFnTp09XUFCQGjRooGbNmqls2bKSJHd3d0nSkSNHrvocAdxeWK4JAEAx1b17dyUnJ2vKlCnWM2sX4+XlpV9//VXTp09XcHCw/vvf/6pWrVo6ePCgnJ2dtWjRIs2fP1+RkZF6//33FRERoe3bt1+wvxIlStjt22y2c56nu1JxcXEyxmju3LnatWuXFi9ebC3VlE6/bXLWrFkaOXKkFi9erDVr1qhGjRo6ceLEefs7kxz+e2nlyZMn7drk5+crLi5Oa9assdu2bt2qZs2aXfHcnEm2Dxw4cMHzPH78uJ5//nlNnDhR27Zt06lTp9S8eXNFREQoPDxcy5Yts9ru379f0unEHkDxQJIHAEAxFRsbqxMnTujkyZOKiYm5rGNcXFwUHR2tMWPGaN26ddqxY4d++OEHSaeTtMaNG+vVV1/V6tWrVbJkSc2aNeuqYouIiNCuXbu0d+9eq2zFihWXPM7NzU0dO3bUtGnTNH36dEVERKhOnTpW/ZIlS9StWzd16NBBNWrUUFBQ0EWfVTuTGO3evdsq+/dLWKTTL7L57bffFBYWpsqVK9ttpUqVknRlc1OpUiV5e3tr48aNF4zr9ddfV2xsrOrUqaOCggKdOnXKqjt58qQKCgqs/Q0bNqhEiRKqXr36BfsD4FhYrgkAQDHl7OxsvaHR2dn5ku3nzJmj33//Xc2aNZOfn5/mzZunwsJCRUREaNmyZUpNTdX999+vsmXLatmyZfr7779VrVq1q4rtvvvuU6VKldS1a1eNGTNGhw4d0uDBgyWdTpguJiEhQe3atdNvv/2mxx57zK6uSpUqmjlzpuLi4mSz2TRkyJCL3j10d3fXPffco9GjR6tixYrKycmx4jijZ8+e+vjjj/XII49Ybwvdtm2bUlJS9Mknn2jlypVXNDdOTk6Kjo7WTz/9dN4vkN+4caO++OILrV69WpJUtWpVOTk56dNPP1VQUJA2b96s+vXrW+0XL16spk2bWss2ATg+7uQBAFCMeXt7n/Pdahfi6+urmTNnqmXLlqpWrZo++ugjTZ8+XdWrV5e3t7d+/PFHtWnTRuHh4Ro8eLDGjRun1q1bX1Vczs7Omj17tvLz81W/fn09+eST1ts1z35W7WwtW7aUv7+/MjMz9eijj9rVvfXWW/Lz81OjRo0UFxenmJgYuzt95zN58mSdOnVKdevWVWJiol5//XW7+nLlymnJkiUqKCjQ/fffrxo1aigxMVG+vr5ycnK6qrl58sknlZKSck4CaozR008/rbfeesu6S+ju7q7k5GS99tpr6tGjh8aPH6/y5ctbx6SkpOipp5666DkCcCw2c7F3GwMAANwilixZoiZNmmjbtm2qVKlSUYdzQxlj1LBhQ7344ot65JFHrrqf+fPnq2/fvlq3bp1cXFjABRQX/G0HAAC3pFmzZsnT01NVqlTRtm3b1KdPHzVu3NjhEzzp9JLUSZMmaf369dfUz+HDh5WUlESCBxQz3MkDAAC3pP/97396/fXXlZ2drdKlSys6Olrjxo1TQEBAUYcGALc0kjwAAAAAcCC8eAUAAAAAHAhJHgAAAAA4EJI8AAAAAHAgJHkAAAAA4EBI8gAAAADAgZDkAQAAAIADIckDAAAAAAdCkgcAAAAADoQkDwAAAAAcyP8HJVPIUH8lZWcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualise the variables with the greatest missingness.\n",
        "missing_plot = (\n",
        "    data_raw.isna().mean()\n",
        "    .mul(100)\n",
        "    .sort_values(ascending=False)\n",
        "    .head(15)\n",
        "    .sort_values()\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(9, 6))\n",
        "plt.barh(missing_plot.index, missing_plot.values)\n",
        "plt.xlabel(\"Missing values (%)\")\n",
        "plt.ylabel(\"Variable\")\n",
        "plt.title(\"Variables with the highest missingness\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "74321f4e",
      "metadata": {
        "id": "74321f4e"
      },
      "source": [
        "### Data-quality interpretation\n",
        "\n",
        "Missingness is partly structural rather than random. Joint-income variables are absent for individual applications, while missing delinquency-history fields may indicate no recorded event. Deleting incomplete rows would therefore remove information and could distort the sample. The modelling pipelines use median imputation with numerical missingness indicators and an explicit categorical `Missing` level. All fitted preprocessing is confined to training folds."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b58f83a1",
      "metadata": {
        "id": "b58f83a1"
      },
      "source": [
        "## 3. Target design: censoring-aware primary and sensitivity experiments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "6303fd84",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        },
        "id": "6303fd84",
        "outputId": "f50da28e-ffad-4b91-e318-31546fd97a9c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    count  percentage\n",
              "loan_status                          \n",
              "Current              9375     93.7500\n",
              "Fully Paid            447      4.4700\n",
              "In Grace Period        67      0.6700\n",
              "Late (31-120 days)     66      0.6600\n",
              "Late (16-30 days)      38      0.3800\n",
              "Charged Off             7      0.0700"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc50e07c-1fa0-4729-8df6-8169434bb4dd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>percentage</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Current</th>\n",
              "      <td>9375</td>\n",
              "      <td>93.7500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>447</td>\n",
              "      <td>4.4700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>In Grace Period</th>\n",
              "      <td>67</td>\n",
              "      <td>0.6700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (31-120 days)</th>\n",
              "      <td>66</td>\n",
              "      <td>0.6600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (16-30 days)</th>\n",
              "      <td>38</td>\n",
              "      <td>0.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charged Off</th>\n",
              "      <td>7</td>\n",
              "      <td>0.0700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc50e07c-1fa0-4729-8df6-8169434bb4dd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bc50e07c-1fa0-4729-8df6-8169434bb4dd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bc50e07c-1fa0-4729-8df6-8169434bb4dd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_2758fd8e-a537-4475-9b64-8993cbb9216c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('original_status')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2758fd8e-a537-4475-9b64-8993cbb9216c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('original_status');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "original_status",
              "summary": "{\n  \"name\": \"original_status\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"loan_status\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Current\",\n          \"Fully Paid\",\n          \"Charged Off\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3779,\n        \"min\": 7,\n        \"max\": 9375,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          9375,\n          447,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.79791140614342,\n        \"min\": 0.06999999999999999,\n        \"max\": 93.75,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          93.75,\n          4.47,\n          0.06999999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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HB8vd3V01a9bUSy+9pDRp0qh9+/bq1auXbDabvv7664cu/Va6dGktWLBA/fr1U9myZZUqVSq98sorKly4sCpUqKDBgwfr+vXrSps2rX744YcYCfbGjRvVs2dPNW/eXPny5VNkZKS+/vprubq6qlmzZo88Pzc3N3344Yfq1q2batasqTfeeEOnT5/WnDlz/vVzPXbsmGrVqqXXX39dhQoVUpIkSbR06VIFBgaqRYsWDuf22Wef6cMPP1SePHmUMWNG1axZMzYfgyVfvnzq3LmzfvvtN2XKlEmzZ89WYGCg5syZY5WpW7eusmXLps6dO+vdd9+Vq6urZs+erQwZMujcuXMOx3vS2J60bgIAnqF4nDkdAJBA7d+/37Rs2dJkzpzZuLm5GW9vb9OyZUvz119/xShrX/boypUrj9x3v9DQUOPv72/Spk1rUqVKZRo3bmyOHj1qJJmxY8da5R61FJOfn1+M96lWrZrDckp37941/fv3N5kzZzbJkyc3lSpVMjt27IhR7mmXDLNbsGCBKVmypHF3dzdp06Y1rVu3Nn///bdDmb///ts0adLEpE6d2nh5eZnmzZubixcvxlgi7VF/x0ctxfWgxYsXm7p165qMGTOapEmTmmzZsplu3bqZS5cuOZT78ssvTa5cuYyrq6vDOW3bts1UqFDBJE+e3Pj4+JgBAwaYtWvXxjjvW7dumVatWpnUqVMbSQ7Lh508edLUrl3buLu7m0yZMpn33nvPrF+/3uEYp06dMp06dTK5c+c2yZIlM2nTpjU1atQwP//882PPz27GjBkmZ86cxt3d3ZQpU8Zs2bLlXz/Xq1evGn9/f1OgQAGTMmVK4+XlZcqXL28WLlzocOyAgADj5+dnPDw8HJYhs38Gv/32W4x4HldP165da4oVK2bc3d1NgQIFHrpM2p49e0z58uWtz2zixIkPPeajYvsvdbN9+/YmZcqUMWJ61FJmAICHsxnzkMfUAAA8R/bt26eSJUvqm2++UevWreM7HAAAgCfGmG4AwHPlzp07MbZ9+umncnFxUdWqVeMhIgAAgKfHmG4AwHNl/Pjx2rNnj2rUqKEkSZJo9erVWr16tbp27SpfX9/4Dg8AACBW6F4OAHiurF+/XiNHjtShQ4d069YtZcuWTW3bttWQIUOUJAnPigEAQMJC0g0AAAAAgJMwphsAAAAAACch6QYAAAAAwEkYHPcEoqOjdfHiRXl4eMhms8V3OAAAAACAeGaM0c2bN+Xj4yMXl0e3Z5N0P4GLFy8yYy4AAAAAIIbz588ra9asj9xP0v0EPDw8JN37Y3p6esZzNAAAAACA+BYSEiJfX18rX3wUku4nYO9S7unpSdINAAAAALD82xBkJlIDAAAAAMBJSLoBAAAAAHASkm4AAAAAAJyEpBsAAAAAACch6QYAAAAAwElIugEAAAAAcBKSbgAAAAAAnISkGwAAAAAAJyHpBgAAAADASUi6AQAAAABwEpJuAAAAAACchKQbAAAAAAAnIekGAAAAAMBJSLoBAAAAAHCSJPEdAJ6tHINWxXcI+I/OjPWL7xAAAAAAPCO0dAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4Sbwm3VFRUXr//feVM2dOJU+eXLlz59YHH3wgY4xVxhijYcOGKXPmzEqePLlq166t48ePOxzn+vXrat26tTw9PZU6dWp17txZt27dciizf/9+ValSRcmSJZOvr6/Gjx8fJ+cIAAAAAHhxxWvSPW7cOH322WeaNm2aDh8+rHHjxmn8+PGaOnWqVWb8+PGaMmWKPv/8c+3atUspU6ZUvXr1dPfuXatM69atdfDgQa1fv14rV67Uli1b1LVrV2t/SEiI6tatq+zZs2vPnj36+OOPNWLECM2cOTNOzxcAAAAA8GKxmfubleNYw4YNlSlTJs2aNcva1qxZMyVPnlzffPONjDHy8fFR//799c4770iSgoODlSlTJs2dO1ctWrTQ4cOHVahQIf32228qU6aMJGnNmjV6+eWX9ffff8vHx0efffaZhgwZooCAACVNmlSSNGjQIP344486cuTIv8YZEhIiLy8vBQcHy9PT0wl/iWcnx6BV8R0C/qMzY/3iOwQAAAAA/+JJ88R4bel+6aWXtGHDBh07dkyS9Oeff2rr1q1q0KCBJOn06dMKCAhQ7dq1rdd4eXmpfPny2rFjhyRpx44dSp06tZVwS1Lt2rXl4uKiXbt2WWWqVq1qJdySVK9ePR09elRBQUEx4goLC1NISIjDDwAAAAAAsZUkPt980KBBCgkJUYECBeTq6qqoqCh99NFHat26tSQpICBAkpQpUyaH12XKlMnaFxAQoIwZMzrsT5IkidKmTetQJmfOnDGOYd+XJk0ah31jxozRyJEjn9FZAgAAAABeVPHa0r1w4UJ9++23+u6777R3717NmzdPn3zyiebNmxefYWnw4MEKDg62fs6fPx+v8QAAAAAAEqZ4bel+9913NWjQILVo0UKSVLRoUZ09e1ZjxoxR+/bt5e3tLUkKDAxU5syZrdcFBgaqRIkSkiRvb29dvnzZ4biRkZG6fv269Xpvb28FBgY6lLH/bi9zP3d3d7m7uz+bkwQAAAAAvLDitaX79u3bcnFxDMHV1VXR0dGSpJw5c8rb21sbNmyw9oeEhGjXrl2qWLGiJKlixYq6ceOG9uzZY5XZuHGjoqOjVb58eavMli1bFBERYZVZv3698ufPH6NrOQAAAAAAz0q8Jt2vvPKKPvroI61atUpnzpzR0qVLNXHiRDVp0kSSZLPZ1KdPH3344Ydavny5/vrrL7Vr104+Pj5q3LixJKlgwYKqX7++unTpot27d2vbtm3q2bOnWrRoIR8fH0lSq1atlDRpUnXu3FkHDx7UggULNHnyZPXr1y++Th0AAAAA8AKI1+7lU6dO1fvvv68ePXro8uXL8vHxUbdu3TRs2DCrzIABAxQaGqquXbvqxo0bqly5stasWaNkyZJZZb799lv17NlTtWrVkouLi5o1a6YpU6ZY+728vLRu3Tr5+/urdOnSSp8+vYYNG+awljcAAAAAAM9avK7TnVCwTjfiEut0AwAAAM+/BLFONwAAAAAAiRlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICT/OekOyoqSvv27VNQUNCziAcAAAAAgEQj1kl3nz59NGvWLEn3Eu5q1aqpVKlS8vX11ebNm591fAAAAAAAJFixTroXL16s4sWLS5JWrFih06dP68iRI+rbt6+GDBnyzAMEAAAAACChinXSffXqVXl7e0uSfvrpJzVv3lz58uVTp06d9Ndffz3zAAEAAAAASKhinXRnypRJhw4dUlRUlNasWaM6depIkm7fvi1XV9dnHiAAAAAAAAlVkti+oGPHjnr99deVOXNm2Ww21a5dW5K0a9cuFShQ4JkHCAAAAABAQhXrpHvEiBEqUqSIzp8/r+bNm8vd3V2S5OrqqkGDBj3zAAEAAAAASKhinXRL0muvvRZjW/v27f9zMAAAAAAAJCZPlXRv2LBBGzZs0OXLlxUdHe2wb/bs2c8kMAAAAAAAErpYJ90jR47UqFGjVKZMGWtcNwAAAAAAiCnWSffnn3+uuXPnqm3bts6IBwAAAACARCPWS4aFh4frpZdeckYsAAAAAAAkKrFOut9880199913zogFAAAAAIBEJdbdy+/evauZM2fq559/VrFixeTm5uawf+LEic8sOAAAAAAAErJYJ9379+9XiRIlJEkHDhxw2MekagAAAAAA/CPW3cs3bdr0yJ+NGzfGOoALFy6oTZs2SpcunZInT66iRYvq999/t/YbYzRs2DBlzpxZyZMnV+3atXX8+HGHY1y/fl2tW7eWp6enUqdOrc6dO+vWrVsOZfbv368qVaooWbJk8vX11fjx42MdKwAAAAAAsRHrpPtZCgoKUqVKleTm5qbVq1fr0KFDmjBhgtKkSWOVGT9+vKZMmaLPP/9cu3btUsqUKVWvXj3dvXvXKtO6dWsdPHhQ69ev18qVK7VlyxZ17drV2h8SEqK6desqe/bs2rNnjz7++GONGDFCM2fOjNPzBQAAAAC8WGzGGBPbF/3+++9auHChzp07p/DwcId9S5YseeLjDBo0SNu2bdOvv/760P3GGPn4+Kh///565513JEnBwcHKlCmT5s6dqxYtWujw4cMqVKiQfvvtN5UpU0aStGbNGr388sv6+++/5ePjo88++0xDhgxRQECAkiZNar33jz/+qCNHjvxrnCEhIfLy8lJwcLA8PT2f+PziQ45Bq+I7BPxHZ8b6xXcIAAAAAP7Fk+aJsW7p/uGHH/TSSy/p8OHDWrp0qSIiInTw4EFt3LhRXl5esTrW8uXLVaZMGTVv3lwZM2ZUyZIl9eWXX1r7T58+rYCAANWuXdva5uXlpfLly2vHjh2SpB07dih16tRWwi1JtWvXlouLi3bt2mWVqVq1qpVwS1K9evV09OhRBQUFxYgrLCxMISEhDj8AAAAAAMRWrJPu0aNHa9KkSVqxYoWSJk2qyZMn68iRI3r99deVLVu2WB3r1KlT+uyzz5Q3b16tXbtWb731lnr16qV58+ZJkgICAiRJmTJlcnhdpkyZrH0BAQHKmDGjw/4kSZIobdq0DmUedoz73+N+Y8aMkZeXl/Xj6+sbq/MCAAAAAEB6iqT75MmT8vO71/01adKkCg0Nlc1mU9++fWM9Rjo6OlqlSpXS6NGjVbJkSXXt2lVdunTR559/HtuwnqnBgwcrODjY+jl//ny8xgMAAAAASJhinXSnSZNGN2/elCRlyZLFWjbsxo0bun37dqyOlTlzZhUqVMhhW8GCBXXu3DlJkre3tyQpMDDQoUxgYKC1z9vbW5cvX3bYHxkZqevXrzuUedgx7n+P+7m7u8vT09PhBwAAAACA2Ip10l21alWtX79ektS8eXP17t1bXbp0UcuWLVWrVq1YHatSpUo6evSow7Zjx44pe/bskqScOXPK29tbGzZssPaHhIRo165dqlixoiSpYsWKunHjhvbs2WOV2bhxo6Kjo1W+fHmrzJYtWxQREWGVWb9+vfLnz+8wUzoAAAAAAM9SrJPuadOmqUWLFpKkIUOGqF+/fgoMDFSzZs00a9asWB2rb9++2rlzp0aPHq0TJ07ou+++08yZM+Xv7y9Jstls6tOnjz788EMtX75cf/31l9q1aycfHx81btxY0r2W8fr166tLly7avXu3tm3bpp49e6pFixby8fGRJLVq1UpJkyZV586ddfDgQS1YsECTJ09Wv379Ynv6AAAAAAA8sadaMuxZWrlypQYPHqzjx48rZ86c6tevn7p06WLtN8Zo+PDhmjlzpm7cuKHKlStrxowZypcvn1Xm+vXr6tmzp1asWCEXFxc1a9ZMU6ZMUapUqawy+/fvl7+/v3777TelT59eb7/9tgYOHPhEMbJkGOISS4YBAAAAz78nzROfKumOiorSjz/+qMOHD0uSChcurEaNGsnV1fXpI36OkXQjLpF0AwAAAM+/J80Tk8T2wCdOnJCfn5/+/vtv5c+fX9K9JbZ8fX21atUq5c6d++mjBgAAAAAgEYn1mO5evXopV65cOn/+vPbu3au9e/fq3Llzypkzp3r16uWMGAEAAAAASJBi3dL9yy+/aOfOnUqbNq21LV26dBo7dqwqVar0TIMDAAAAACAhi3VLt7u7u7VO9/1u3bqlpEmTPpOgAAAAAABIDGKddDds2FBdu3bVrl27ZIyRMUY7d+5U9+7d1ahRI2fECAAAAABAghTrpHvKlCnKnTu3KlasqGTJkilZsmSqVKmS8uTJo8mTJzsjRgAAAAAAEqRYj+lOnTq1li1bpuPHj+vIkSOSpIIFCypPnjzPPDgAAAAAABKyWCfddnnz5lXevHmfZSwAAAAAACQqT5R09+vX74kPOHHixKcOBgAAAACAxOSJku4//vjjiQ5ms9n+UzAAAAAAACQmT5R0b9q0ydlxAAAAAACQ6MR69nIAAAAAAPBkSLoBAAAAAHASkm4AAAAAAJyEpBsAAAAAACd5oqS7VKlSCgoKkiSNGjVKt2/fdmpQAAAAAAAkBk+UdB8+fFihoaGSpJEjR+rWrVtODQoAAAAAgMTgiZYMK1GihDp27KjKlSvLGKNPPvlEqVKlemjZYcOGPdMAAQAAAABIqJ4o6Z47d66GDx+ulStXymazafXq1UqSJOZLbTYbSTcAAAAAAP/viZLu/Pnz64cffpAkubi4aMOGDcqYMaNTAwMAAAAAIKF7oqT7ftHR0c6IAwAAAACARCfWSbcknTx5Up9++qkOHz4sSSpUqJB69+6t3LlzP9PgAAAAAABIyGK9TvfatWtVqFAh7d69W8WKFVOxYsW0a9cuFS5cWOvXr3dGjAAAAAAAJEixbukeNGiQ+vbtq7Fjx8bYPnDgQNWpU+eZBQcAAAAAQEIW65buw4cPq3PnzjG2d+rUSYcOHXomQQEAAAAAkBjEOunOkCGD9u3bF2P7vn37mNEcAAAAAID7xLp7eZcuXdS1a1edOnVKL730kiRp27ZtGjdunPr16/fMAwQAAAAAIKGKddL9/vvvy8PDQxMmTNDgwYMlST4+PhoxYoR69er1zAMEAAAAACChinXSbbPZ1LdvX/Xt21c3b96UJHl4eDzzwAAAAAAASOieap1uO5JtAAAAAAAeLdYTqQEAAAAAgCdD0g0AAAAAgJOQdAMAAAAA4CSxSrojIiJUq1YtHT9+3FnxAAAAAACQaMQq6XZzc9P+/fudFQsAAAAAAIlKrLuXt2nTRrNmzXJGLAAAAAAAJCqxXjIsMjJSs2fP1s8//6zSpUsrZcqUDvsnTpz4zIIDAAAAACAhi3XSfeDAAZUqVUqSdOzYMYd9Npvt2UQFAAAAAEAiEOuke9OmTc6IAwAAAACAROeplww7ceKE1q5dqzt37kiSjDHPLCgAAAAAABKDWCfd165dU61atZQvXz69/PLLunTpkiSpc+fO6t+//zMPEAAAAACAhCrWSXffvn3l5uamc+fOKUWKFNb2N954Q2vWrHmmwQEAAAAAkJDFekz3unXrtHbtWmXNmtVhe968eXX27NlnFhgAAAAAAAldrFu6Q0NDHVq47a5fvy53d/dnEhQAAAAAAIlBrJPuKlWqaP78+dbvNptN0dHRGj9+vGrUqPFMgwMAAAAAICGLdffy8ePHq1atWvr9998VHh6uAQMG6ODBg7p+/bq2bdvmjBgBAAAAAEiQYt3SXaRIER07dkyVK1fWq6++qtDQUDVt2lR//PGHcufO7YwYAQAAAABIkGLd0i1JXl5eGjJkyLOOBQAAAACAROWpku6goCDNmjVLhw8fliQVKlRIHTt2VNq0aZ9pcAAAAAAAJGSx7l6+ZcsW5ciRQ1OmTFFQUJCCgoI0ZcoU5cyZU1u2bHFGjAAAAAAAJEixbun29/fXG2+8oc8++0yurq6SpKioKPXo0UP+/v7666+/nnmQAAAAAAAkRLFu6T5x4oT69+9vJdyS5Orqqn79+unEiRPPNDgAAAAAABKyWCfdpUqVssZy3+/w4cMqXrz4MwkKAAAAAIDE4Im6l+/fv9/6d69evdS7d2+dOHFCFSpUkCTt3LlT06dP19ixY50TJQAAAAAACZDNGGP+rZCLi4tsNpv+rajNZlNUVNQzC+55ERISIi8vLwUHB8vT0zO+w3msHINWxXcI+I/OjPWL7xAAAAAA/IsnzROfqKX79OnTzywwAAAAAABeFE+UdGfPnt3ZcQAAAAAAkOjEeskwSbp48aK2bt2qy5cvKzo62mFfr169nklgAAAAAAAkdLGevXzu3LnKmTOnOnfurE8++USTJk2yfj799NOnDmTs2LGy2Wzq06ePte3u3bvy9/dXunTplCpVKjVr1kyBgYEOrzt37pz8/PyUIkUKZcyYUe+++64iIyMdymzevFmlSpWSu7u78uTJo7lz5z51nAAAAAAAPKlYJ93vv/++hg0bpuDgYJ05c0anT5+2fk6dOvVUQfz222/64osvVKxYMYftffv21YoVK7Ro0SL98ssvunjxopo2bWrtj4qKkp+fn8LDw7V9+3bNmzdPc+fO1bBhw6wyp0+flp+fn2rUqKF9+/apT58+evPNN7V27dqnihUAAAAAgCcV66T79u3batGihVxcYv3Sh7p165Zat26tL7/8UmnSpLG2BwcHa9asWZo4caJq1qyp0qVLa86cOdq+fbt27twpSVq3bp0OHTqkb775RiVKlFCDBg30wQcfaPr06QoPD5ckff7558qZM6cmTJigggULqmfPnnrttdc0adKkZxI/AAAAAACPEuvMuXPnzlq0aNEzC8Df319+fn6qXbu2w/Y9e/YoIiLCYXuBAgWULVs27dixQ5K0Y8cOFS1aVJkyZbLK1KtXTyEhITp48KBV5sFj16tXzzrGw4SFhSkkJMThBwAAAACA2Ir1RGpjxoxRw4YNtWbNGhUtWlRubm4O+ydOnPjEx/rhhx+0d+9e/fbbbzH2BQQEKGnSpEqdOrXD9kyZMikgIMAqc3/Cbd9v3/e4MiEhIbpz546SJ0/+0HMcOXLkE58HAAAAAAAP81RJ99q1a5U/f35Jks1ms/bd/+9/c/78efXu3Vvr169XsmTJYhuGUw0ePFj9+vWzfg8JCZGvr288RgQAAAAASIhinXRPmDBBs2fPVocOHf7TG+/Zs0eXL19WqVKlrG1RUVHasmWLpk2bprVr1yo8PFw3btxwaO0ODAyUt7e3JMnb21u7d+92OK59dvP7yzw443lgYKA8PT0f2sotSe7u7nJ3d/9P5wcAAAAAQKzHdLu7u6tSpUr/+Y1r1aqlv/76S/v27bN+ypQpo9atW1v/dnNz04YNG6zXHD16VOfOnVPFihUlSRUrVtRff/2ly5cvW2XWr18vT09PFSpUyCpz/zHsZezHAAAAAADAWWLd0t27d29NnTpVU6ZM+U9v7OHhoSJFijhsS5kypdKlS2dt79y5s/r166e0adPK09NTb7/9tipWrKgKFSpIkurWratChQqpbdu2Gj9+vAICAjR06FD5+/tbLdXdu3fXtGnTNGDAAHXq1EkbN27UwoULtWrVqv8UPwAAAAAA/ybWSffu3bu1ceNGrVy5UoULF44xkdqSJUueWXCTJk2Si4uLmjVrprCwMNWrV08zZsyw9ru6umrlypV66623VLFiRaVMmVLt27fXqFGjrDI5c+bUqlWr1LdvX02ePFlZs2bVV199pXr16j2zOAEAAAAAeBibMcbE5gUdO3Z87P45c+b8p4CeRyEhIfLy8lJwcLA8PT3jO5zHyjGIFvyE7sxYv/gOAQAAAMC/eNI8MdYt3YkxqQYAAAAAwBliPZEaAAAAAAB4MrFu6c6ZM+dj1+M+derUfwoIAAAAAIDEItZJd58+fRx+j4iI0B9//KE1a9bo3XfffVZxAQAAAACQ4D3VkmEPM336dP3+++//OSAAAAAAABKLZzamu0GDBvrf//73rA4HAAAAAECC98yS7sWLFytt2rTP6nAAAAAAACR4se5eXrJkSYeJ1IwxCggI0JUrVzRjxoxnGhwAAAAAAAlZrJPuxo0bO/zu4uKiDBkyqHr16ipQoMCzigsAAAAAgAQv1kn38OHDnREHAAAAAACJzjMb0w0AAAAAABw9cUu3i4uLw1juh7HZbIqMjPzPQQEAAAAAkBg8cdK9dOnSR+7bsWOHpkyZoujo6GcSFAAAAAAAicETJ92vvvpqjG1Hjx7VoEGDtGLFCrVu3VqjRo16psEBAAAAAJCQPdWY7osXL6pLly4qWrSoIiMjtW/fPs2bN0/Zs2d/1vEBAAAAAJBgxSrpDg4O1sCBA5UnTx4dPHhQGzZs0IoVK1SkSBFnxQcAAAAAQIL1xN3Lx48fr3Hjxsnb21vff//9Q7ubAwAAAACAf9iMMeZJCrq4uCh58uSqXbu2XF1dH1luyZIlzyy450VISIi8vLwUHBwsT0/P+A7nsXIMWhXfIeA/OjPWL75DAAAAAPAvnjRPfOKW7nbt2v3rkmEAAAAAAOAfT5x0z50714lhAAAAAACQ+DzV7OUAAAAAAODfkXQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAk8Zp0jxkzRmXLlpWHh4cyZsyoxo0b6+jRow5l7t69K39/f6VLl06pUqVSs2bNFBgY6FDm3Llz8vPzU4oUKZQxY0a9++67ioyMdCizefNmlSpVSu7u7sqTJ4/mzp3r7NMDAAAAALzg4jXp/uWXX+Tv76+dO3dq/fr1ioiIUN26dRUaGmqV6du3r1asWKFFixbpl19+0cWLF9W0aVNrf1RUlPz8/BQeHq7t27dr3rx5mjt3roYNG2aVOX36tPz8/FSjRg3t27dPffr00Ztvvqm1a9fG6fkCAAAAAF4sNmOMie8g7K5cuaKMGTPql19+UdWqVRUcHKwMGTLou+++02uvvSZJOnLkiAoWLKgdO3aoQoUKWr16tRo2bKiLFy8qU6ZMkqTPP/9cAwcO1JUrV5Q0aVINHDhQq1at0oEDB6z3atGihW7cuKE1a9b8a1whISHy8vJScHCwPD09nXPyz0iOQaviOwT8R2fG+sV3CAAAAAD+xZPmic/VmO7g4GBJUtq0aSVJe/bsUUREhGrXrm2VKVCggLJly6YdO3ZIknbs2KGiRYtaCbck1atXTyEhITp48KBV5v5j2MvYjwEAAAAAgDMkie8A7KKjo9WnTx9VqlRJRYoUkSQFBAQoadKkSp06tUPZTJkyKSAgwCpzf8Jt32/f97gyISEhunPnjpInT+6wLywsTGFhYdbvISEh//0EAQAAAAAvnOempdvf318HDhzQDz/8EN+haMyYMfLy8rJ+fH194zskAAAAAEAC9Fwk3T179tTKlSu1adMmZc2a1dru7e2t8PBw3bhxw6F8YGCgvL29rTIPzmZu//3fynh6esZo5ZakwYMHKzg42Po5f/78fz5HAAAAAMCLJ16TbmOMevbsqaVLl2rjxo3KmTOnw/7SpUvLzc1NGzZssLYdPXpU586dU8WKFSVJFStW1F9//aXLly9bZdavXy9PT08VKlTIKnP/Mexl7Md4kLu7uzw9PR1+AAAAAACIrXgd0+3v76/vvvtOy5Ytk4eHhzUG28vLS8mTJ5eXl5c6d+6sfv36KW3atPL09NTbb7+tihUrqkKFCpKkunXrqlChQmrbtq3Gjx+vgIAADR06VP7+/nJ3d5ckde/eXdOmTdOAAQPUqVMnbdy4UQsXLtSqVcz0DQAAAABwnnht6f7ss88UHBys6tWrK3PmzNbPggULrDKTJk1Sw4YN1axZM1WtWlXe3t5asmSJtd/V1VUrV66Uq6urKlasqDZt2qhdu3YaNWqUVSZnzpxatWqV1q9fr+LFi2vChAn66quvVK9evTg9XwAAAADAi+W5Wqf7ecU63YhLrNMNAAAAPP8S5DrdAAAAAAAkJiTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJCTdAAAAAAA4CUk3AAAAAABOQtINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATpIkvgMAEP9yDFoV3yHgPzoz1i++QwAAAMBD0NINAAAAAICTkHQDAAAAAOAkJN0AAAAAADgJSTcAAAAAAE5C0g0AAAAAgJOQdAMAAAAA4CQk3QAAAAAAOAlJNwAAAAAATkLSDQAAAACAk5B0AwAAAADgJC9U0j19+nTlyJFDyZIlU/ny5bV79+74DgkAAAAAkIi9MEn3ggUL1K9fPw0fPlx79+5V8eLFVa9ePV2+fDm+QwMAAAAAJFJJ4juAuDJx4kR16dJFHTt2lCR9/vnnWrVqlWbPnq1BgwbFc3QAkLDkGLQqvkPAM3BmrF+cvh/1JnGI63oDAAndC5F0h4eHa8+ePRo8eLC1zcXFRbVr19aOHTviMTIAAAA8Dg9rEgce1uBF9kIk3VevXlVUVJQyZcrksD1Tpkw6cuRIjPJhYWEKCwuzfg8ODpYkhYSEODfQZyA67HZ8h4D/KD7qGfUm4YvrekOdSRyoN3ga1Bs8jbiuN0WGr43T94NzHBhZL75DeCx7vTbGPLbcC5F0x9aYMWM0cuTIGNt9fX3jIRq8aLw+je8IkBBRb/A0qDd4GtQbPA3qDZ5GQqk3N2/elJeX1yP3vxBJd/r06eXq6qrAwECH7YGBgfL29o5RfvDgwerXr5/1e3R0tK5fv6506dLJZrM5PV48XEhIiHx9fXX+/Hl5enrGdzhIIKg3eBrUGzwN6g2eBvUGT4N683wwxujmzZvy8fF5bLkXIulOmjSpSpcurQ0bNqhx48aS7iXSGzZsUM+ePWOUd3d3l7u7u8O21KlTx0GkeBKenp5cXBBr1Bs8DeoNngb1Bk+DeoOnQb2Jf49r4bZ7IZJuSerXr5/at2+vMmXKqFy5cvr0008VGhpqzWYOAAAAAMCz9sIk3W+88YauXLmiYcOGKSAgQCVKlNCaNWtiTK4GAAAAAMCz8sIk3ZLUs2fPh3YnR8Lg7u6u4cOHx+j6DzwO9QZPg3qDp0G9wdOg3uBpUG8SFpv5t/nNAQAAAADAU3GJ7wAAAAAAAEisSLoBAAAAAHASkm4ACV5UVFR8hwAgkYuOjo7vEAC8YEJCQrj2JBIk3QASrIiICNWvX1+TJ0+O71CQgNy6dUsDBw7UsWPH4jsUJBBXrlxRQECA9TvT4eBJ3Z8wUW/wpMLCwtSkSRO1bdtW169fj+9w8AyQdOO5YYzhaR5ixc3NTfnz59eECRN08eLF+A4HCcS+ffv0008/ady4cfEdChKAmzdv6oMPPlCDBg30008/SZJsNls8R4WEYP369apVq5Z+//13SdQbPJnIyEi98847OnfunEaPHq306dPHd0h4Bki6Ee/27NmjBQsWyGazycXFRefOnZNEVz48nr1L+dixY+Xi4qKPP/44niNCQlG+fHl17NhR27Zt088//xzf4eA55+HhoSlTpqhRo0YaO3asBg4cGN8hIYGoUqWKSpQooT59+uiDDz6I73DwnFu9erV+/vlnubi4aNeuXWrQoIEKFy6sbdu2xXdoeAZIuhHv5s6dqzfffFO//PKLGjRooCpVqigkJEQuLlRPPJwxRq6urpKkq1ev6v3339dXX32lffv2xW9geK7ZH9S4ubmpcuXKqlChgiZMmBDPUeF5ZYyRMcaqN++9956GDBmiCRMmaPLkybpx40b8BojnWnR0tJIlS6YJEyZoyJAhmjRpkkaNGqUrV67Ed2h4zty9e1fdu3eXn5+fdf/bpk0bTZs2TdWrV1eVKlV09uzZ+A4T/xFZDeKNfWzT1KlTJUm1atWSu7u7du7cKU9Pz/gMDc+RgwcPav78+QoPD7e22Ww2nT59WlWqVFH9+vW1bt06hYaG6uOPP1ZEREQ8Rovnmaurq65fv6527dpp4sSJOnXqlDZv3qzvv/8+vkPDcyYyMlI2m836kaTkyZOrXr16Gj16tGbOnKnly5fHc5R4HkVGRkqS1XDg4uKiBg0aaNSoUfr555/16aefxmN0eB6dPHlSGzZs0K5du9S0aVNJ0pEjRxQSEqJr164pOjpa2bNnj+co8V+RdCNORUdHW93G7TcyM2fOlIeHh5IkSaL3339fmTNnjs8Q8ZxZsGCB9u7dKzc3N4ftw4YNk5ubmzZu3KghQ4boww8/1Pfff2+NuQQedOHCBTVo0EAXL15Ux44d5efnp4wZM2rSpEkKDg6O7/DwHEmSJIkkady4cRozZozDJGoDBgxQnjx5tGTJEh06dCi+QsRzxn5vY687Gzdu1PHjx6393bp1U7Vq1bRy5Upt3LgxXmLE8yl9+vQ6f/68Tp48qZkzZ6p69erKnz+/Bg4cqIMHD1qt3KzUkrDZDFMpIo4YY6xE+/Tp07p+/bpKly5t7S9RooRy5Mihr776ikkj4FBf7CIjI5UkSRJdvHhRxYsX15QpU9SyZUtJ976M2rZtqwMHDmjLli1KnTp1PESN50F0dLRDC6W9Lm3evFlNmjTRzp07lT9/fknSd999p08++UQNGjTQRx99FJ9hIx49eL1ZtWqVunfvrjRp0qhjx45q1KiRcufOraioKLm6umrDhg3y9/fXiBEj1KJFi3iMHM+bFStWqE+fPsqYMaP8/f3VvHlzubu7S5IOHDigoUOHysPDQ19//XU8R4q4Fh0d/cihk40bN9batWuVOnVqTZo0SS1atNC1a9fUuHFjubu76+eff37ofRESDlq64XT25zo2m03h4eHq2LGjSpUqpcaNG+vNN9/UX3/9JUmaNm2ali9frg0bNjCJGqwvlujoaF25ckVlypTRunXrZIyRt7e3UqVKZbVO2m+E33nnHR05coTuwi+wqKgoubi4yGazKTQ0VNI/den69evy9fV1uGl5+eWXVaZMGS1cuJAlxF5QD97IXr58WR9++KE6dOig/fv3q2/fvsqdO7ckWXNJ1KpVSxkzZtTatWslMfHni8re8mj//KdPn67u3bvrrbfe0pIlS+Tn5yd3d3drf5EiRVS2bFkFBgbqwIED8RY34pb983dxcdH169e1e/dunT592hoOt2zZMm3fvl02m01t2rRRixYtFB0drbRp02rQoEHatGmTfvrpJ9lsNmv4AhIekm44nf1mZuvWrZo2bZqioqK0adMmffTRR9q+fbs+++wzBQUFqXLlymrcuLE+/vhjawbz8PBwxui+YB7sPuXi4qIMGTIoNDRU06ZN0/Xr1xUaGqpq1appxYoVCgoKsm6E3dzc5ObmpjFjxujEiRPxET7iUGRkpMNYf+leUnT79m1169ZNfn5+euutt7Rq1SpJ97rwBQYG6tChQ9aNS+rUqVWyZEmdPHlS48ePj/NzQPyz2WyKiIjQp59+qmPHjunIkSP6+++/1aJFC0VFRWnnzp3atWuXNmzY4DB5Ws+ePbVmzRqFh4cz8ecLytXVVVFRUTp//rwk6bffftPLL7+sd955R5kyZVLKlCkVFhamsLAw6zX16tXToUOHdPv27fgKG3HMfn145513VLhwYb311lvKnz+/6tSpo+XLl6tOnTo6cOCAJk2apEmTJunvv/+2Hh5XrlxZbdq0kb+/v6R7wxfopJww8S2BZ84+4+v91q5dqxYtWmjq1Klq0qSJSpQooXbt2qlLly76448/tHDhQkn3xncfOXJEw4cP1wcffKCMGTNq9uzZ8XEaiGP2ZNueQF+8eNHhRmXBggVas2aNVqxYIQ8PD9WrV09Xr151mH3677//VqtWreTm5qajR4/G7QkgTm3YsEEVK1bU4cOHHbbv27dPRYsW1ZkzZ1SvXj2dOXNGTZs21bJly1S1alUVL15cn332mU6ePGm9JigoSMWLF9fGjRu1f//+uD4VxLGHtUpv2rRJH374oZIkSaLSpUvr9u3b8vf3V7FixfTBBx+oWbNm6ty5s9555x3rNdmyZVPJkiWth8R48dy+fVutW7fWgAEDZIzR9evXdf36dU2YMEH+/v5q06aNihQpoh49elivKVOmjHx8fKxx3SRQid+5c+dUq1Yta+LOefPmadGiRYqKilLz5s21du1aZcyYUa+88opKlCiht956y3qtl5eXevXqpZs3b2rkyJGSqDMJlgGeocjISOvfoaGhDvu6d+9uvLy8zOrVq61t169fN02aNDGNGzc2R48eNcYYM3/+fPPGG2+Y4sWLm2+//TZuAkeciY6ONtHR0Y/cP3PmTJMnTx5TpkwZU758eXPo0CETERFhjDGmbdu2Jm/evObChQvmzp07ZsKECSZp0qSmTp06pnXr1sbLy8t8/fXXJjw8PK5OB/Hk1q1bZt26dTG2T5w40ZQrV87cvHnT2ta6dWtTvHhxc/78eXP06FGTN29eU61aNfPFF1+YTz/91FSsWNF8++23JiAgIC5PAc8B+3dWRESESZ06tfnf//5njDFm27ZtZtSoUWbevHlm9erV5sqVK2bUqFGmZMmS5tixY8YYY44cOWLSpUtnLl++HG/xI+7cf39zv6ZNm5o+ffoYY4xZuXKlad26tcmdO7fp16+fGT16tJk6darx8vIyM2fOtI7Ts2dPM378+Md+FyLx+Oqrr0zJkiXNmTNnHLafO3fO1K5d2+TPn9/89ddfxhhj/ve//xmbzWY2b95slbtz547p37+/KVeunImKiorT2PHskHTjmQsLCzN9+vQxdevWNf7+/mbx4sXGGGP27t1rihUrZvr162du375tlV+4cKGpVKmSGTVqlLUtJCQkzuOG893/ZXHs2DFz6NAhc/XqVWOMMVevXjVt2rQxuXPnNl999ZXZvn27adKkialQoYLZvn27McaYmzdvmmTJkplhw4ZZx1q1apUZMGCAeeWVV8zGjRsd3osbmsTp/ocqf//9t9mxY4f1u/0BjDH3rkXG3Hu4lyZNGjNlyhRjjDG//PKL6dSpkylevLjx9fU133zzTRxGj/h2/fp10759e+sm1xhjrl27Zl5++WXz/vvvP/J1w4YNM40bN3bYNmrUKHPy5EmnxYrny6VLl8ypU6ccto0cOdLkzZvX+v3WrVvGGGM9LA4MDDSlS5e2HugYY4y/v7+ZPn16HESM+BYdHW1KlixpOnfu/ND9u3btMkmTJjUff/yxMcaYK1eumNdff90ULVrUoRz3xQkf3cvxnzzYTe/3339XwYIFtW/fPr388styd3dX69attWrVKpUsWVKvvPKKduzYoXXr1lmvad68ubJmzarVq1dbXT49PDzi9DwQN1xcXBQQEKBXX31VtWrVUv369VWhQgUdOnRIoaGhyp49uxYvXqzOnTurRIkSSpIkiXbt2qX//e9/CggIUKpUqTR8+HB99tln+u233yTdmwhr3LhxWr58uWrUqGENb7CPh0LiYf6/S52bm5vu3r0rSWrfvr3ee+89azhBmTJltGvXLklS0qRJFR4erjRp0sjPz89aTq5q1aqaNWuWVq1apXPnzql169bxcDaICw9bYicgIEDnz5/Xa6+9plmzZkmS0qZNq/DwcKte2cf8r169Whs3blSTJk00ffp0h5nKw8PD1alTJ+XKlSsOzgRx7WF1p3Pnzqpbt65VbySpdOnS8vDw0JEjRyRJKVOmlCSFhITo1KlT6t+/v4wxKlWqlHXP1KdPH7300ktxcBaIS+aBbt/R0dEKDw9XZGSk9dk/eN9crFgx1atXTwsWLJB0b+6RPn366MCBA5o2bZpVzn5fzERqCRdJN56aPbGR7q1/K0krV65UtWrVtGnTJvXu3Vvt2rVTeHi4NZGRv7+/kiRJopUrV+ry5cvWsYYOHap58+ZZM8QicRoxYoR8fX2VJEkSLV++XEOGDFHy5Mn11ltvyd3dXe3atVOJEiU0bdo0ZcuWzZqRfO7cuVYiNWjQIN26dUvTpk1zGPMt3btJun+pKCQu9s912bJlSpkypVauXKlhw4bp1KlT2rx5sySpdu3acnNzs8b6u7m5KSIiQoGBgdb1xX4znSVLlrg/CcQJ+8M3+xwR+/bts9a6LViwoDZs2KCGDRtq3Lhxev/99yVJ9evX18qVKyX9s9by9u3b1aNHDyVLlkyHDh3SG2+8Yb1H0qRJqUOJUHR0tKKjo626c+fOHWvf999/r9dee03vv/++Jk6cKGOMMmTIoICAAGtZMOneUoS9e/dWpUqVdPnyZS1btkw5cuSw7pny5MmjEiVKxOl5wTl27NihSZMmKSIiQjabzSHxdnFxUVRUlNKkSaOzZ8/q4sWLcnFxcSiTLFkyeXl5yWaz6datW5Kk4sWL66uvvlL9+vVjvJ/92oQEKJ5a2JGA3d9lNygoyDRs2NDUqVPHGGNM+fLlzZw5c8zdu3dN/fr1TZo0aczQoUMdusV89tlnpmjRoubTTz+N89gRf06cOGHSp09vmjdv7rB95cqVxmazmW3bthljjNmyZYspVqyYw3h+Dw8P0759e3Pw4EFjjDG7d++OMTYKid/Ro0fNtGnTTJcuXczs2bOtcdstW7Y0lSpVMgcPHjS3b982Q4cONSlSpDDz5883Bw4cMD/88IPx9fU1P/74YzyfAZxl586dJigoyBjj+B21adMmU7hwYVOwYEGTPn16M3LkSGtM9u3bt83ixYtN1qxZTdeuXc1HH31kmjRpYo4fP269PigoyKE78aPG9SLhun/+h/vrzs6dO03Tpk3Na6+9ZiZPnmwNI7h9+7aZN2+eyZYtm+nevbu5fPmyyZEjh1mwYIH12kuXLpnp06c7DH1hLG7i1L17d1OsWDHz008/GWMc65D9Mx89erRJmjSp+eqrrxxeax+CULFiRdOkSZOHHp9hcokHSTeemP3iYLdhwwYzdOhQ065dO3P8+HETGhpqOnfubIoWLWpSpUplWrRo4XDzYh9vGxERYV555RXzww8/xGn8iF+RkZFm6tSpJkWKFA4J85o1a4yXl5f5+eefjTHGDBw40Lz00kvWpFbLly83vr6+Jnny5GbRokXGmH++hLiJSbweTG7u3r1rWrVqZTJmzGhq1aplwsLCzN27d40xxpw6dcr4+vqa4cOHm7t375q7d++at99+22TPnt3kypXLpE+f3prECIlPUFCQyZYtm3XTap/P4bPPPjM5c+Y0w4cPNxcvXjQLFy40L730kjXpld2PP/5oWrZsaWw2m/H19bXmmbj/Zjc6OprrTSI0fvx4U7FiRXPx4kVr2927d03//v2Np6en8ff3N2+//bapVauWadiwocNrf/zxR1O9enVTqFAhU7ZsWTNu3LiHvkd0dDQPaxIh+2d67tw5U6VKFdO1a1dz5coVY8w/1477ryE5c+Y0JUuWNOvXr3c4zsaNG03JkiXN1q1b4yhyxBeSbvyrB2809u/fb6ZOnWqyZMlicubM6TB51ZgxY4yvr6/58MMPHV7z119/mRo1apg//vjDGPPPBEd4sVy+fNmUKVPGau0ODg42tWvXNiVKlLCS7A8//NBkz57dfP/992bfvn3mlVdeMcuWLTNr166Nz9ARRx68OY2MjLRuXNatW2eKFCliKleubO23Pwx8//33TaFChayHN8bcm5xv69attBQkcpGRkWbhwoUmadKkZvfu3db2b7/91kydOtX6fdSoUcbV1dUUKVLEeoBnd+fOHdOiRQuTNGlSa/JPJF72a8Kff/5pbDabmT17tnXt2bp1q/Hz8zO///67Vb5jx47GZrPFeHh37do106BBA2Oz2Uy/fv2MMY7XMK49idODE7WOHz/eVKlSxcyePTtGWft31Lp160zhwoWNl5eXGTRokJk3b57p27evSZkypenbty+rrrwASLrxxG7cuGGKFy9uypUrZ06cOGFee+014+npafbs2WOVOX78uKlZs6Z56aWXzIYNG8zRo0fN999/bwoUKGBatmzJkjwvgH97or9s2TJjs9lMx44dTYYMGUz9+vXN9evXHcrUrFnT5M+f36RMmdK0atXK4SENNzGJw799jnPnzjUVKlQwzZo1M926dbPq1fvvv28KFixoLT1ov1EJCwszxYoVM61btzZ///23c4PHcyEqKsqqF3fu3DFNmzY15cqVs/YHBQWZiIgIs3XrVpM/f35Tvnx5M3fuXFOpUiXTuHFjExwcbIz55yHwiRMnTOXKlc38+fPj/mQQp6KioqzPvUuXLiZPnjzmyJEj1v6VK1caY+59X+XLl88UKlTINGrUyHh7e1uzk9tff+nSJdOnTx9TsmTJOD4LxLeVK1eaV1991XTv3t14e3ubRo0aWcNRHtYz5sCBA6ZHjx6mVKlSpmrVqqZOnToOQxCQuJF046Huv1jcvXvXdO/e3Xz99demb9++VuK8du1aU6BAATNs2DCH1/7666+mefPmJl26dKZYsWImU6ZMjN9+Ad24ceOh22/evGm6du1qbDabWbVqlbU9MjLSeiIcHBxsDhw44LAUD8l24nH/gxl7F3Fj7n3Gd+7cMV27djU+Pj5mwoQJZtasWdYNb0BAgDl69KipV6+ead26tXXTa0+8P//8c1OvXj3WTX7B7N6927z77rtm1KhRxmazmaVLl1r7wsLCTMOGDU3//v2th3utW7c2GTJkMBMnTjTGOHYdL1iwoMPylUh87v8uCQwMNMePHzdJkyY1H330kcP8M7/++qspUqSI1XNv7dq1xs3NzQwZMsQY43gdmzp1qqlWrZo1NAGJ3/z5802aNGnMRx99ZObPn2/atGljkiVLZi39db+H3b8EBgZa/46KimL4yguA2cvxUPYZNqV7y15s3rxZ7dq1k6+vrzJlyiRJqlu3ripVqqRt27bp119/tcpXrlxZCxcu1P79+zV9+nT9/fff6t27d5yfA+KHMUYdOnTQjBkzJMVcHiNVqlTq0qWLUqRIocDAQEn3ZpN2dXW1ZuX09PRU4cKFlStXLmsmWWYkT/jsdcHV1VVBQUHq2bOnOnTooL59++rEiROy2Wy6fPmy9u/fr8WLF6tfv37q1KmTsmTJot27d+v06dPKly+f6tevrxMnTuiHH36Q9M/1qlu3blqzZo0yZMgQb+eIuPXRRx+pcuXKCg8PV0BAgFKmTKm+fftay+ocP35c+/fvV+HChZUmTRqFhITo5s2bypw5s7Zu3aobN27IZrPJxcVFBw8elM1mYwmwRM5msykiIkJdu3ZV7ty5NWXKFEVFRWnmzJk6dOiQVW7u3LnKlCmThgwZIkm6deuWkidPrtGjR+vQoUPW7ObSvWtaSEiI0qVLF+fnA+d6cOk4+/fYtm3bVL58eb333ntq27atvv76a7388stavXq1du7cKemfJcTuv3+xvz5jxozW8V1cXBzuu5E48QnjoXbu3KmWLVsqKChIGTJk0CeffCI3Nzd5e3tLurc+qST16tVLISEhWrZsmW7fvi3pnwuKj4+PKleuzPIGiUx0dLRu3LghSYqIiIixz2azKSgoyFpH+2FfJCVKlJC/v7/ee+893blzx+Hm5UF8GSUe9s9x1KhRypQpk06cOCFPT09NmTJFnTp10rFjx6w12ytWrKgPP/xQqVOnlqurq9asWaMKFSpIklq0aKGcOXPq888/18WLF636Yx5YIxWJ27Vr17RixQqNGzdOn376qaZPn66lS5fq5s2bGj58uKR7yVCOHDm0cuVKbdq0SQMHDpSHh4emT5+uhQsXKnXq1NbxevfurZIlSzqsxY3EadGiRVqzZo22bNmi4cOHa+3atYqKitLUqVMVFBQkSUqTJo2uXLmiW7du6caNG/r555/13nvvadasWcqZM6d1vVmxYoUGDx6shg0bWkvVIeEzDyw7eP36dUn3vsfCwsJ05MgRFS9eXNI/a2cPGDBAhw4d0pIlSxQWFhZjCTH76+/3uPsfJDLx1saO58bDxuD+9ddfxmazmTlz5hhjjAkNDTVNmzY1xYsXt8rYu8uMHDnSlChRwnzzzTdxES7i0dmzZ02RIkXMtGnTHLpLXbp0yRjzz4QhY8eONQ0aNDDXrl175LHOnDljUqVKZd566y3nBo3nRkREhKldu7ZxcXFxmMhq+/btxt3d3UyYMMFs3rzZ5MiRw2TMmNEULVrU/O9//7PKHT161Bw6dMgYY8ycOXNM3759H1vHkPDEZpbwoKAg4+7u7jDJ4t27d82YMWOMu7u7OXfunDHGmC+//NLUrFnTZM6c2VSqVMmcOHHCKh8ZGWl9B4aGhj7DM8HzrEWLFqZRo0YO25YtW2ZcXFzMsmXLjDH3Jr7KmzevyZcvn0mdOrWpUaOGOX/+vFXe/h148OBBazlLJD5btmwxNWrUMNWqVTOtW7c2W7ZsMcYY8/bbb5sCBQoYYxyHZFasWNEUKVKECRkRA81HL6g7d+5o9+7dOnnypFxdXXXr1i0FBARIuvd0r0iRInr77bc1atQonT9/XilSpNDgwYN19OhRzZo1S9I/T/a6d+8ub29vq9s5Ep/z589LkrJly6YsWbJoxYoV2r9/v4wxeuutt1SvXj2dO3fO6tWQLFkynTlzRh4eHo986p8tWzZ999136tq1a5ydB+KPMUZJkiRR7dq1lT9/fqVNm1bSvd4SFStWVPr06XXhwgVlzZpV+fLlk6+vr/bt26emTZtKutcFb/r06VaX8vbt22vixInWcZDw2XvKuLi46Nq1a7p165a172HXkaCgIJUuXVrbtm2ztrm7u6tGjRqKjo7WBx98IEnq2LGjfvrpJ23evFlbt25V7ty5reO5urpaLU0pUqRw5unBiR7sAmz3YL2x98Tz8vLS1atXrTJRUVFq1KiRypYtq6lTpyogIEB16tTR6tWr1atXL82bN08bN25U1qxZrdfYuwwXKlRIhQoVctapIR599tlnaty4sSpWrKiuXbsqXbp0atiwof744w91795dFy5c0NSpU63W60uXLik6OlrXrl3T1q1bdefOnXg+AzxX4i3dR7wZO3asKVCggPHz8zO5cuUyZ8+eNdWqVTM1atRwaPW+deuWSZMmjXnvvfes1of+/fsbHx8fa/Ije8sma1AmXrNmzTJlypSxJj3bv3+/yZIlixk9erQxxpjffvvN1K1b15QuXdp89913xhhjTp8+bTw9Pc2ff/5pjPn3SdCYJC3xu/8zLlOmjGnVqpXVanT48GHj4+NjLcczYcIEkzt3bvPOO++Ybdu2md9//900bNjQ5MuXz2HyPSQ+t2/fNu3atXNY+9g+4dDDWsA7d+5sXnnlFbNr1y5r26ZNm0zatGmNzWazWqXux/dV4hQVFWVGjBhhBg4caJYsWWJu3rz5yLILFiww2bJls9ZMtk/k+fLLLxubzWY++eQTc+fOnRivo+4kTg+7B7lz545p2LCh+fzzz61t48aNMzabzerZ+fHHH5vkyZObnj17mjVr1phOnTqZPn36mJ9//pmJ0RADSfcL5OTJk6Z69eomd+7cZsmSJebMmTPWEhnr1q0z7u7u1jI89gvQ1KlTTapUqczevXuNMf8kU926dYufk0Cc279/vylXrpzp06ePCQoKMsYY06dPH1O0aFHrhjYsLMx069bN5MmTx0ydOtXs27fPVK9e3SxcuDAeI8fzxn7D+uOPP5ps2bKZ77//3kycONF4eHiYpk2bWkvxBAUFmW+++cb4+PiYIkWKGF9fX/Paa6/FWFoOicvdu3eNn5+fteRkjx49TMmSJU3t2rVjlLXf0G7dutVUr17d1KhRw+zZs8ecPXvW9OjRw4wdO9Z88803Vp1C4rZjxw7j6+trKlasaOrWrWt8fHxM/fr1H/n5Hz9+3LRo0cKUKFHCWv3gypUrxt/f39SvX9/07dvXIWniwXDi9OCs4b/++qv1sOXWrVvG19fXHD9+3KxcudJkyZLFlCxZMsaD37Fjx5qaNWua7Nmzm0qVKpmzZ886HB+wI+l+gXz44YemcuXK5vTp0w7b7ReF5s2bm1KlSjksmRESEmIyZMhg/P39rafGX3/9tTXmCYlXVFSUdaPx0UcfmXLlyplvv/3WGHNvSa98+fKZXr16mYsXLxpj7i0FNmfOHOPr62veffdd4+HhYSXdfPG8OJ705rRp06bGzc3N5MyZ0yxZssTafn9duX79ujl16pTD0nFIvPbt22eyZ8/u0Dq9ZcsWkzJlSjNu3LhHvm7z5s2mUqVKxtfX13h4eJjy5cuz3OALZvDgwaZ+/frGmHstlH/99ZdJmTKl6dWrl7WE4IP1YPfu3aZAgQImR44cplmzZiZz5symQ4cOVhKOxO3+75qzZ8+aP//809hsNvP1118bY4w5deqUqVWrlsmSJYvx9vY2U6ZMsXp5Xr582VrLPSoqykRGRjrcW3PNwcOQdL8gAgMDjaenpxk/fvwjy5w8edKkSJHCTJ061dq2a9cukyVLFmOz2cxPP/0UF6HiOXTt2jVTs2ZN07p1a+tmdurUqSZXrlwOE10Zc++hzBtvvGFsNpvp3LlzfISLeGIfbvI49tbuI0eOGG9vbzNy5EhrG103E7/Hfcb79+83NpvNmpjRbvz48cbLy8uaGM3u/hvb0NBQs3fvXrN58+ZHlkHC9qi6c/v2bVOqVCkzbNgwh3Jff/21yZQpk/nhhx8eecxLly6ZadOmmbZt21rDW+x4WJw43X9NuH37tmnTpo3x9vY23bp1MzabzZQuXdpcu3bNREVFmTfffNPkz5/fGoZgf+38+fNNgwYNzI0bN2Icn+8xPAoTqb0gLl++rKioKOXIkUNSzLWTo6OjlStXLg0cOFBDhw7V3LlzFRAQoCVLlmj8+PGaPn26XnrppXiIHPHlzp07+uijj/Tnn38qbdq0atu2rQ4dOqQVK1ZIknr27Clvb2/98MMPOnXqlPW6Nm3aaOrUqSpbtqxCQkJ09+7d+DoFxLEkSZLIGKOJEyfqm2++cZjgytw3cVV0dLTy58+vFi1a6Mcff9T27dutfUicHvz8p0yZosmTJ2vz5s3WZEPh4eEqWbKkvvnmG4fXdu/eXenTp9fkyZMl/TNp1v1r36ZIkUIlS5ZUtWrVHlkGCdP9dUeS1q5dq0OHDiksLEySlDx5cmXMmFEHDhyQ9M/9TZs2bVSwYEF98803unbt2kOP6+3tLX9/f82fP19dunSR9E/dYanKxMU8ZM3sSZMmaefOnVq/fr0GDx6s2bNn6/jx45o4caJcXFzUokULpU6dWh999JHWrl2rPXv2qGfPnho4cKDq1KkjDw+PGO/D9xgeKT4zfsSdI0eOGFdXVzNlypR/bY1q2bKlyZEjh0mTJo0pXLiwOXbsWBxFibjyYOvPw57ob9682RQtWtR06dLF2ta8eXPz8ssvW5MWrVmzxmTJksXMnDnTerpr75o3c+ZMkzNnTmedAp5DS5cuNRkyZDAlS5Y09erVM5kzZzbjx4+PMa7SXt+Cg4NNgQIFTOfOna0uoEjc5s+fb9KlS2cqV65sypUrZ7Jnz2569uxpjLk3nKl58+amadOm5tSpU8aYf+rK2LFjTZ48eczt27fjLXbEr3nz5hlfX19TqFAh4+3tbRo0aGBdN7788kuTKVMma0lBezfgLVu2GJvNZo4fP26MeXzPB1q2E6/7P9uFCxeaTp06GWOMKV26tOnVq5dD2QkTJpiUKVOaw4cPG2OM+eWXX0zNmjVNgQIFTL58+UylSpWsSWKB2OAx3gsif/78qlKlir799ltduHBBUsylNAYOHKjhw4fru+++0+rVq7V48WIdOHBAefPmjY+Q4QTGGGtZHkmaP3++Nm/ebD3RP3funFW2WrVqatGihfbs2aNVq1ZJutfiFBAQoBUrVigiIkL16tVTtWrVNGPGDO3Zs0eSrGXDPD09lT59emtZFiQe9np0v6CgIE2aNEkDBw7U3r17tWbNGjVs2FDjx4/X+vXrHcq6uLgoKipKnp6e6tSpk65du2bVGyReixcv1oQJEzR27Fj9+uuvWrVqld577z2tXLlShw4dkoeHhxo3bqzTp09bS8PZr01JkiRRypQpdfPmzfg8BcSDGzduqEePHho2bJhGjBih9evX68cff9SaNWus3jQlS5ZUoUKFrGXi3N3dZYxRpUqVlC1bNq1bt07S43s+0LKdeNmXIVyxYoVGjRolX19fSVLGjBkVFBQk6Z/vtfbt2ytDhgwaN26coqKiVLVqVa1atUo7d+7Ujz/+qK1bt6pYsWKKjo5+5JKowMNwhXmB9OvXT7///rtmz56tK1euyGazWTfOBw8e1OnTp1WmTBlJUoECBVSzZs34DBfPmPn/dUVdXFz0xx9/qGzZsurQoYNu3LghY4zGjh2rN954Q2fPnrVe06hRI2XNmlVffvmljDGqWbOmKlWqpF9//VU///yzJGno0KFKmzat0qdPb73u3LlzateunYoUKaI0adLE+bnCeSIjI616dPv2bWv73r17FRYWpv79++vvv/9Wq1at9MMPP+jtt99WnTp1YhzHfoP77rvvaunSpdSTRORhayZHR0crLCxMtWrVUosWLWSMUfr06ZUvXz4lSZLEGobSqlUrVahQQUuWLNGSJUus11++fFm+vr7KmDFjnJ0Hng9hYWHy8vLSggUL1KlTJ/n4+Kh8+fIqV66cUqZMKele0v3aa69p06ZNWrBggaR7CfbRo0cVFRVF48EL7ueff1aTJk30wQcfqGfPnhoxYoSioqJUvnx5HTt2THv27LG+15IkSaLUqVPru+++09atWyVJSZMmlZeXlwoWLCjp3jXOxcWF4SuIFZLuF8grr7yiPn36aPTo0XrzzTe1Zs0abdy4UcOGDVPt2rWVNm1a1ahRI77DhJPYbDbdvXtX7dq1U/ny5VWhQgWFhISocePGstlsypIli1xdXTVv3jzrNUWKFNHLL7+sM2fO6KuvvpIk9erVS3fv3tX8+fN19epVFSxYUBs2bFCuXLms9zHG6IMPPtDs2bMZ35TI2Fukhw8frho1alit2Ldu3dKZM2c0evRoFS5cWHfu3NHu3bs1bNgwpUyZUhcvXnQ4jv1mhZaCxMM8MPb2fi4uLqpTp44mTJigVKlSWZ+/r6+vbt++rQwZMlhl+/fvrwoVKui1115TixYt1LhxY82YMUNt2rRxeB+8GDJlyqQePXqoXLly1rbBgwfr7Nmz2rRpk3788Ue5uLiobdu2atmypTp16qQxY8Zo27ZtmjRpktKnT68CBQrE4xkgrkRFRT30+uDr66tUqVLp0KFDql69uqR716maNWvKy8tLY8eOtcpeu3ZNJUuWVPny5TVs2DBJMXtIcF+DpxIPXdoRzz7++GNTqFAhkz59elOsWDFTqlQps2bNmvgOC04WERFhevToYVxdXa11Ju8f5xQVFWV69eplKlWqZI3ZNube2qUlSpQwNWrUMIGBgcaYe8vPjR8/3kRGRlpj5Jix88Xw119/mfz585tSpUqZmTNnWteOK1eumCJFipi0adOa33//3SofHR1t5s2bZ7744gtr/VMkbgsWLDCpU6e2lgx88Npw/3Vn2rRppmzZssYYE2OpptmzZ5v33nvPdOzYkWXjEqno6OhYzTB/9epVU7t2bZMlSxbz3nvvmVatWhkfHx8zfPhwa76a/v37m5deesnkzp3bVKhQwRqbi8Tt/uvK/v37za5du8ytW7ccZhzPkiWL+eijjxxe9/XXX5ssWbKY4sWLm86dO5ssWbKYAQMGmFWrVplUqVKZEydOxOl5IPEi6X5BhYSEmKCgIHPw4MH4DgVxaNWqVaZGjRpm4MCB1rbly5ebGjVqmIMHD5rdu3ebmjVrmm7duln7w8PDTfny5Y2Hh4d5++23jTEsw/MiGzJkiGnatGmMpVJu3bplhg8fbtzd3c2xY8dMcHCwMcaYxYsXm+LFi5uPP/6Y9W8TudDQUDN69GiTN29ekzNnTpM7d+5HXivs25s0aWJdV/7N/Q/5kPDdnyRdu3bN3Lx586Hl7v/Mb926ZXbv3u0woV7v3r3NSy+95DAZY2RkpEOyxCRpL4YLFy6YBg0aGB8fH5MzZ05Tp04da1nToKAg0759e1OrVq0YEwQfOHDADBw40LzyyivW0nHTp083uXLlirFUIfC06F7+gkqVKpVSp06tQoUKxXcoiEMvv/yyypYtqx07dmjevHlq2LCh2rdvr7p16ypv3rwqW7asatWqpd9//91anuenn35SgQIFNHToUDVv3tzheA9OpoXEITIy8qHbb9++raVLlypHjhzy8vKyPv/IyEilTJlSPXv2VN26dVW9enXVr19fNWrUUPv27dWhQwe98847cnNzi8vTQBy7e/eurl+/rt69e2vRokW6efOmRo0aJenhy1RGRUXp8OHD1vwhhw4dUo0aNXT+/PkYx46OjparqytjKBMRFxcX3b17V507d9arr76qPXv2OHQNtl+H7EOWJCllypQqW7askidProiICEn3JsM6evSoNfTFXldy584t6Z/xt0jcfvvtN73yyitKly6dtmzZok2bNilXrlwaO3asjh8/rtSpU6tJkyYKCwvTrFmzrNdFR0ercOHCGjt2rJYvX64uXbro7Nmz+vHHH1W7dm1r0jXgv+Iq9ILixuXF1bJlS7m5ualTp05Kly6dDh06pEGDBlljlDp06KA6deqof//+KlOmjF5//XXVq1dPAwYMUJUqVST9U3+4kUlc7ImR/eZ1x44dOnfunMLDwyVJISEhSpYsmdKlS6fo6GiHmaUlKX369Fq0aJG++OILtWzZUi+//LICAwPVp0+fuD8ZxLm0adPqzTfflL+/v0qXLq2BAwdq7NixCggIkIuLi0NC5erqqiNHjihJkiQqUqSIevfurVKlSilFihTKlClTjHGZXGsSn+3bt6tIkSI6ceKEBg8erPz580v6Z8y+/boybtw4jRo1ylrP3c7NzU2XLl3Stm3b1K9fP2syxgfrCuNvE4fQ0FBJ9x6iPOyBf/r06fXSSy/p66+/Vu7cufXbb79p8eLFOnnypD766CNJ0quvvqoKFSpo+/bt1mSw988vsmLFCvXq1UtFihRRihQprNcBzwJrtAAvmBIlSuiVV17RtWvXVLduXXl7ezskUD4+Pvrggw9Us2ZNnTp1Sk2bNrUmOTL/PwM6Eid7HVixYoX69eunZMmS6ebNm3r11Vc1cuRIeXt7K0eOHFq+fLmaNWum/PnzW3Xi+++/V8GCBVWiRAk1bNgwns8E8cWeOEn3ZiKfN2+e+vbtq++//z7G9WPp0qU6ePCgihUrprx582r37t0qVqxYfISNeLBs2TJVqlTJmrwzJCTEoX7s2rVLr7/+umw2myZOnKjkyZNLkgIDA3Xw4EGdPHlSI0eOVLFixaxJ9pD4GGNUp04dlSxZUiNHjlSKFCkkSSdPnlSSJEmULVs22Ww25cyZUx988IGuX7+utm3b6vDhwxo1apQCAwM1Z84cbdiwQbVq1VKTJk20adMmbdu2TbVr17bqnM1mU7Zs2ZQ2bVqtWrVKVatWjc/TRmIUX/3aAcSfCxcumObNmxs/Pz9z6dIlY8zjx7wxSVri9eBnO2HCBJMvXz4zZswYExQUZBYvXmzKlCljevfubYwx5uzZsyZTpkzmzTffNLt27TLBwcFmw4YNplixYmbMmDHxcAZwtv/y//+SJUuMzWYz27dvt7bZrzXjx483+fLlM8uXL3fYx/jbxONRY/CvXbtmfH19za5du8z169dNs2bNTJ06dUzTpk3NvHnzjDH3JrgaPXq0CQkJcXjt4cOHTaVKlUzhwoXNnDlznH0KiAf2emO/FowYMcKkTZvW7Nmzx9y8edO88cYbJlWqVKZAgQKma9euJiwszHrt+PHjTe3ata0J9JYtW2aSJEli6tata5XZv39/HJ4NcI/NGNbeAF5E3333naZNm6bGjRtrwIABjyxnaN1OlO7v3RAeHq7Zs2erY8eO+uqrr+Tp6am2bdtKkvz9/TVr1izZbDZt2rRJFSpU0Ny5c/Xtt99q69atKlasmA4cOKABAwZo+PDh8XlKeMbu/38/MjJSwcHBSpcuXYxykZGRVlfgB926dUtt2rTRhQsX9Ntvv2nnzp26cOGCGjdurGvXrjmsux0VFUVX4ETi/uvLg4wxCgkJUY0aNdSyZUtt3bpVqVKlUv369fW///1PW7du1fTp0/XGG2888vj79u1TiRIlrN+pO4nDg/cb4eHhSpo0qSQpZ86catCggYoUKaIdO3bI399fhw8f1ptvvqnRo0erR48e8vDwsMZt//TTT5KkiRMnaunSpQoJCdG0adOsYXLS4+sp8MzFZ8YPIP7cvXvXdOvWzRQpUsT88ccf8R0OnOTfZnv+448/TOHChU3Dhg1NYGCgCQkJMZGRkWb16tUmT548pnr16mbRokWmatWqpnr16tbrbt68aTZu3Gi+/fZbc+3aNWefBuLR559/bry8vMyHH35o7t69+8hyY8aMMTNmzIix/ffffzcpUqQwFSpUMDabzQwcONCh9dy+1BMSn2+++cb07t07Ro+qwMBA06BBA1OzZk1Tp04daznK0NBQ07dvX5MpU6YnOj69sBKP+7+rlixZYpo1a2bat29vvvjiC2OMMStXrjQ2m80ULFjQbNq0ySo7atQokz17drN161ZjjDHvvfeeKVSokJkyZYoZN26cKVOmjFm8eHGMFTeAuMbjHeAF5e7urmbNmqlZs2bKmTNnfIcDJ4iOjrZaDa5evarw8HBrApqzZ8+qbdu2+uyzz9S4cWMtX75cGTNmlIeHh0JDQzVt2jQ1b95cq1ev1muvvSZvb2/98ssv+uGHHyTdWwGhRo0aatWqldKmTRtv5wjnuXPnjsaOHavPP/9cBQoU0MyZM3X06NEY5Xbs2KHcuXNrxowZ8vb2jnGMVatW6c6dO3J3d9f27ds1duxYh1bJR7WSI+G6cOGCWrVqpfbt22vZsmVatWqVJFkT6mXMmFEVK1bUpk2blCxZMqvHQ4oUKdSqVSulTJlSv/zyy7++D63biYfNZtPJkydVpUoV9ezZU+XKlVORIkWUKVMmSZKfn5+aNWumkydPWpPmSdL7778vNzc3zZo1S+Hh4Wrfvr2qVaumadOmac6cORo6dKiaNWvmsOIGEB9IuoEXWJ06dTRixAh5eXnFdyh4hsz/jxpycXHR33//rY4dO6pbt27q1auXNmzYIEnKnj27du3apS+//FK5cuWSzWZTVFSUJOnSpUtavXq1atWqpWTJkik4OFg2m03FihXTpEmT4u284DwPuxmNiIhQqlSp1K1bN/3666+KiIjQZ599pps3b1plwsLCtHnzZnXu3FkHDx5UkyZNHF4/ZcoUTZkyRbNmzdLmzZtVoUIFRUdHc/ObyFy/fl379++XdO/6c+TIEV29elVLly5V8eLFtXLlSh0+fFjSP0uB9e3bV8WKFdPhw4f1559/WscKDAzU3bt3lTVr1rg/EcSbmzdvqm/fvvL19dVvv/2mAQMG6J133tGrr75qfTd9/PHHioiIsK5HdqNHj9bKlSu1atUq5cuXTzNmzNCaNWt0+PBhvfrqq5Lu1Uu6kiNexW9DO4DnARMXJU5Dhw41yZMnN506dTI//fSTWbBggcP+lStXGg8PjxgToJ06dcpUr17d1KpVy2zevNm0atXKvPrqq+bYsWNxGT7iwIP/79u7+dpdunTJ6sK7YMEC4+bmZn7++WeHMvdPYvSgkydPOvxOd+DEaejQoaZz587W7zdu3DC//PKLMcaYtWvXmpIlS5qPPvrI2m8fUrBo0SJTtGhRU6NGDbN3715z4sQJ065dO9OqVSsTGhoatyeBePXtt98aLy8vs3XrVoeu5vZ/268d/v7+JkeOHNZEaXYlSpQw1atXN5cvX3bYzjUHzwsmUgOAROiTTz7RwoULNXToUDVq1Mhh3969e1WqVClJUsOGDRUREaGJEyeqcOHCku61eq5evVqjRo3S5cuXlTdvXs2bN0+ZM2eO8/NA3Jg5c6ZmzJghDw8P5cuXT126dFGFChWs/fYJhypUqKDUqVNr3rx5VrfPJ/G4ydaQ8LVt21YlS5ZUv379HjqpWdeuXXXy5EkNHz5cVatWdagPP//8s/r376/IyEhdvnxZRYoU0ddff01L9wumb9++Wrt2rQ4dOvTQ/fZ6FR0drbRp06p79+4aPny4tZTcqVOnFB4ergIFCsRl2MATo58FACQixhhdvHhRU6dOVd26dfXyyy9b+7Zt26YKFSqocePG+v777yVJw4cP18GDB7VmzRrdvXtX0r1u6X5+flq/fr02b96sdevWkXAnUseOHVPVqlU1ZswY9ejRQy1atNChQ4c0c+ZMqz5I/wxZmDlzptatW6e1a9daXcTtXT8f12WchDtxsn/2hQsXtq4p9yfc9q7kvXr10s2bN7V06VKFhoYqSZIkVn2pXbu2fv31Vy1btkzr1q3Tpk2blDVrVoYgvGDOnj2rZMmS6eLFizH2GWOseuXi4qIxY8bok08+0a5du6wyuXLlUoECBag3eG6RdANAImKz2XT8+HGdP39evXv3tpKdVatWqVGjRipYsKCKFSumJUuW6Pr16ypbtqwaNWqkxYsX67fffnM4lqenp7Jnzx4fpwEnuX/s7e3bt7Vx40blzZtXO3bsUNeuXeXv76/SpUvr2rVrcnd3t5JtV1dXRUVFqVixYmrXrp3Gjh2rU6dOacOGDfrwww91584dxku+gOyJUO7cueXl5aV9+/ZJ+uchTZIkSWSMUZEiRdSwYUPt3LlTa9askST98ccfVtLu6empPHnyqGTJkpLuJfPUpxdL7dq1dfDgQR07dszaZq9HNptN4eHhGjlypCTprbfeUqFChXTjxo0Yx6He4HlFzQSARObs2bNKmTKl9uzZY22rX7++fv/9d82ZM0eNGzdWQECA5s6dK0kaNWqUDh06pCVLlujOnTvxFDXiwqRJkzRlyhRJ92aKrl27toYOHeow6/iVK1dUtmxZnTx5UmFhYZLuJUH2mfA/++wznT59WrVr11adOnUUFhZmdfHEi8WeFGXLlk2XLl3SgQMHJMlhrWV7mZ49e8rDw0PffvutmjRporJly2r16tUPPS6zkr94mjZtKh8fH02fPt1q7b6/Hv3yyy9at26dTp48KeneQ5vGjRvHR6jAUyHpBoBEwn5zW65cOYWGhuq3336zkmibzWYtDdeqVSt5eHjohx9+0IULF5Q+fXp9++236t+/P8lTInfmzBkVKlRI0r36kidPHqteLF++XBkyZNDWrVu1atUqNWzYUG+++aake0mQi4uLLl++rPHjxyssLEylSpXSsWPHNHr06Hg7H8Qve1JUvnx5+fr66ttvv9WlS5ccythbHtOmTatkyZLpxx9/1NWrV7V37141bNgwzmPG88nb21vDhw/X//73P40YMUI3btzQnTt3dPPmTc2bN0+DBg1SgwYNrOuVfXw3U1MhoWAiNQBIYOyTWj1uX+PGjfXnn39q7ty5qlatmkOZu3fvqm3btsqXL58++uijuAgZ8cw+CdHYsWP1v//9L8ZQAkn68ccfdebMGXXs2FGStHHjRnXv3l0zZsxQs2bNFBUVpYULF6pPnz6aOXOmtRSPvSvw/a1SSDweNjHaw/bv379fZcuW1eTJk9WuXTulSJFCxhjZbDZduXLFmqjxyy+/pO7gkQYPHqzZs2crODhYRYsWlc1m06lTp/Txxx9b1yYgISLpBoAE4v6bX/vN7KOcOHFCJUqUULly5TR+/HiVKVNGISEhOnbsmIYNG6arV69q+vTpKlu2bFyFj+fAokWL9MUXX+iTTz5RiRIlHlqP7A9ujh07prp162rQoEHq3r27JCk0NFQpU6a0yv5bQoaE6/6He5GRkfr777+VLl06eXh4PLJsv379tH37dnXr1s1KkOx1bMGCBXrjjTes11B38DD2yUBXrlypyMhIubm5qWvXrtb+xz10Bp5n1FoAeM48+CzUPhur/QZ1woQJ6tixo2bMmPHQiWSio6OVJ08effnllwoICFD58uVVsGBBtWrVSjVq1FDq1Km1ceNGEu4XyJOMvbWz39CeOXNGnp6eDj0l7Am3fVZqkqbEy14PJk+erBw5cqh169YqXry41q1bZ81s/+C1avTo0SpfvrymT5+uxYsXS/qnjtkTbuoO/k2WLFnUrVs3+fv7Wwm3vd6QcCOhoqUbABKIS5cuaeTIkVq/fr2qV6+u+fPny9/fXwMGDJCPj4/VAnB/62VwcLB++OEH3bp1S3fu3FHjxo1VpEiReD4TxKf69evLZrNp9uzZMZaCCw4OVmRkpH744Qd9/PHHev311/XBBx8oadKkdAFOhB7s6WBfP9t+Lfnwww/19ddfa9iwYapZs6YmT56sVatW6a233lKPHj0cjmV/TVBQkL7++muNHz9eXbp0Uf/+/ZUqVaq4PjUkIv/WswtICFg4EwCeMzdu3NC4ceNUvXp11atXT7dv39YHH3ygc+fOyd3dXTt37lSGDBlUqVIlffHFF/rmm280YMAAqwXAfnNijJGXl5e6desWn6eDOPKkY2/Hjx+vsmXLatmyZdbYW0k6f/68vvrqK61evVqBgYFW0o3Eyz7eet26dWrdurWSJEmimzdvWuOxN23apEGDBql169YKCgrSgQMHFBgYqHTp0sU4lv36kyZNGvXq1Ut58uTR77//rl9++UV+fn5xfWpIREi4kRjQRwMAnjNnzpzRhg0b9O233yoyMlIpUqRQcHCwli5dqoiICGXIkEGS1LFjRxUpUkQ///yzNTGWvSu6xI3Ki+L+4QeRkZE6c+aMbt68GaOcfbbfYsWKyd/fX3PnztWCBQus/b6+vipXrpz69++vs2fPWgn3/XUKiUt4eLjmzJmjtm3b6uzZs+rZs6deeeUVnT9/XidOnND58+fVuHFjvffee8qWLZtSpkypHTt2OIzNfpC9A+XLL7+sYcOGkXADgEi6AeC5U6JECbVq1UrHjh3T/PnzJUlDhgxRiRIldO3aNV29elXSvaS6Q4cOun37thYuXChJVvdyvDie1dhb6V7X8wfH3jKGMvFKmjSpWrZsqezZs6tIkSLavXu3xo0bpxw5cihv3rxycXFRunTptH37di1ZskQLFixQ7ty5dfbsWS1atEjXrl2LccyHrdHNNQnAi45vUgB4Dr3++uvKli2blixZogsXLihLlix64403dPnyZa1YscIqV6VKFVWvXl0bNmyIMXEREo8HkxZ7Qmxvhf7www81Y8YMjRs3TosXL9brr7+u/v37a/bs2ZL+qRMuLi6Kjo5WsmTJNGLECLVr1059+vTRyJEjFRoaKldXV+u9kiRhBFpiY4yx6kxUVJQk6dChQwoODlZERIQ++eQTlS9fXhEREYqKilLXrl3l7u6ur7/+WnXq1LGOM2fOHP3000/WMR7FXu+4JgF40ZF0A8BzyMfHR40bN9a1a9c0d+5cSVL37t2VKVMmrVy5UseOHbPKtmnTRnnz5lXWrFnjKVo4m33s7bfffitJ1thbY0yMsbfJkiWL1djbmTNnymazafPmzdZ7IfGJioqSzWaTi4uLwsLCrPH/devW1Z49e9S8eXP17t1bkuTm5iZXV1c1atRIJUqUkJ+fn95//30tWrRIVapU0Zw5c/Tqq68qY8aM8XlKAJBgkHQDwHOqadOmKl68uNavX689e/bI3d1db775ps6fP68lS5ZY5QoUKKAFCxaoQoUK8RgtnImxt/iv7En2qFGjVLduXbVs2VJffPGFbDabcubMqdatW+vSpUuaNm2apHu9KfLkyaPly5erYsWK2rZtmyZOnKgiRYro+PHjaty4cTyeDQAkLCwZBgDPsU2bNmnkyJEqUqSIdTPcvHlznT9/XvPmzVP+/PnjOULElfPnz6tq1aq6evWqChYsqKlTp6p8+fKS7j14OXbsmKpWraohQ4ZYXYHPnj2r3bt3q2bNmg9t9bazL8nD0jyJ1969e9WpUycZY/TWW2/p4MGDWrlypQYPHqyuXbvq2rVrGjNmjL7//nudPn1aSZMmlSTduXNHyZMnV2RkpEJDQ+Xl5SXp32fLBwD8g5ZuAHiO1ahRQ1WrVtWff/5ptW6PHDlSn3/+OQl3IsbYW8TGg+0nD/t948aNql+/vv788091795dnTp10t27dzV06FAFBQUpXbp0atmypTw9PfXaa6/pxx9/VOnSpfXVV19Za3B7eXkpOjpa0dHRJNwAEAu0dAPAc+7QoUPq1KmTChcurJkzZ3Kzm8jd34IYFhYmd3d3SfcSpzNnzmjYsGE6cOCA/vjjD+s1J06cUNu2bRUaGqpXX31VxYoV05QpU3Tu3DlNnjyZrsAvgLCwMG3fvl01atRw2G7vvfDXX38pZcqU8vHx0TvvvKOvv/5aTZo00aZNm1SrVi3Nnj1bERER2rx5swYPHqyQkBC9/vrr+vDDD+PpjAAg8SDpBoAEYNu2bSpXrpzc3NziOxTEkVGjRmnDhg3y8fFR9erV1a1bN0nSmjVr1KFDBw0dOlQ9e/ZUZGSkkiRJoitXrmjo0KE6fvy47ty5oxIlSmjy5MlWN2EkXpGRkerVq5e2bdumBQsWqECBAlbr9IMGDBigbdu2adKkSSpXrpzeffddffrpp9q7d6+KFi0qSbp8+bK8vLwcHvjQCwIAnh5JNwAAzxHG3uJprFq1ShMmTFCZMmU0fvz4h5Y5f/68ypcvr0mTJlmT7LVr107ffPONihQpov379zuUj4qKkouLCwk3APxHjOkGACCOMPYWzuLn56eyZctq27Zt2rBhg6R/1nG3S5s2rdzc3KyhCWvXrtXt27e1fv16jRkzJsYxXV1dSbgB4Bkg6QYAII7YbDaFhYVp06ZN1u929i689erVU9euXXX37l317NlT1atXV7169ZQ8eXL1799fkqwx2xcvXtSAAQPUoEEDvf3223JxcbG6FN//b7wYWrZsKQ8PD82bN0+RkZFycXFxSLzd3Nw0aNAgffLJJypUqJAaNWqkChUqqFatWiwZBwBORPdyAADiCGNv4WwTJ07UwoUL1aNHD7Vr186hTly7dk3GGF26dEl79uyRn5+fMmTIEM8RA0DixyNwAADiSJIkSeTn56d06dJp9uzZkvTQhPv8+fP65ptv1KtXL5UrV06SFBgYqKioKLVu3doqlzFjRrm7uysqKoqEG5KkFi1aKFu2bFq4cKECAwNls9kUHBysCRMmqHTp0lqwYIGKFi2qDh06KEOGDFbdAQA4D0k3AABxiLG3cCYfHx81btxYQUFBmjNnjjZv3qzKlStr1KhR+r/27j2myvqB4/jnCGGngCBCqQacqAMD5VJBK206AiewCC8FGcscWzrRpghpDmg6hbmZaenUJBW7TGgOwrFpKiY2cCQFdOGEgCgsqVSglTXE4PeH48wTKogd+ZXv11/wPN/bczx/+OF7eV5//XUtWLDAWravr4/vDgDcAiwvBwDgFqutrdUbb7yhMWPGaMeOHXJ0dLRZZn7x4kVt375dr732mvz9/dXc3KycnBxlZGSM8Mjxb9Dd3a1FixZp586d6unpUWpqqjZt2mS9f60tDQAA+yB0AwAwAth7C3s6fPiwDhw4oNTUVPn4+EiS9Z3uAIBbi9ANAMAIOHPmjBYvXqw//vhD27dv19ixY/Xrr7/q/fff18aNGwcsBeadyRguvjsAMLJYWwQAwAhg7y1uhf53tfPdAYCRw0w3AAAjhL23AAD897GxBwCAETJ69GglJibKzc3tqntvCdwAAPz7MdMNAMD/CfbeAgDw38NMNwAA/wf6994CAID/Fma6AQAAAACwEzaLAQAAAABgJ4RuAAAAAADshNANAAAAAICdELoBAAAAALATQjcAAAAAAHZC6AYAAAAAwE4I3QAA3AKnTp2SwWBQbW3tkOvk5+fLzc3tlo/DZDJpw4YN/2i/AADcrgjdAAAMUVtbm1JSUvTAAw/IyclJvr6+WrRokc6fPz9oXW9vb7W3t2v8+PFD7i8pKUknTpy4mSH/Z82ZM0fTpk274XorVqxQWFjYPz4eAACuhdANAMAQnDx5UuHh4WpsbNTu3bvV1NSkrVu3qqysTE899ZQ6OjquWffixYtycHCQl5eXHB0dh9yn0WjUmDFj/onhAwCAEULoBgBgCBYsWCAnJycdOHBAkydPlo+Pj2JjY3Xo0CH9+OOPyszMtJY1mUxatWqVZs+eLVdXV82dO/eqy7r37t0rs9msO++8U5GRkdq1a5cMBoO6urokDVxe3j9L++GHH8pkMumee+7Riy++qN9++81aZv/+/Xr66afl5uYmDw8PPfvss2pubr6pZ29tbVVCQoKcnZ3l6uqqxMRE/fzzz9b7zc3NSkhI0NixY+Xs7KyIiAgdOnTIpg2TyaTc3FylpKTIxcVFPj4+2rZt23X73bNnj4KDg2U0GuXh4aHo6GhduHBBK1as0K5du1RSUiKDwSCDwaAjR45IkpYtWyZ/f3/ddddd8vPzU3Z2tnp6eqyf58qVK1VXV2etl5+ff9V/m66uLpt2Ozs7lZycLE9PTxmNRpnNZu3cufOmPlcAwO2B0A0AwCA6Ojr02WefKTU1VUaj0eael5eXkpOTVVhYqL6+Puv1t956S6GhoaqpqVF2dvaANltaWvT8889r2rRpqqur07x582yC+7U0Nzfr008/VWlpqUpLS1VeXq41a9ZY71+4cEFLlixRdXW1ysrKNGrUKE2fPl29vb3Devbe3l4lJCSoo6ND5eXlOnjwoE6ePKmkpCRrmd9//11xcXEqKytTTU2NYmJiFB8fr9bWVpu21q1bp/DwcNXU1Cg1NVXz589XQ0PDVfttb2/XrFmzlJKSIovFoiNHjmjGjBnq6+tTRkaGEhMTFRMTo/b2drW3t2vChAmSJBcXF+Xn56u+vl7vvPOO8vLytH79ekmXl+unp6dr3Lhx1npXPsf1ZGdnq76+Xvv27ZPFYtGWLVt03333DecjBQDcZoa+xg0AgNtUY2Oj+vr6FBgYeNX7gYGB6uzs1NmzZ63LwZ955hmlp6dby5w6dcqmznvvvaeAgACtXbtWkhQQEKDvvvtOOTk51x1Lb2+v8vPz5eLiIkl6+eWXVVZWZq03c+ZMm/I7duyQp6en6uvrb2g/eb+ysjJ9++23amlpkbe3tyTpgw8+0Lhx43T8+HFFREQoNDRUoaGh1jqrVq1ScXGx9u7dq4ULF1qvx8XFKTU1VdLlGen169fr888/V0BAwIB+29vbdenSJc2YMUO+vr6SpODgYOt9o9Go7u5ueXl52dTLysqy/mwymZSRkaGCggItXbpURqNRzs7OcnR0HFBvMK2trXr00UcVHh5ubRsAgKFgphsAgCG6ciZ7MP3h7FoaGhoUERFhc+2JJ54YtF2TyWQN3JJ0//3365dffrH+3tjYqFmzZsnPz0+urq7WcPj3Weehslgs8vb2tgZuSQoKCpKbm5ssFoukyzPdGRkZCgwMlJubm5ydnWWxWAb0GRISYv3ZYDDIy8vLZuxXCg0NVVRUlIKDg/XCCy8oLy9PnZ2dg463sLBQEydOlJeXl5ydnZWVlTXsZ7/S/PnzVVBQoLCwMC1dulSVlZU33SYA4PZA6AYAYBCPPPKIDAaDNWT+ncVikbu7uzw9Pa3X7r77bruM5Y477rD53WAw2Cwdj4+PV0dHh/Ly8lRVVaWqqipJlw9zs5eMjAwVFxcrNzdXX3zxhWpraxUcHDygz8HGfiUHBwcdPHhQ+/btU1BQkDZu3KiAgAC1tLRccxzHjh1TcnKy4uLiVFpaqpqaGmVmZg767KNGXf7v0JV/VOnfB94vNjZWp0+fVlpams6cOaOoqChlZGRct10AACRCNwAAg/Lw8NCUKVO0efNm/fnnnzb3fvrpJ3388cdKSkqSwWAYcpsBAQGqrq62uXb8+PGbGuf58+fV0NCgrKwsRUVFWZe934zAwEC1tbWpra3Neq2+vl5dXV0KCgqSJFVUVGjOnDmaPn26goOD5eXlNWA5/XAYDAZNnDhRK1euVE1NjZycnFRcXCxJcnJy0l9//WVTvrKyUr6+vsrMzFR4eLjMZrNOnz5tU+Zq9fr/WNLe3m69drX3mHt6euqVV17RRx99pA0bNgx6EBwAABKhGwCAIdm0aZO6u7s1depUHT16VG1tbdq/f7+mTJmiBx98cNC92H83b948/fDDD1q2bJlOnDihTz75RPn5+ZJ0Q+H9Su7u7vLw8NC2bdvU1NSkw4cPa8mSJcNqq190dLSCg4OVnJysr7/+Wl9++aVmz56tyZMnW5fQm81mFRUVqba2VnV1dXrppZeGfXBbv6qqKuXm5qq6ulqtra0qKirS2bNnrfvqTSaTvvnmGzU0NOjcuXPq6emR2WxWa2urCgoK1NzcrHfffdca0vuZTCa1tLSotrZW586dU3d3t4xGo5588kmtWbNGFotF5eXlNnvDJenNN99USUmJmpqa9P3336u0tPSae/wBALgSoRsAgCEwm82qrq6Wn5+fEhMT9fDDD2vu3LmKjIzUsWPHdO+9995Qew899JD27NmjoqIihYSEaMuWLdbTy0ePHj2sMY4aNUoFBQX66quvNH78eKWlpVkPahsug8GgkpISubu7a9KkSYqOjpafn58KCwutZd5++225u7trwoQJio+P19SpU/XYY4/dVL+urq46evSo4uLi5O/vr6ysLK1bt06xsbGSpFdffVUBAQEKDw+Xp6enKioq9NxzzyktLU0LFy5UWFiYKisrB5wcP3PmTMXExCgyMlKenp7avXu3pMsHzl26dEmPP/64Fi9erNWrV9vUc3Jy0vLlyxUSEqJJkybJwcFBBQUFN/WMAIDbg6HvRk6FAQAAdpOTk6OtW7faLOUGAAD/brwyDACAEbJ582ZFRETIw8NDFRUVWrt2rc0rtgAAwL8foRsAgBHS2Nio1atXq6OjQz4+PkpPT9fy5ctHelgAAOAfxPJyAAAAAADshIPUAAAAAACwE0I3AAAAAAB2QugGAAAAAMBOCN0AAAAAANgJoRsAAAAAADshdAMAAAAAYCeEbgAAAAAA7ITQDQAAAACAnRC6AQAAAACwk/8BRCDbjPcTUxUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Inspect the original target before consolidation.\n",
        "original_status = (\n",
        "    data_raw[\"loan_status\"]\n",
        "    .value_counts(dropna=False)\n",
        "    .rename_axis(\"loan_status\")\n",
        "    .to_frame(\"count\")\n",
        ")\n",
        "original_status[\"percentage\"] = original_status[\"count\"] / len(data_raw) * 100\n",
        "\n",
        "display(original_status)\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(original_status.index.astype(str), original_status[\"count\"])\n",
        "plt.ylabel(\"Number of loans\")\n",
        "plt.xlabel(\"Original loan status\")\n",
        "plt.title(\"Original loan-status distribution\")\n",
        "plt.xticks(rotation=30, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "393fd7aa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "393fd7aa",
        "outputId": "0c2bd7dc-4d73-496f-b19a-010179378830"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Primary non-current outcome cohort\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    count  percentage  imbalance_ratio_vs_smallest\n",
              "risk_class                                                        \n",
              "Repaid                447     71.5200                       6.1233\n",
              "Early Delinquency     105     16.8000                       1.4384\n",
              "Severe Delinquency     73     11.6800                       1.0000"
            ],
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              "      <td>71.5200</td>\n",
              "      <td>6.1233</td>\n",
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              "      <th>Early Delinquency</th>\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Full operational snapshot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               count  percentage  imbalance_ratio_vs_smallest\n",
              "risk_class                                                   \n",
              "Performing      9822     98.2200                     134.5479\n",
              "Early Warning    105      1.0500                       1.4384\n",
              "High Risk         73      0.7300                       1.0000"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "snapshot_distribution",
              "summary": "{\n  \"name\": \"snapshot_distribution\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"risk_class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Performing\",\n          \"Early Warning\",\n          \"High Risk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5619,\n        \"min\": 73,\n        \"max\": 9822,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9822,\n          105,\n          73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56.193729484109994,\n        \"min\": 0.73,\n        \"max\": 98.22,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          98.22,\n          1.05,\n          0.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"imbalance_ratio_vs_smallest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76.9777116220685,\n        \"min\": 1.0,\n        \"max\": 134.54794520547946,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          134.54794520547946,\n          1.4383561643835616,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
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      ],
      "source": [
        "# Primary target: exclude Current loans because their eventual outcome is unresolved.\n",
        "PRIMARY_TARGET_MAP = {\n",
        "    \"Fully Paid\": \"Repaid\",\n",
        "    \"In Grace Period\": \"Early Delinquency\",\n",
        "    \"Late (16-30 days)\": \"Early Delinquency\",\n",
        "    \"Late (31-120 days)\": \"Severe Delinquency\",\n",
        "    \"Charged Off\": \"Severe Delinquency\",\n",
        "}\n",
        "PRIMARY_CLASS_ORDER = [\"Repaid\", \"Early Delinquency\", \"Severe Delinquency\"]\n",
        "PRIMARY_FOCUS_CLASS = \"Severe Delinquency\"\n",
        "\n",
        "# Sensitivity target: retain the entire operational snapshot.\n",
        "SNAPSHOT_TARGET_MAP = {\n",
        "    \"Current\": \"Performing\",\n",
        "    \"Fully Paid\": \"Performing\",\n",
        "    \"In Grace Period\": \"Early Warning\",\n",
        "    \"Late (16-30 days)\": \"Early Warning\",\n",
        "    \"Late (31-120 days)\": \"High Risk\",\n",
        "    \"Charged Off\": \"High Risk\",\n",
        "}\n",
        "SNAPSHOT_CLASS_ORDER = [\"Performing\", \"Early Warning\", \"High Risk\"]\n",
        "SNAPSHOT_FOCUS_CLASS = \"High Risk\"\n",
        "\n",
        "data = data_raw.copy()\n",
        "data[\"primary_risk_class\"] = data[\"loan_status\"].map(PRIMARY_TARGET_MAP)\n",
        "data[\"snapshot_risk_class\"] = data[\"loan_status\"].map(SNAPSHOT_TARGET_MAP)\n",
        "\n",
        "primary_cohort = data.loc[data[\"loan_status\"].ne(\"Current\")].copy()\n",
        "\n",
        "primary_distribution = (\n",
        "    primary_cohort[\"primary_risk_class\"]\n",
        "    .value_counts()\n",
        "    .reindex(PRIMARY_CLASS_ORDER)\n",
        "    .rename_axis(\"risk_class\")\n",
        "    .to_frame(\"count\")\n",
        ")\n",
        "primary_distribution[\"percentage\"] = (\n",
        "    primary_distribution[\"count\"] / len(primary_cohort) * 100\n",
        ")\n",
        "primary_distribution[\"imbalance_ratio_vs_smallest\"] = (\n",
        "    primary_distribution[\"count\"] / primary_distribution[\"count\"].min()\n",
        ")\n",
        "\n",
        "snapshot_distribution = (\n",
        "    data[\"snapshot_risk_class\"]\n",
        "    .value_counts()\n",
        "    .reindex(SNAPSHOT_CLASS_ORDER)\n",
        "    .rename_axis(\"risk_class\")\n",
        "    .to_frame(\"count\")\n",
        ")\n",
        "snapshot_distribution[\"percentage\"] = snapshot_distribution[\"count\"] / len(data) * 100\n",
        "snapshot_distribution[\"imbalance_ratio_vs_smallest\"] = (\n",
        "    snapshot_distribution[\"count\"] / snapshot_distribution[\"count\"].min()\n",
        ")\n",
        "\n",
        "print(\"Primary non-current outcome cohort\")\n",
        "display(primary_distribution)\n",
        "\n",
        "print(\"Full operational snapshot\")\n",
        "display(snapshot_distribution)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "e48ca017",
      "metadata": {
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          "height": 997
        },
        "id": "e48ca017",
        "outputId": "6282fb99-91da-4771-a316-cc7acc5d9616"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(7, 5))\n",
        "plt.bar(primary_distribution.index, primary_distribution[\"count\"])\n",
        "plt.ylabel(\"Number of loans\")\n",
        "plt.xlabel(\"Primary risk class\")\n",
        "plt.title(\"Primary experiment: non-current outcome cohort\")\n",
        "plt.xticks(rotation=20, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "plt.bar(snapshot_distribution.index, snapshot_distribution[\"count\"])\n",
        "plt.ylabel(\"Number of loans\")\n",
        "plt.xlabel(\"Snapshot risk class\")\n",
        "plt.title(\"Sensitivity experiment: full operational snapshot\")\n",
        "plt.xticks(rotation=20, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f4f1a105",
      "metadata": {
        "id": "f4f1a105"
      },
      "source": [
        "### Critical target justification\n",
        "\n",
        "The primary cohort is not a perfect lifetime-default cohort: delinquent loans may recover, and only a small number are fully charged off. Nevertheless, excluding `Current` accounts materially reduces right-censoring and avoids treating unresolved loans as completed successes. The full-snapshot experiment is retained as a sensitivity analysis to quantify how the original censoring structure changes model behaviour. Conclusions must therefore be framed as **observed loan-performance classification**, not causal or lifetime probability-of-default estimation."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f5eba69e",
      "metadata": {
        "id": "f5eba69e"
      },
      "source": [
        "## 4. Data-quality rules, feature engineering and leakage governance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "6452b39a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "6452b39a",
        "outputId": "0e8c2b31-27e1-4db9-a325-72d251908a56"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                             count       mean         std        min  \\\n",
              "nonpositive_income_flag   625.0000     0.0048      0.0692     0.0000   \n",
              "credit_history_years      625.0000    16.7136      7.6805     4.0000   \n",
              "loan_to_income            622.0000     0.2431      0.1959     0.0080   \n",
              "credit_utilisation_ratio  624.0000     0.3903      0.2791     0.0000   \n",
              "income_per_open_account   621.0000 9,967.8081 18,602.7159 1,000.0000   \n",
              "log_annual_income         625.0000    11.0882      0.9859     0.0000   \n",
              "log_loan_amount           625.0000     9.4425      0.7771     6.9088   \n",
              "log_total_credit_limit    625.0000    11.6526      1.2154     0.0000   \n",
              "log_total_credit_utilized 625.0000    10.2679      1.5720     0.0000   \n",
              "\n",
              "                                 25%        50%         75%          max  \n",
              "nonpositive_income_flag       0.0000     0.0000      0.0000       1.0000  \n",
              "credit_history_years         12.0000    15.0000     20.0000      53.0000  \n",
              "loan_to_income                0.1126     0.1950      0.3333       2.3333  \n",
              "credit_utilisation_ratio      0.1495     0.3267      0.6239       1.1378  \n",
              "income_per_open_account   4,500.0000 6,800.0000 10,900.0000 350,000.0000  \n",
              "log_annual_income            10.7790    11.1375     11.5129      14.6484  \n",
              "log_loan_amount               8.9873     9.5179     10.0859      10.5967  \n",
              "log_total_credit_limit       10.9255    11.7353     12.6152      14.8311  \n",
              "log_total_credit_utilized     9.7978    10.5790     11.1698      13.7562  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3d11addc-83f0-4c6c-bff9-f9797018a4fd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>nonpositive_income_flag</th>\n",
              "      <td>625.0000</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.0692</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_history_years</th>\n",
              "      <td>625.0000</td>\n",
              "      <td>16.7136</td>\n",
              "      <td>7.6805</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>12.0000</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>20.0000</td>\n",
              "      <td>53.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_to_income</th>\n",
              "      <td>622.0000</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>0.1959</td>\n",
              "      <td>0.0080</td>\n",
              "      <td>0.1126</td>\n",
              "      <td>0.1950</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>2.3333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_utilisation_ratio</th>\n",
              "      <td>624.0000</td>\n",
              "      <td>0.3903</td>\n",
              "      <td>0.2791</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1495</td>\n",
              "      <td>0.3267</td>\n",
              "      <td>0.6239</td>\n",
              "      <td>1.1378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>income_per_open_account</th>\n",
              "      <td>621.0000</td>\n",
              "      <td>9,967.8081</td>\n",
              "      <td>18,602.7159</td>\n",
              "      <td>1,000.0000</td>\n",
              "      <td>4,500.0000</td>\n",
              "      <td>6,800.0000</td>\n",
              "      <td>10,900.0000</td>\n",
              "      <td>350,000.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log_annual_income</th>\n",
              "      <td>625.0000</td>\n",
              "      <td>11.0882</td>\n",
              "      <td>0.9859</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>10.7790</td>\n",
              "      <td>11.1375</td>\n",
              "      <td>11.5129</td>\n",
              "      <td>14.6484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log_loan_amount</th>\n",
              "      <td>625.0000</td>\n",
              "      <td>9.4425</td>\n",
              "      <td>0.7771</td>\n",
              "      <td>6.9088</td>\n",
              "      <td>8.9873</td>\n",
              "      <td>9.5179</td>\n",
              "      <td>10.0859</td>\n",
              "      <td>10.5967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log_total_credit_limit</th>\n",
              "      <td>625.0000</td>\n",
              "      <td>11.6526</td>\n",
              "      <td>1.2154</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>10.9255</td>\n",
              "      <td>11.7353</td>\n",
              "      <td>12.6152</td>\n",
              "      <td>14.8311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log_total_credit_utilized</th>\n",
              "      <td>625.0000</td>\n",
              "      <td>10.2679</td>\n",
              "      <td>1.5720</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>9.7978</td>\n",
              "      <td>10.5790</td>\n",
              "      <td>11.1698</td>\n",
              "      <td>13.7562</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(primary_cohort[engineered_features]\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5365907428821481,\n        \"min\": 621.0,\n        \"max\": 625.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          622.0,\n          621.0,\n          625.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3320.1163008762796,\n        \"min\": 0.0048,\n        \"max\": 9967.808134434657,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          11.652615126651517,\n          16.7136,\n          11.088156922848075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6200.373432704975,\n        \"min\": 0.06917091429651144,\n        \"max\": 18602.7158541262,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.215443766230054,\n          7.680492104960391,\n          0.9858845476348926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 332.8876373141683,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.0,\n          6.90875477931522,\n          0.008\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1497.8107106959608,\n        \"min\": 0.0,\n        \"max\": 4500.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          10.925542718482083,\n          12.0,\n          10.778977123006351\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2264.236749541861,\n        \"min\": 0.0,\n        \"max\": 6800.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          11.735276702486741,\n          15.0,\n          11.13751903414526\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3630.57546824978,\n        \"min\": 0.0,\n        \"max\": 10900.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          12.615201035620073,\n          20.0,\n          11.51293546492023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 116662.03009075527,\n        \"min\": 1.0,\n        \"max\": 350000.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          14.831103853099838,\n          53.0,\n          14.648420115681892\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Transparent quality flags are retained rather than silently deleting questionable records.\n",
        "data[\"nonpositive_income_flag\"] = data[\"annual_income\"].le(0).astype(int)\n",
        "\n",
        "# Non-positive income cannot support meaningful ratios and is converted to missing.\n",
        "clean_income = data[\"annual_income\"].where(data[\"annual_income\"].gt(0))\n",
        "\n",
        "issue_year = pd.to_datetime(\n",
        "    data[\"issue_month\"],\n",
        "    format=\"%b-%Y\",\n",
        "    errors=\"coerce\",\n",
        ").dt.year\n",
        "\n",
        "data[\"credit_history_years\"] = issue_year - data[\"earliest_credit_line\"]\n",
        "\n",
        "# Fixed upper caps prevent isolated data-entry anomalies from dominating ratios.\n",
        "# They are deliberately generous and do not use information estimated from the test set.\n",
        "data[\"loan_to_income\"] = (\n",
        "    data[\"loan_amount\"] / clean_income\n",
        ").clip(lower=0, upper=10)\n",
        "\n",
        "data[\"credit_utilisation_ratio\"] = (\n",
        "    data[\"total_credit_utilized\"]\n",
        "    / data[\"total_credit_limit\"].replace(0, np.nan)\n",
        ").clip(lower=0, upper=5)\n",
        "\n",
        "data[\"income_per_open_account\"] = (\n",
        "    clean_income\n",
        "    / data[\"open_credit_lines\"].replace(0, np.nan)\n",
        ")\n",
        "\n",
        "# Log features retain ordering while reducing the leverage of extreme monetary values.\n",
        "for source_column in [\n",
        "    \"annual_income\",\n",
        "    \"loan_amount\",\n",
        "    \"total_credit_limit\",\n",
        "    \"total_credit_utilized\",\n",
        "]:\n",
        "    data[f\"log_{source_column}\"] = np.log1p(\n",
        "        data[source_column].clip(lower=0)\n",
        "    )\n",
        "\n",
        "# Recreate the primary cohort after feature engineering.\n",
        "primary_cohort = data.loc[data[\"loan_status\"].ne(\"Current\")].copy()\n",
        "\n",
        "engineered_features = [\n",
        "    \"nonpositive_income_flag\",\n",
        "    \"credit_history_years\",\n",
        "    \"loan_to_income\",\n",
        "    \"credit_utilisation_ratio\",\n",
        "    \"income_per_open_account\",\n",
        "    \"log_annual_income\",\n",
        "    \"log_loan_amount\",\n",
        "    \"log_total_credit_limit\",\n",
        "    \"log_total_credit_utilized\",\n",
        "]\n",
        "\n",
        "display(primary_cohort[engineered_features].describe().T)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "46c4d531",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "46c4d531",
        "outputId": "ee7cab6d-a880-485d-bd4f-be966130b564"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                              column_group  \\\n",
              "0                               Identifier   \n",
              "1       Post-outcome repayment information   \n",
              "2  Lender-derived underwriting and pricing   \n",
              "3               High-cardinality free text   \n",
              "4                                  Targets   \n",
              "\n",
              "                                             columns  \\\n",
              "0                                         Unnamed: 0   \n",
              "1  balance, paid_total, paid_principal, paid_inte...   \n",
              "2       grade, sub_grade, interest_rate, installment   \n",
              "3                                          emp_title   \n",
              "4  loan_status, primary_risk_class, snapshot_risk...   \n",
              "\n",
              "               primary_action  \\\n",
              "0                     Exclude   \n",
              "1                     Exclude   \n",
              "2  Exclude from primary model   \n",
              "3                     Exclude   \n",
              "4     Exclude from predictors   \n",
              "\n",
              "                                       justification  \n",
              "0  Identifiers do not represent borrower risk and...  \n",
              "1  Observed after lending; inclusion would create...  \n",
              "2  May reproduce an existing proprietary risk dec...  \n",
              "3  Thousands of sparse employment titles create m...  \n",
              "4                             Direct target leakage.  "
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              "        google.colab.notebook.generateWithVariable('feature_governance');\n",
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              "type": "dataframe",
              "variable_name": "feature_governance",
              "summary": "{\n  \"name\": \"feature_governance\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"column_group\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Post-outcome repayment information\",\n          \"Targets\",\n          \"Lender-derived underwriting and pricing\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"columns\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"balance, paid_total, paid_principal, paid_interest, paid_late_fees\",\n          \"loan_status, primary_risk_class, snapshot_risk_class\",\n          \"grade, sub_grade, interest_rate, installment\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"primary_action\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Exclude\",\n          \"Exclude from primary model\",\n          \"Exclude from predictors\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"justification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Observed after lending; inclusion would create temporal target leakage.\",\n          \"Direct target leakage.\",\n          \"May reproduce an existing proprietary risk decision rather than independently estimate risk.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "ID_COLUMNS = [\"Unnamed: 0\"]\n",
        "\n",
        "# These variables are observed after lending and directly reveal repayment progress.\n",
        "POST_OUTCOME_COLUMNS = [\n",
        "    \"balance\",\n",
        "    \"paid_total\",\n",
        "    \"paid_principal\",\n",
        "    \"paid_interest\",\n",
        "    \"paid_late_fees\",\n",
        "]\n",
        "\n",
        "# These variables are known at origination but partly encode Lending Club's prior\n",
        "# underwriting and pricing system. They are excluded from the independent primary model.\n",
        "LENDER_DERIVED_COLUMNS = [\n",
        "    \"grade\",\n",
        "    \"sub_grade\",\n",
        "    \"interest_rate\",\n",
        "    \"installment\",\n",
        "]\n",
        "\n",
        "HIGH_CARDINALITY_COLUMNS = [\"emp_title\"]\n",
        "\n",
        "TARGET_COLUMNS = [\n",
        "    \"loan_status\",\n",
        "    \"primary_risk_class\",\n",
        "    \"snapshot_risk_class\",\n",
        "]\n",
        "\n",
        "feature_governance = pd.DataFrame([\n",
        "    {\n",
        "        \"column_group\": \"Identifier\",\n",
        "        \"columns\": \", \".join(ID_COLUMNS),\n",
        "        \"primary_action\": \"Exclude\",\n",
        "        \"justification\": \"Identifiers do not represent borrower risk and can permit memorisation.\",\n",
        "    },\n",
        "    {\n",
        "        \"column_group\": \"Post-outcome repayment information\",\n",
        "        \"columns\": \", \".join(POST_OUTCOME_COLUMNS),\n",
        "        \"primary_action\": \"Exclude\",\n",
        "        \"justification\": \"Observed after lending; inclusion would create temporal target leakage.\",\n",
        "    },\n",
        "    {\n",
        "        \"column_group\": \"Lender-derived underwriting and pricing\",\n",
        "        \"columns\": \", \".join(LENDER_DERIVED_COLUMNS),\n",
        "        \"primary_action\": \"Exclude from primary model\",\n",
        "        \"justification\": \"May reproduce an existing proprietary risk decision rather than independently estimate risk.\",\n",
        "    },\n",
        "    {\n",
        "        \"column_group\": \"High-cardinality free text\",\n",
        "        \"columns\": \", \".join(HIGH_CARDINALITY_COLUMNS),\n",
        "        \"primary_action\": \"Exclude\",\n",
        "        \"justification\": \"Thousands of sparse employment titles create memorisation and weak transportability.\",\n",
        "    },\n",
        "    {\n",
        "        \"column_group\": \"Targets\",\n",
        "        \"columns\": \", \".join(TARGET_COLUMNS),\n",
        "        \"primary_action\": \"Exclude from predictors\",\n",
        "        \"justification\": \"Direct target leakage.\",\n",
        "    },\n",
        "])\n",
        "\n",
        "display(feature_governance)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "a3f6e494",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3f6e494",
        "outputId": "447b2a26-4be5-44c9-d1a1-6ee9f22da194"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Primary predictor matrix: (625, 53)\n",
            "Numerical predictors: 44\n",
            "Categorical predictors: 9\n"
          ]
        }
      ],
      "source": [
        "PRIMARY_EXCLUSIONS = (\n",
        "    ID_COLUMNS\n",
        "    + POST_OUTCOME_COLUMNS\n",
        "    + LENDER_DERIVED_COLUMNS\n",
        "    + HIGH_CARDINALITY_COLUMNS\n",
        "    + TARGET_COLUMNS\n",
        ")\n",
        "\n",
        "X_primary = primary_cohort.drop(columns=PRIMARY_EXCLUSIONS, errors=\"ignore\")\n",
        "y_primary = primary_cohort[\"primary_risk_class\"].copy()\n",
        "\n",
        "print(\"Primary predictor matrix:\", X_primary.shape)\n",
        "print(\"Numerical predictors:\", X_primary.select_dtypes(include=np.number).shape[1])\n",
        "print(\"Categorical predictors:\", X_primary.select_dtypes(exclude=np.number).shape[1])\n",
        "\n",
        "assert \"loan_status\" not in X_primary.columns\n",
        "assert not set(POST_OUTCOME_COLUMNS).intersection(X_primary.columns)\n",
        "assert not set(LENDER_DERIVED_COLUMNS).intersection(X_primary.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "54f83d11",
      "metadata": {
        "id": "54f83d11"
      },
      "source": [
        "### Leakage position\n",
        "\n",
        "The feature set approximates an **origination-time information set**. Post-payment variables are removed even though retaining them would likely increase apparent accuracy. `grade`, `sub_grade`, `interest_rate` and `installment` are tested later only as a declared sensitivity analysis because they partly encode the lender's prior assessment. This separation supports a more credible claim that the primary models learn from applicant and credit-file information rather than reproducing known outcomes or prior scores."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fff4df8e",
      "metadata": {
        "id": "fff4df8e"
      },
      "source": [
        "## 5. Exploratory data analysis for the primary cohort"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "40775ef3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "40775ef3",
        "outputId": "1e1b2982-fbfb-4ace-b36f-9a244bfa682a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    annual_income  debt_to_income  loan_amount  delinq_2y  \\\n",
              "primary_risk_class                                                          \n",
              "Repaid                68,700.0000         16.1500  12,600.0000     0.0000   \n",
              "Early Delinquency     65,000.0000         17.4200  15,000.0000     0.0000   \n",
              "Severe Delinquency    70,000.0000         15.9500  15,000.0000     0.0000   \n",
              "\n",
              "                    inquiries_last_12m  accounts_opened_24m  loan_to_income  \\\n",
              "primary_risk_class                                                            \n",
              "Repaid                          2.0000               4.0000          0.1797   \n",
              "Early Delinquency               2.0000               4.0000          0.2153   \n",
              "Severe Delinquency              2.0000               3.0000          0.2344   \n",
              "\n",
              "                    credit_utilisation_ratio  \n",
              "primary_risk_class                            \n",
              "Repaid                                0.3097  \n",
              "Early Delinquency                     0.3977  \n",
              "Severe Delinquency                    0.3137  "
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>annual_income</th>\n",
              "      <th>debt_to_income</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>delinq_2y</th>\n",
              "      <th>inquiries_last_12m</th>\n",
              "      <th>accounts_opened_24m</th>\n",
              "      <th>loan_to_income</th>\n",
              "      <th>credit_utilisation_ratio</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>primary_risk_class</th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Repaid</th>\n",
              "      <td>68,700.0000</td>\n",
              "      <td>16.1500</td>\n",
              "      <td>12,600.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>0.1797</td>\n",
              "      <td>0.3097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Early Delinquency</th>\n",
              "      <td>65,000.0000</td>\n",
              "      <td>17.4200</td>\n",
              "      <td>15,000.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>0.2153</td>\n",
              "      <td>0.3977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Severe Delinquency</th>\n",
              "      <td>70,000.0000</td>\n",
              "      <td>15.9500</td>\n",
              "      <td>15,000.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>0.2344</td>\n",
              "      <td>0.3137</td>\n",
              "    </tr>\n",
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              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \")\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"primary_risk_class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Repaid\",\n          \"Early Delinquency\",\n          \"Severe Delinquency\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"annual_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2594.2243542145693,\n        \"min\": 65000.0,\n        \"max\": 70000.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          68700.0,\n          65000.0,\n          70000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"debt_to_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7972661621650173,\n        \"min\": 15.95,\n        \"max\": 17.42,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          16.15,\n          17.42,\n          15.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1385.6406460551018,\n        \"min\": 12600.0,\n        \"max\": 15000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          15000.0,\n          12600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delinq_2y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inquiries_last_12m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accounts_opened_24m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5773502691896258,\n        \"min\": 3.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loan_to_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.027757040446989026,\n        \"min\": 0.17967914438502675,\n        \"max\": 0.234375,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.17967914438502675\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"credit_utilisation_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04968702163945816,\n        \"min\": 0.30967972938593596,\n        \"max\": 0.39769613852923136,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.30967972938593596\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "eda_numeric = [\n",
        "    \"annual_income\",\n",
        "    \"debt_to_income\",\n",
        "    \"loan_amount\",\n",
        "    \"delinq_2y\",\n",
        "    \"inquiries_last_12m\",\n",
        "    \"accounts_opened_24m\",\n",
        "    \"loan_to_income\",\n",
        "    \"credit_utilisation_ratio\",\n",
        "]\n",
        "\n",
        "display(\n",
        "    primary_cohort.groupby(\n",
        "        \"primary_risk_class\",\n",
        "        observed=False,\n",
        "    )[eda_numeric]\n",
        "    .median()\n",
        "    .reindex(PRIMARY_CLASS_ORDER)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "a82ed655",
      "metadata": {
        "colab": {
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          "height": 1000
        },
        "id": "a82ed655",
        "outputId": "721afcc6-1a8f-46ac-97b0-514c2d11b443"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for feature in [\n",
        "    \"debt_to_income\",\n",
        "    \"loan_amount\",\n",
        "    \"inquiries_last_12m\",\n",
        "    \"loan_to_income\",\n",
        "]:\n",
        "    values_by_class = [\n",
        "        primary_cohort.loc[\n",
        "            primary_cohort[\"primary_risk_class\"].eq(class_name),\n",
        "            feature,\n",
        "        ].dropna().values\n",
        "        for class_name in PRIMARY_CLASS_ORDER\n",
        "    ]\n",
        "\n",
        "    plt.figure(figsize=(8, 5))\n",
        "    plt.boxplot(\n",
        "        values_by_class,\n",
        "        tick_labels=PRIMARY_CLASS_ORDER,\n",
        "        showfliers=False,\n",
        "    )\n",
        "    plt.ylabel(feature.replace(\"_\", \" \").title())\n",
        "    plt.xlabel(\"Observed performance class\")\n",
        "    plt.title(f\"{feature.replace('_', ' ').title()} by performance class\")\n",
        "    plt.xticks(rotation=15)\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "d7017c6c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "d7017c6c",
        "outputId": "0e96300c-2323-4249-bde3-385cb2b78958"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_features = [\n",
        "    \"annual_income\",\n",
        "    \"debt_to_income\",\n",
        "    \"loan_amount\",\n",
        "    \"delinq_2y\",\n",
        "    \"inquiries_last_12m\",\n",
        "    \"total_credit_limit\",\n",
        "    \"total_credit_utilized\",\n",
        "    \"accounts_opened_24m\",\n",
        "    \"loan_to_income\",\n",
        "    \"credit_utilisation_ratio\",\n",
        "]\n",
        "\n",
        "corr = primary_cohort[correlation_features].corr(method=\"spearman\")\n",
        "\n",
        "plt.figure(figsize=(9, 8))\n",
        "image = plt.imshow(corr.values, aspect=\"auto\")\n",
        "plt.colorbar(image, fraction=0.046, pad=0.04)\n",
        "plt.xticks(\n",
        "    range(len(correlation_features)),\n",
        "    correlation_features,\n",
        "    rotation=90,\n",
        ")\n",
        "plt.yticks(\n",
        "    range(len(correlation_features)),\n",
        "    correlation_features,\n",
        ")\n",
        "plt.title(\"Spearman correlation among selected numerical variables\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "c1739d6e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "c1739d6e",
        "outputId": "79157214-c9df-471f-c8b5-2e176e21b9ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Observed adverse-status rate by loan_purpose\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                      n  adverse_count  adverse_rate\n",
              "loan_purpose                                        \n",
              "major_purchase       27              9        0.3333\n",
              "home_improvement     48             15        0.3125\n",
              "medical              21              6        0.2857\n",
              "debt_consolidation  312             88        0.2821\n",
              "credit_card          94             24        0.2553\n",
              "other                76             17        0.2237"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52c94bc7-e8b3-4b2e-b771-317b708b2e45\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "                 n  adverse_count  adverse_rate\n",
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              "                    n  adverse_count  adverse_rate\n",
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              "summary": "{\n  \"name\": \"    )\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"application_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"individual\",\n          \"joint\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306,\n        \"min\": 96,\n        \"max\": 529,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          529,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adverse_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73,\n        \"min\": 37,\n        \"max\": 141,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          141,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adverse_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08405804265168534,\n        \"min\": 0.2665406427221172,\n        \"max\": 0.3854166666666667,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.2665406427221172,\n          0.3854166666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def grouped_adverse_summary(\n",
        "    frame,\n",
        "    group_column,\n",
        "    target_column,\n",
        "    reference_class,\n",
        "    minimum_group_size=20,\n",
        "):\n",
        "    result = (\n",
        "        frame.assign(\n",
        "            adverse_status=frame[target_column].ne(reference_class)\n",
        "        )\n",
        "        .groupby(group_column, dropna=False)\n",
        "        .agg(\n",
        "            n=(target_column, \"size\"),\n",
        "            adverse_count=(\"adverse_status\", \"sum\"),\n",
        "            adverse_rate=(\"adverse_status\", \"mean\"),\n",
        "        )\n",
        "        .query(\"n >= @minimum_group_size\")\n",
        "        .sort_values(\"adverse_rate\", ascending=False)\n",
        "    )\n",
        "    return result\n",
        "\n",
        "\n",
        "for group_column in [\n",
        "    \"loan_purpose\",\n",
        "    \"homeownership\",\n",
        "    \"application_type\",\n",
        "]:\n",
        "    print(f\"\\nObserved adverse-status rate by {group_column}\")\n",
        "    display(\n",
        "        grouped_adverse_summary(\n",
        "            primary_cohort,\n",
        "            group_column,\n",
        "            \"primary_risk_class\",\n",
        "            \"Repaid\",\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d95432b6",
      "metadata": {
        "id": "d95432b6"
      },
      "source": [
        "### EDA interpretation\n",
        "\n",
        "The descriptive patterns are exploratory associations. They can be influenced by historical underwriting, omitted variables, sample selection and small subgroup counts. They do not demonstrate that any borrower attribute causes delinquency. The multivariable models below test whether the patterns remain predictive after joint adjustment, but they also remain associational rather than causal."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9563d05",
      "metadata": {
        "id": "c9563d05"
      },
      "source": [
        "## 6. Stratified hold-out design"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "b9dac53b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "b9dac53b",
        "outputId": "60530698-5925-4de1-e0b6-b5467d78c181"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    train_count  test_count  train_pct  test_pct\n",
              "primary_risk_class                                              \n",
              "Repaid                      358          89    71.6000   71.2000\n",
              "Early Delinquency            84          21    16.8000   16.8000\n",
              "Severe Delinquency           58          15    11.6000   12.0000"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train_count</th>\n",
              "      <th>test_count</th>\n",
              "      <th>train_pct</th>\n",
              "      <th>test_pct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>primary_risk_class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Repaid</th>\n",
              "      <td>358</td>\n",
              "      <td>89</td>\n",
              "      <td>71.6000</td>\n",
              "      <td>71.2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Early Delinquency</th>\n",
              "      <td>84</td>\n",
              "      <td>21</td>\n",
              "      <td>16.8000</td>\n",
              "      <td>16.8000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Severe Delinquency</th>\n",
              "      <td>58</td>\n",
              "      <td>15</td>\n",
              "      <td>11.6000</td>\n",
              "      <td>12.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <style>\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8df4c0d4-665c-4194-8362-a8353f34992f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8df4c0d4-665c-4194-8362-a8353f34992f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_9317c33c-d540-42dc-adae-e1c9364a5cfa\">\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('split_summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('split_summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "split_summary",
              "summary": "{\n  \"name\": \"split_summary\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"primary_risk_class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Repaid\",\n          \"Early Delinquency\",\n          \"Severe Delinquency\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 166,\n        \"min\": 58,\n        \"max\": 358,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          358,\n          84,\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 41,\n        \"min\": 15,\n        \"max\": 89,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          89,\n          21,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.241740828863534,\n        \"min\": 11.600000000000001,\n        \"max\": 71.6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          71.6,\n          16.8,\n          11.600000000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.88120030250315,\n        \"min\": 12.0,\n        \"max\": 71.2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          71.2,\n          16.8,\n          12.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training shape: (500, 53)\n",
            "Untouched test shape: (125, 53)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X_primary,\n",
        "    y_primary,\n",
        "    test_size=TEST_SIZE,\n",
        "    stratify=y_primary,\n",
        "    random_state=RANDOM_STATE,\n",
        ")\n",
        "\n",
        "split_summary = pd.concat(\n",
        "    [\n",
        "        y_train.value_counts().reindex(PRIMARY_CLASS_ORDER).rename(\"train_count\"),\n",
        "        y_test.value_counts().reindex(PRIMARY_CLASS_ORDER).rename(\"test_count\"),\n",
        "    ],\n",
        "    axis=1,\n",
        ")\n",
        "split_summary[\"train_pct\"] = split_summary[\"train_count\"] / len(y_train) * 100\n",
        "split_summary[\"test_pct\"] = split_summary[\"test_count\"] / len(y_test) * 100\n",
        "\n",
        "display(split_summary)\n",
        "print(\"Training shape:\", X_train.shape)\n",
        "print(\"Untouched test shape:\", X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7d2ecf4c",
      "metadata": {
        "id": "7d2ecf4c"
      },
      "source": [
        "The test set is not used for feature selection, imputation, scaling, category encoding, resampling, parameter tuning or threshold selection. Repeated stratified cross-validation is applied only to the training set, and all fitted preprocessing remains inside each fold."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "02a68e3c",
      "metadata": {
        "id": "02a68e3c"
      },
      "source": [
        "## 7. Leakage-safe preprocessing pipelines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "32f3d13c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32f3d13c",
        "outputId": "56588a79-1b75-432d-8f60-09641d1aaa1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numerical predictors: 44\n",
            "Categorical predictors: 9\n"
          ]
        }
      ],
      "source": [
        "def make_preprocessor(reference_frame, scale_numeric):\n",
        "    numeric_columns = (\n",
        "        reference_frame\n",
        "        .select_dtypes(include=np.number)\n",
        "        .columns\n",
        "        .tolist()\n",
        "    )\n",
        "    categorical_columns = (\n",
        "        reference_frame\n",
        "        .select_dtypes(exclude=np.number)\n",
        "        .columns\n",
        "        .tolist()\n",
        "    )\n",
        "\n",
        "    numeric_steps = [\n",
        "        (\n",
        "            \"imputer\",\n",
        "            SimpleImputer(\n",
        "                strategy=\"median\",\n",
        "                add_indicator=True,\n",
        "            ),\n",
        "        ),\n",
        "    ]\n",
        "\n",
        "    if scale_numeric:\n",
        "        numeric_steps.append(\n",
        "            (\"scaler\", StandardScaler())\n",
        "        )\n",
        "\n",
        "    numeric_pipeline = Pipeline(numeric_steps)\n",
        "\n",
        "    categorical_pipeline = Pipeline([\n",
        "        (\n",
        "            \"imputer\",\n",
        "            SimpleImputer(\n",
        "                strategy=\"constant\",\n",
        "                fill_value=\"Missing\",\n",
        "            ),\n",
        "        ),\n",
        "        (\n",
        "            \"onehot\",\n",
        "            OneHotEncoder(\n",
        "                handle_unknown=\"ignore\",\n",
        "                min_frequency=5,\n",
        "            ),\n",
        "        ),\n",
        "    ])\n",
        "\n",
        "    preprocessor = ColumnTransformer([\n",
        "        (\"numeric\", numeric_pipeline, numeric_columns),\n",
        "        (\"categorical\", categorical_pipeline, categorical_columns),\n",
        "    ])\n",
        "\n",
        "    return preprocessor, numeric_columns, categorical_columns\n",
        "\n",
        "\n",
        "logistic_preprocessor, logistic_numeric, logistic_categorical = make_preprocessor(\n",
        "    X_train,\n",
        "    scale_numeric=True,\n",
        ")\n",
        "forest_preprocessor, forest_numeric, forest_categorical = make_preprocessor(\n",
        "    X_train,\n",
        "    scale_numeric=False,\n",
        ")\n",
        "\n",
        "print(\"Numerical predictors:\", len(logistic_numeric))\n",
        "print(\"Categorical predictors:\", len(logistic_categorical))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e74b0ede",
      "metadata": {
        "id": "e74b0ede"
      },
      "source": [
        "## 8. Evaluation and interpretation utilities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "6621697b",
      "metadata": {
        "id": "6621697b"
      },
      "outputs": [],
      "source": [
        "def safe_divide(numerator, denominator):\n",
        "    return np.nan if denominator == 0 else numerator / denominator\n",
        "\n",
        "\n",
        "def extract_estimator_classes(estimator):\n",
        "    # Pipelines store the classifier under the final model step.\n",
        "    if hasattr(estimator, \"named_steps\"):\n",
        "        return np.asarray(estimator.named_steps[\"model\"].classes_)\n",
        "    return np.asarray(estimator.classes_)\n",
        "\n",
        "\n",
        "def evaluate_fitted_model(\n",
        "    name,\n",
        "    estimator,\n",
        "    X_train_eval,\n",
        "    y_train_eval,\n",
        "    X_test_eval,\n",
        "    y_test_eval,\n",
        "    class_order,\n",
        "    focus_class,\n",
        "):\n",
        "    train_pred = estimator.predict(X_train_eval)\n",
        "    test_pred = estimator.predict(X_test_eval)\n",
        "    test_proba = estimator.predict_proba(X_test_eval)\n",
        "\n",
        "    model_classes = extract_estimator_classes(estimator)\n",
        "    focus_index = int(np.where(model_classes == focus_class)[0][0])\n",
        "\n",
        "    macro_precision, macro_recall, macro_f1, _ = (\n",
        "        precision_recall_fscore_support(\n",
        "            y_test_eval,\n",
        "            test_pred,\n",
        "            labels=class_order,\n",
        "            average=\"macro\",\n",
        "            zero_division=0,\n",
        "        )\n",
        "    )\n",
        "\n",
        "    focus_precision = precision_score(\n",
        "        y_test_eval,\n",
        "        test_pred,\n",
        "        labels=[focus_class],\n",
        "        average=\"macro\",\n",
        "        zero_division=0,\n",
        "    )\n",
        "    focus_recall = recall_score(\n",
        "        y_test_eval,\n",
        "        test_pred,\n",
        "        labels=[focus_class],\n",
        "        average=\"macro\",\n",
        "        zero_division=0,\n",
        "    )\n",
        "    focus_f1 = f1_score(\n",
        "        y_test_eval,\n",
        "        test_pred,\n",
        "        labels=[focus_class],\n",
        "        average=\"macro\",\n",
        "        zero_division=0,\n",
        "    )\n",
        "\n",
        "    try:\n",
        "        macro_auc = roc_auc_score(\n",
        "            y_test_eval,\n",
        "            test_proba,\n",
        "            labels=model_classes,\n",
        "            multi_class=\"ovr\",\n",
        "            average=\"macro\",\n",
        "        )\n",
        "    except ValueError:\n",
        "        macro_auc = np.nan\n",
        "\n",
        "    focus_true = y_test_eval.eq(focus_class).astype(int)\n",
        "    focus_probability = test_proba[:, focus_index]\n",
        "\n",
        "    summary = {\n",
        "        \"model\": name,\n",
        "        \"train_macro_f1\": f1_score(\n",
        "            y_train_eval,\n",
        "            train_pred,\n",
        "            labels=class_order,\n",
        "            average=\"macro\",\n",
        "            zero_division=0,\n",
        "        ),\n",
        "        \"test_accuracy\": accuracy_score(y_test_eval, test_pred),\n",
        "        \"test_balanced_accuracy\": balanced_accuracy_score(\n",
        "            y_test_eval,\n",
        "            test_pred,\n",
        "        ),\n",
        "        \"test_macro_precision\": macro_precision,\n",
        "        \"test_macro_recall\": macro_recall,\n",
        "        \"test_macro_f1\": macro_f1,\n",
        "        \"test_weighted_f1\": f1_score(\n",
        "            y_test_eval,\n",
        "            test_pred,\n",
        "            labels=class_order,\n",
        "            average=\"weighted\",\n",
        "            zero_division=0,\n",
        "        ),\n",
        "        \"test_macro_roc_auc_ovr\": macro_auc,\n",
        "        \"focus_precision\": focus_precision,\n",
        "        \"focus_recall\": focus_recall,\n",
        "        \"focus_f1\": focus_f1,\n",
        "        \"focus_average_precision\": average_precision_score(\n",
        "            focus_true,\n",
        "            focus_probability,\n",
        "        ),\n",
        "        \"focus_brier_score\": brier_score_loss(\n",
        "            focus_true,\n",
        "            focus_probability,\n",
        "        ),\n",
        "    }\n",
        "\n",
        "    report = pd.DataFrame(\n",
        "        classification_report(\n",
        "            y_test_eval,\n",
        "            test_pred,\n",
        "            labels=class_order,\n",
        "            output_dict=True,\n",
        "            zero_division=0,\n",
        "        )\n",
        "    ).T\n",
        "\n",
        "    return {\n",
        "        \"summary\": summary,\n",
        "        \"predictions\": pd.Series(\n",
        "            test_pred,\n",
        "            index=y_test_eval.index,\n",
        "            name=name,\n",
        "        ),\n",
        "        \"probabilities\": pd.DataFrame(\n",
        "            test_proba,\n",
        "            index=y_test_eval.index,\n",
        "            columns=model_classes,\n",
        "        ),\n",
        "        \"report\": report,\n",
        "    }\n",
        "\n",
        "\n",
        "def evaluate_predictions(\n",
        "    name,\n",
        "    y_true,\n",
        "    y_pred,\n",
        "    class_order,\n",
        "    focus_class,\n",
        "):\n",
        "    macro_precision, macro_recall, macro_f1, _ = (\n",
        "        precision_recall_fscore_support(\n",
        "            y_true,\n",
        "            y_pred,\n",
        "            labels=class_order,\n",
        "            average=\"macro\",\n",
        "            zero_division=0,\n",
        "        )\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"model\": name,\n",
        "        \"accuracy\": accuracy_score(y_true, y_pred),\n",
        "        \"balanced_accuracy\": balanced_accuracy_score(y_true, y_pred),\n",
        "        \"macro_precision\": macro_precision,\n",
        "        \"macro_recall\": macro_recall,\n",
        "        \"macro_f1\": macro_f1,\n",
        "        \"weighted_f1\": f1_score(\n",
        "            y_true,\n",
        "            y_pred,\n",
        "            labels=class_order,\n",
        "            average=\"weighted\",\n",
        "            zero_division=0,\n",
        "        ),\n",
        "        \"focus_precision\": precision_score(\n",
        "            y_true,\n",
        "            y_pred,\n",
        "            labels=[focus_class],\n",
        "            average=\"macro\",\n",
        "            zero_division=0,\n",
        "        ),\n",
        "        \"focus_recall\": recall_score(\n",
        "            y_true,\n",
        "            y_pred,\n",
        "            labels=[focus_class],\n",
        "            average=\"macro\",\n",
        "            zero_division=0,\n",
        "        ),\n",
        "        \"focus_f1\": f1_score(\n",
        "            y_true,\n",
        "            y_pred,\n",
        "            labels=[focus_class],\n",
        "            average=\"macro\",\n",
        "            zero_division=0,\n",
        "        ),\n",
        "    }\n",
        "\n",
        "\n",
        "def plot_confusion_matrices(\n",
        "    y_true,\n",
        "    y_pred,\n",
        "    class_order,\n",
        "    title_prefix,\n",
        "):\n",
        "    raw_cm = confusion_matrix(\n",
        "        y_true,\n",
        "        y_pred,\n",
        "        labels=class_order,\n",
        "    )\n",
        "    ConfusionMatrixDisplay(\n",
        "        raw_cm,\n",
        "        display_labels=class_order,\n",
        "    ).plot(values_format=\"d\")\n",
        "    plt.title(f\"{title_prefix}: raw confusion matrix\")\n",
        "    plt.xticks(rotation=20)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    normalised_cm = confusion_matrix(\n",
        "        y_true,\n",
        "        y_pred,\n",
        "        labels=class_order,\n",
        "        normalize=\"true\",\n",
        "    )\n",
        "    ConfusionMatrixDisplay(\n",
        "        normalised_cm,\n",
        "        display_labels=class_order,\n",
        "    ).plot(values_format=\".2f\")\n",
        "    plt.title(f\"{title_prefix}: row-normalised confusion matrix\")\n",
        "    plt.xticks(rotation=20)\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "84d9e765",
      "metadata": {
        "id": "84d9e765"
      },
      "outputs": [],
      "source": [
        "def automated_model_commentary(\n",
        "    validation_table,\n",
        "    test_table,\n",
        "    baseline_name,\n",
        "    candidate_names,\n",
        "    focus_class,\n",
        "):\n",
        "    baseline_macro_f1 = test_table.loc[baseline_name, \"test_macro_f1\"]\n",
        "    paragraphs = []\n",
        "\n",
        "    for model_name in candidate_names:\n",
        "        cv_score = validation_table.loc[model_name, \"cv_macro_f1_mean\"]\n",
        "        cv_std = validation_table.loc[model_name, \"cv_macro_f1_std\"]\n",
        "        cv_train = validation_table.loc[model_name, \"cv_train_macro_f1\"]\n",
        "        test_score = test_table.loc[model_name, \"test_macro_f1\"]\n",
        "        balanced = test_table.loc[model_name, \"test_balanced_accuracy\"]\n",
        "        focus_recall = test_table.loc[model_name, \"focus_recall\"]\n",
        "\n",
        "        gap = cv_train - cv_score\n",
        "        baseline_delta = test_score - baseline_macro_f1\n",
        "\n",
        "        if gap > 0.15:\n",
        "            overfit_text = (\n",
        "                f\"The training–validation macro-F1 gap is {gap:.3f}, \"\n",
        "                \"which indicates substantial overfitting.\"\n",
        "            )\n",
        "        elif gap > 0.07:\n",
        "            overfit_text = (\n",
        "                f\"The training–validation macro-F1 gap is {gap:.3f}, \"\n",
        "                \"which indicates moderate overfitting.\"\n",
        "            )\n",
        "        else:\n",
        "            overfit_text = (\n",
        "                f\"The training–validation macro-F1 gap is {gap:.3f}, \"\n",
        "                \"which does not indicate severe overfitting.\"\n",
        "            )\n",
        "\n",
        "        if baseline_delta <= 0:\n",
        "            utility_text = (\n",
        "                f\"On the hold-out set it did not exceed the majority baseline \"\n",
        "                f\"(macro-F1 difference {baseline_delta:.3f}).\"\n",
        "            )\n",
        "        elif baseline_delta < 0.05:\n",
        "            utility_text = (\n",
        "                f\"On the hold-out set it exceeded the majority baseline by only \"\n",
        "                f\"{baseline_delta:.3f} macro-F1 points, so the practical gain is limited.\"\n",
        "            )\n",
        "        else:\n",
        "            utility_text = (\n",
        "                f\"On the hold-out set it exceeded the majority baseline by \"\n",
        "                f\"{baseline_delta:.3f} macro-F1 points.\"\n",
        "            )\n",
        "\n",
        "        paragraphs.append(\n",
        "            f\"**{model_name}.** Mean repeated-CV macro F1 was \"\n",
        "            f\"{cv_score:.3f} ± {cv_std:.3f}; hold-out macro F1 was \"\n",
        "            f\"{test_score:.3f}, balanced accuracy was {balanced:.3f}, and \"\n",
        "            f\"{focus_class} recall was {focus_recall:.3f}. \"\n",
        "            f\"{overfit_text} {utility_text}\"\n",
        "        )\n",
        "\n",
        "    display(\n",
        "        Markdown(\n",
        "            \"### Result-specific critical analysis\\n\\n\"\n",
        "            + \"\\n\\n\".join(paragraphs)\n",
        "            + \"\\n\\nModel selection is based on training-set cross-validation, \"\n",
        "              \"whereas deployment suitability is judged separately from the untouched \"\n",
        "              \"test set, minority-class errors, calibration and governance constraints.\"\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "37f0d0d1",
      "metadata": {
        "id": "37f0d0d1"
      },
      "source": [
        "## 9. Naïve baselines and model-selection design"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "531903b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "531903b6",
        "outputId": "d552bf58-dc91-418a-af1d-a1c2a4b0869a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   train_macro_f1  test_accuracy  test_balanced_accuracy  \\\n",
              "model                                                                      \n",
              "Majority baseline          0.2782         0.7120                  0.3333   \n",
              "\n",
              "                   test_macro_precision  test_macro_recall  test_macro_f1  \\\n",
              "model                                                                       \n",
              "Majority baseline                0.2373             0.3333         0.2773   \n",
              "\n",
              "                   test_weighted_f1  test_macro_roc_auc_ovr  focus_precision  \\\n",
              "model                                                                          \n",
              "Majority baseline            0.5922                  0.5000           0.0000   \n",
              "\n",
              "                   focus_recall  focus_f1  focus_average_precision  \\\n",
              "model                                                                \n",
              "Majority baseline        0.0000    0.0000                   0.1200   \n",
              "\n",
              "                   focus_brier_score  \n",
              "model                                 \n",
              "Majority baseline             0.1200  "
            ],
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              "\n",
              "  <div id=\"df-aeb0c1e5-2705-47ea-a7f6-80d539d00808\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train_macro_f1</th>\n",
              "      <th>test_accuracy</th>\n",
              "      <th>test_balanced_accuracy</th>\n",
              "      <th>test_macro_precision</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Majority baseline</th>\n",
              "      <td>0.2782</td>\n",
              "      <td>0.7120</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.2373</td>\n",
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              "\n",
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\"num_unique_values\": 1,\n        \"samples\": [\n          0.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    precision  recall  f1-score  support\n",
              "Repaid                 0.7120  1.0000    0.8318  89.0000\n",
              "Early Delinquency      0.0000  0.0000    0.0000  21.0000\n",
              "Severe Delinquency     0.0000  0.0000    0.0000  15.0000\n",
              "accuracy               0.7120  0.7120    0.7120   0.7120\n",
              "macro avg              0.2373  0.3333    0.2773 125.0000\n",
              "weighted avg           0.5069  0.7120    0.5922 125.0000"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Repaid</th>\n",
              "      <td>0.7120</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.8318</td>\n",
              "      <td>89.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Early Delinquency</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>21.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Severe Delinquency</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>15.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.7120</td>\n",
              "      <td>0.7120</td>\n",
              "      <td>0.7120</td>\n",
              "      <td>0.7120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.2373</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>125.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.5069</td>\n",
              "      <td>0.7120</td>\n",
              "      <td>0.5922</td>\n",
              "      <td>125.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b289e930-e40d-421e-8c01-1772f460c731')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b289e930-e40d-421e-8c01-1772f460c731 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b289e930-e40d-421e-8c01-1772f460c731');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(dummy_evaluation[\\\"report\\\"])\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3297381479919865,\n        \"min\": 0.0,\n        \"max\": 0.712,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.506944,\n          0.712\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4142103956346966,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3621493057242,\n        \"min\": 0.0,\n        \"max\": 0.8317757009345794,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          0.5922242990654205,\n          0.712\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57.11553633352896,\n        \"min\": 0.712,\n        \"max\": 125.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21.0,\n          125.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Majority baseline: demonstrates why accuracy alone is misleading.\n",
        "dummy_preprocessor, _, _ = make_preprocessor(\n",
        "    X_train,\n",
        "    scale_numeric=False,\n",
        ")\n",
        "\n",
        "dummy_pipeline = Pipeline([\n",
        "    (\"preprocessor\", dummy_preprocessor),\n",
        "    (\n",
        "        \"model\",\n",
        "        DummyClassifier(\n",
        "            strategy=\"most_frequent\",\n",
        "            random_state=RANDOM_STATE,\n",
        "        ),\n",
        "    ),\n",
        "])\n",
        "\n",
        "dummy_pipeline.fit(X_train, y_train)\n",
        "\n",
        "dummy_evaluation = evaluate_fitted_model(\n",
        "    \"Majority baseline\",\n",
        "    dummy_pipeline,\n",
        "    X_train,\n",
        "    y_train,\n",
        "    X_test,\n",
        "    y_test,\n",
        "    PRIMARY_CLASS_ORDER,\n",
        "    PRIMARY_FOCUS_CLASS,\n",
        ")\n",
        "\n",
        "display(\n",
        "    pd.DataFrame([dummy_evaluation[\"summary\"]])\n",
        "    .set_index(\"model\")\n",
        ")\n",
        "display(dummy_evaluation[\"report\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "05bdaae5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "05bdaae5",
        "outputId": "63064714-92de-46d9-8765-eb362c270eeb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    balanced_weight  moderate_sqrt_weight\n",
              "class                                                    \n",
              "Repaid                       0.4655                0.6823\n",
              "Early Delinquency            1.9841                1.4086\n",
              "Severe Delinquency           2.8736                1.6952"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-078e5297-21e7-4fab-bcf8-fa268b48bc52\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>balanced_weight</th>\n",
              "      <th>moderate_sqrt_weight</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Repaid</th>\n",
              "      <td>0.4655</td>\n",
              "      <td>0.6823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Early Delinquency</th>\n",
              "      <td>1.9841</td>\n",
              "      <td>1.4086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Severe Delinquency</th>\n",
              "      <td>2.8736</td>\n",
              "      <td>1.6952</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-078e5297-21e7-4fab-bcf8-fa268b48bc52')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-078e5297-21e7-4fab-bcf8-fa268b48bc52 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-078e5297-21e7-4fab-bcf8-fa268b48bc52');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \")\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Repaid\",\n          \"Early Delinquency\",\n          \"Severe Delinquency\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balanced_weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2176278611811524,\n        \"min\": 0.4655493482309125,\n        \"max\": 2.8735632183908044,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.4655493482309125,\n          1.9841269841269842,\n          2.8735632183908044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"moderate_sqrt_weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5220889333416086,\n        \"min\": 0.682311767618669,\n        \"max\": 1.6951587590520258,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.682311767618669,\n          1.4085904245475276,\n          1.6951587590520258\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Search uses one stratified five-fold partition; final stability checks use repeated CV.\n",
        "search_cv = StratifiedKFold(\n",
        "    n_splits=CV_SPLITS,\n",
        "    shuffle=True,\n",
        "    random_state=RANDOM_STATE,\n",
        ")\n",
        "\n",
        "repeated_cv = RepeatedStratifiedKFold(\n",
        "    n_splits=CV_SPLITS,\n",
        "    n_repeats=CV_REPEATS,\n",
        "    random_state=RANDOM_STATE,\n",
        ")\n",
        "\n",
        "# A non-repeated partition is required for unique out-of-fold probabilities.\n",
        "threshold_cv = StratifiedKFold(\n",
        "    n_splits=CV_SPLITS,\n",
        "    shuffle=True,\n",
        "    random_state=RANDOM_STATE,\n",
        ")\n",
        "\n",
        "MULTI_SCORING = {\n",
        "    \"macro_f1\": \"f1_macro\",\n",
        "    \"balanced_accuracy\": \"balanced_accuracy\",\n",
        "    \"accuracy\": \"accuracy\",\n",
        "    \"macro_roc_auc\": \"roc_auc_ovr\",\n",
        "}\n",
        "\n",
        "weight_classes = np.array(PRIMARY_CLASS_ORDER)\n",
        "balanced_weight_values = compute_class_weight(\n",
        "    class_weight=\"balanced\",\n",
        "    classes=weight_classes,\n",
        "    y=y_train,\n",
        ")\n",
        "\n",
        "balanced_weight_dict = dict(\n",
        "    zip(weight_classes, balanced_weight_values)\n",
        ")\n",
        "sqrt_weight_dict = {\n",
        "    class_name: float(np.sqrt(weight))\n",
        "    for class_name, weight in balanced_weight_dict.items()\n",
        "}\n",
        "\n",
        "display(\n",
        "    pd.DataFrame({\n",
        "        \"class\": weight_classes,\n",
        "        \"balanced_weight\": balanced_weight_values,\n",
        "        \"moderate_sqrt_weight\": [\n",
        "            sqrt_weight_dict[class_name]\n",
        "            for class_name in weight_classes\n",
        "        ],\n",
        "    }).set_index(\"class\")\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "57f81185",
      "metadata": {
        "id": "57f81185"
      },
      "source": [
        "Macro F1 is the primary refit criterion because it gives equal importance to each class. Balanced accuracy, per-class recall and ROC-AUC are retained as complementary evidence. Class weighting is treated as a tunable modelling choice rather than assumed to be beneficial."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0f9c5f2",
      "metadata": {
        "id": "d0f9c5f2"
      },
      "source": [
        "## 10. Multinomial Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "0f10770f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "id": "0f10770f",
        "outputId": "b8e45c49-bcb7-4107-92cf-dc3cc9dd0375"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
            "Logistic Regression search time: 8.5 seconds\n",
            "Best parameters:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'model__C': 0.1,\n",
              " 'model__class_weight': {np.str_('Repaid'): 0.682311767618669,\n",
              "  np.str_('Early Delinquency'): 1.4085904245475276,\n",
              "  np.str_('Severe Delinquency'): 1.6951587590520258}}"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best search-CV macro F1: 0.3684485303397444\n"
          ]
        }
      ],
      "source": [
        "logistic_pipeline = Pipeline([\n",
        "    (\"preprocessor\", logistic_preprocessor),\n",
        "    (\n",
        "        \"model\",\n",
        "        LogisticRegression(\n",
        "            solver=\"lbfgs\",\n",
        "            penalty=\"l2\",\n",
        "            tol=1e-3,\n",
        "            max_iter=2000,\n",
        "            random_state=RANDOM_STATE,\n",
        "        ),\n",
        "    ),\n",
        "])\n",
        "\n",
        "logistic_parameter_grid = {\n",
        "    \"model__C\": [0.01, 0.1, 1.0],\n",
        "    \"model__class_weight\": [\n",
        "        None,\n",
        "        sqrt_weight_dict,\n",
        "        \"balanced\",\n",
        "    ],\n",
        "}\n",
        "\n",
        "logistic_search = GridSearchCV(\n",
        "    estimator=logistic_pipeline,\n",
        "    param_grid=logistic_parameter_grid,\n",
        "    scoring=MULTI_SCORING,\n",
        "    refit=\"macro_f1\",\n",
        "    cv=search_cv,\n",
        "    n_jobs=N_JOBS,\n",
        "    return_train_score=True,\n",
        "    verbose=1,\n",
        ")\n",
        "\n",
        "start_time = time.time()\n",
        "logistic_search.fit(X_train, y_train)\n",
        "logistic_runtime = time.time() - start_time\n",
        "\n",
        "print(f\"Logistic Regression search time: {logistic_runtime:,.1f} seconds\")\n",
        "print(\"Best parameters:\")\n",
        "display(logistic_search.best_params_)\n",
        "print(\"Best search-CV macro F1:\", logistic_search.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "5c444b14",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "5c444b14",
        "outputId": "7bcab93e-dfbf-45dd-bebc-ff4325397a57"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   rank_test_macro_f1  mean_train_macro_f1  mean_test_macro_f1  \\\n",
              "0                   1               0.6047              0.3684   \n",
              "1                   2               0.5116              0.3632   \n",
              "2                   3               0.5979              0.3600   \n",
              "3                   4               0.6661              0.3555   \n",
              "4                   5               0.6496              0.3504   \n",
              "5                   6               0.4729              0.3399   \n",
              "6                   7               0.6194              0.3348   \n",
              "7                   8               0.4772              0.3259   \n",
              "8                   9               0.3153              0.2882   \n",
              "\n",
              "   std_test_macro_f1  mean_test_balanced_accuracy  mean_test_accuracy  \\\n",
              "0             0.0378                       0.3699              0.5980   \n",
              "1             0.0318                       0.4013              0.4700   \n",
              "2             0.0252                       0.3880              0.4760   \n",
              "3             0.0310                       0.3587              0.5600   \n",
              "4             0.0341                       0.3735              0.4700   \n",
              "5             0.0419                       0.3488              0.6560   \n",
              "6             0.0292                       0.3383              0.6200   \n",
              "7             0.0309                       0.3443              0.6720   \n",
              "8             0.0151                       0.3337              0.7040   \n",
              "\n",
              "   mean_test_macro_roc_auc                                             params  \n",
              "0                   0.5685  {'model__C': 0.1, 'model__class_weight': {'Rep...  \n",
              "1                   0.5694  {'model__C': 0.01, 'model__class_weight': 'bal...  \n",
              "2                   0.5625  {'model__C': 0.1, 'model__class_weight': 'bala...  \n",
              "3                   0.5407  {'model__C': 1.0, 'model__class_weight': {'Rep...  \n",
              "4                   0.5356  {'model__C': 1.0, 'model__class_weight': 'bala...  \n",
              "5                   0.5787  {'model__C': 0.01, 'model__class_weight': {'Re...  \n",
              "6                   0.5482     {'model__C': 1.0, 'model__class_weight': None}  \n",
              "7                   0.5725     {'model__C': 0.1, 'model__class_weight': None}  \n",
              "8                   0.5851    {'model__C': 0.01, 'model__class_weight': None}  "
            ],
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              "\n",
              "  <div id=\"df-2052cdef-8334-4eab-bc1b-30c6f3439d18\" class=\"colab-df-container\">\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank_test_macro_f1</th>\n",
              "      <th>mean_train_macro_f1</th>\n",
              "      <th>mean_test_macro_f1</th>\n",
              "      <th>std_test_macro_f1</th>\n",
              "      <th>mean_test_balanced_accuracy</th>\n",
              "      <th>mean_test_accuracy</th>\n",
              "      <th>mean_test_macro_roc_auc</th>\n",
              "      <th>params</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.6047</td>\n",
              "      <td>0.3684</td>\n",
              "      <td>0.0378</td>\n",
              "      <td>0.3699</td>\n",
              "      <td>0.5980</td>\n",
              "      <td>0.5685</td>\n",
              "      <td>{'model__C': 0.1, 'model__class_weight': {'Rep...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.5116</td>\n",
              "      <td>0.3632</td>\n",
              "      <td>0.0318</td>\n",
              "      <td>0.4013</td>\n",
              "      <td>0.4700</td>\n",
              "      <td>0.5694</td>\n",
              "      <td>{'model__C': 0.01, 'model__class_weight': 'bal...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.3600</td>\n",
              "      <td>0.0252</td>\n",
              "      <td>0.3880</td>\n",
              "      <td>0.4760</td>\n",
              "      <td>0.5625</td>\n",
              "      <td>{'model__C': 0.1, 'model__class_weight': 'bala...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.6661</td>\n",
              "      <td>0.3555</td>\n",
              "      <td>0.0310</td>\n",
              "      <td>0.3587</td>\n",
              "      <td>0.5600</td>\n",
              "      <td>0.5407</td>\n",
              "      <td>{'model__C': 1.0, 'model__class_weight': {'Rep...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.6496</td>\n",
              "      <td>0.3504</td>\n",
              "      <td>0.0341</td>\n",
              "      <td>0.3735</td>\n",
              "      <td>0.4700</td>\n",
              "      <td>0.5356</td>\n",
              "      <td>{'model__C': 1.0, 'model__class_weight': 'bala...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.4729</td>\n",
              "      <td>0.3399</td>\n",
              "      <td>0.0419</td>\n",
              "      <td>0.3488</td>\n",
              "      <td>0.6560</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>{'model__C': 0.01, 'model__class_weight': {'Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.6194</td>\n",
              "      <td>0.3348</td>\n",
              "      <td>0.0292</td>\n",
              "      <td>0.3383</td>\n",
              "      <td>0.6200</td>\n",
              "      <td>0.5482</td>\n",
              "      <td>{'model__C': 1.0, 'model__class_weight': None}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.4772</td>\n",
              "      <td>0.3259</td>\n",
              "      <td>0.0309</td>\n",
              "      <td>0.3443</td>\n",
              "      <td>0.6720</td>\n",
              "      <td>0.5725</td>\n",
              "      <td>{'model__C': 0.1, 'model__class_weight': None}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.3153</td>\n",
              "      <td>0.2882</td>\n",
              "      <td>0.0151</td>\n",
              "      <td>0.3337</td>\n",
              "      <td>0.7040</td>\n",
              "      <td>0.5851</td>\n",
              "      <td>{'model__C': 0.01, 'model__class_weight': None}</td>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(search_results_table(logistic_search))\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"rank_test_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_train_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11245454754460067,\n        \"min\": 0.31534583923641074,\n        \"max\": 0.6661252398165438,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.47718761517494324,\n          0.5115807868673267,\n          0.47286701541657294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.024821779733389473,\n        \"min\": 0.2881914449409926,\n        \"max\": 0.3684485303397444,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.3258832739589369,\n          0.3631557687372542,\n          0.33986632591193133\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_test_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007620504959698627,\n        \"min\": 0.015076470402926444,\n        \"max\": 0.041893533621682806,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.03092750305330818,\n          0.03182444650088442,\n          0.041893533621682806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_balanced_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.023114526756889082,\n        \"min\": 0.3337168983399307,\n        \"max\": 0.40131956823133297,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.3442785351443181,\n          0.40131956823133297,\n          0.348775451281665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09148223871331529,\n        \"min\": 0.4699999999999999,\n        \"max\": 0.704,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.672,\n          0.47000000000000003,\n          0.656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_macro_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01717006750036671,\n        \"min\": 0.5355906682986358,\n        \"max\": 0.5851071480055348,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.5724636496736524,\n          0.5693546315183804,\n          0.5787432891712316\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def search_results_table(search_object, top_n=12):\n",
        "    columns = [\n",
        "        \"rank_test_macro_f1\",\n",
        "        \"mean_train_macro_f1\",\n",
        "        \"mean_test_macro_f1\",\n",
        "        \"std_test_macro_f1\",\n",
        "        \"mean_test_balanced_accuracy\",\n",
        "        \"mean_test_accuracy\",\n",
        "        \"mean_test_macro_roc_auc\",\n",
        "        \"params\",\n",
        "    ]\n",
        "\n",
        "    return (\n",
        "        pd.DataFrame(search_object.cv_results_)[columns]\n",
        "        .sort_values(\"rank_test_macro_f1\")\n",
        "        .head(top_n)\n",
        "        .reset_index(drop=True)\n",
        "    )\n",
        "\n",
        "\n",
        "display(search_results_table(logistic_search))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ecf483e",
      "metadata": {
        "id": "5ecf483e"
      },
      "source": [
        "`C` controls inverse regularisation strength: smaller values impose stronger shrinkage and can improve generalisation when the one-hot feature space is large. Class weighting changes the contribution of minority observations to the loss. The selected combination must therefore be interpreted as an empirical trade-off between stable coefficients, majority accuracy and minority detection."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1297637e",
      "metadata": {
        "id": "1297637e"
      },
      "source": [
        "## 11. Random Forest ensemble"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "95b3f6af",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "95b3f6af",
        "outputId": "77e33186-5a4a-4b30-8470-28af74486c4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n",
            "Random Forest search time: 85.4 seconds\n",
            "Best parameters:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'model__class_weight': 'balanced',\n",
              " 'model__max_depth': 8,\n",
              " 'model__max_features': 'sqrt',\n",
              " 'model__min_samples_leaf': 8,\n",
              " 'model__min_samples_split': 8,\n",
              " 'model__n_estimators': 321}"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best search-CV macro F1: 0.38820344092438763\n"
          ]
        }
      ],
      "source": [
        "forest_pipeline = Pipeline([\n",
        "    (\"preprocessor\", forest_preprocessor),\n",
        "    (\n",
        "        \"model\",\n",
        "        RandomForestClassifier(\n",
        "            random_state=RANDOM_STATE,\n",
        "            n_jobs=1,\n",
        "        ),\n",
        "    ),\n",
        "])\n",
        "\n",
        "forest_parameter_distributions = {\n",
        "    \"model__n_estimators\": randint(200, 601),\n",
        "    \"model__max_depth\": [None, 4, 6, 8, 12],\n",
        "    \"model__min_samples_split\": randint(2, 21),\n",
        "    \"model__min_samples_leaf\": randint(1, 11),\n",
        "    \"model__max_features\": [\"sqrt\", \"log2\", 0.5],\n",
        "    \"model__class_weight\": [\n",
        "        None,\n",
        "        sqrt_weight_dict,\n",
        "        \"balanced\",\n",
        "        \"balanced_subsample\",\n",
        "    ],\n",
        "}\n",
        "\n",
        "forest_search = RandomizedSearchCV(\n",
        "    estimator=forest_pipeline,\n",
        "    param_distributions=forest_parameter_distributions,\n",
        "    n_iter=RF_SEARCH_ITERATIONS,\n",
        "    scoring=MULTI_SCORING,\n",
        "    refit=\"macro_f1\",\n",
        "    cv=search_cv,\n",
        "    random_state=RANDOM_STATE,\n",
        "    n_jobs=N_JOBS,\n",
        "    return_train_score=True,\n",
        "    verbose=1,\n",
        ")\n",
        "\n",
        "start_time = time.time()\n",
        "forest_search.fit(X_train, y_train)\n",
        "forest_runtime = time.time() - start_time\n",
        "\n",
        "print(f\"Random Forest search time: {forest_runtime:,.1f} seconds\")\n",
        "print(\"Best parameters:\")\n",
        "display(forest_search.best_params_)\n",
        "print(\"Best search-CV macro F1:\", forest_search.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "cdeeeb1c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "cdeeeb1c",
        "outputId": "37933a76-9ce2-41ab-b53b-334c5998e413"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   rank_test_macro_f1  mean_train_macro_f1  mean_test_macro_f1  \\\n",
              "0                   1               0.9038              0.3882   \n",
              "1                   2               0.9188              0.3763   \n",
              "2                   3               0.9208              0.3730   \n",
              "3                   4               0.8290              0.3724   \n",
              "4                   5               0.9193              0.3687   \n",
              "5                   6               0.9703              0.3534   \n",
              "6                   7               0.9267              0.3074   \n",
              "7                   8               0.3849              0.2894   \n",
              "\n",
              "   std_test_macro_f1  mean_test_balanced_accuracy  mean_test_accuracy  \\\n",
              "0             0.0217                       0.3931              0.6440   \n",
              "1             0.0220                       0.3872              0.6540   \n",
              "2             0.0345                       0.3767              0.6380   \n",
              "3             0.0395                       0.3796              0.6020   \n",
              "4             0.0271                       0.3760              0.6300   \n",
              "5             0.0462                       0.3690              0.6640   \n",
              "6             0.0252                       0.3454              0.7100   \n",
              "7             0.0202                       0.3389              0.7180   \n",
              "\n",
              "   mean_test_macro_roc_auc                                             params  \n",
              "0                   0.5835  {'model__class_weight': 'balanced', 'model__ma...  \n",
              "1                   0.5845  {'model__class_weight': 'balanced_subsample', ...  \n",
              "2                   0.5801  {'model__class_weight': 'balanced', 'model__ma...  \n",
              "3                   0.5717  {'model__class_weight': 'balanced_subsample', ...  \n",
              "4                   0.5746  {'model__class_weight': 'balanced', 'model__ma...  \n",
              "5                   0.5822  {'model__class_weight': 'balanced_subsample', ...  \n",
              "6                   0.6038  {'model__class_weight': {'Repaid': 0.682311767...  \n",
              "7                   0.6014  {'model__class_weight': None, 'model__max_dept...  "
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              "      <th>params</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.3882</td>\n",
              "      <td>0.0217</td>\n",
              "      <td>0.3931</td>\n",
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              "      <td>{'model__class_weight': 'balanced', 'model__ma...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.9188</td>\n",
              "      <td>0.3763</td>\n",
              "      <td>0.0220</td>\n",
              "      <td>0.3872</td>\n",
              "      <td>0.6540</td>\n",
              "      <td>0.5845</td>\n",
              "      <td>{'model__class_weight': 'balanced_subsample', ...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.9208</td>\n",
              "      <td>0.3730</td>\n",
              "      <td>0.0345</td>\n",
              "      <td>0.3767</td>\n",
              "      <td>0.6380</td>\n",
              "      <td>0.5801</td>\n",
              "      <td>{'model__class_weight': 'balanced', 'model__ma...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.8290</td>\n",
              "      <td>0.3724</td>\n",
              "      <td>0.0395</td>\n",
              "      <td>0.3796</td>\n",
              "      <td>0.6020</td>\n",
              "      <td>0.5717</td>\n",
              "      <td>{'model__class_weight': 'balanced_subsample', ...</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.3687</td>\n",
              "      <td>0.0271</td>\n",
              "      <td>0.3760</td>\n",
              "      <td>0.6300</td>\n",
              "      <td>0.5746</td>\n",
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              "      <td>0.3534</td>\n",
              "      <td>0.0462</td>\n",
              "      <td>0.3690</td>\n",
              "      <td>0.6640</td>\n",
              "      <td>0.5822</td>\n",
              "      <td>{'model__class_weight': 'balanced_subsample', ...</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.9267</td>\n",
              "      <td>0.3074</td>\n",
              "      <td>0.0252</td>\n",
              "      <td>0.3454</td>\n",
              "      <td>0.7100</td>\n",
              "      <td>0.6038</td>\n",
              "      <td>{'model__class_weight': {'Repaid': 0.682311767...</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.3849</td>\n",
              "      <td>0.2894</td>\n",
              "      <td>0.0202</td>\n",
              "      <td>0.3389</td>\n",
              "      <td>0.7180</td>\n",
              "      <td>0.6014</td>\n",
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              "summary": "{\n  \"name\": \"display(search_results_table(forest_search, top_n=16))\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"rank_test_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2,\n          6,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_train_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19065145339937775,\n        \"min\": 0.3849297079107009,\n        \"max\": 0.9702840682345991,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9188065184406152,\n          0.9702840682345991,\n          0.9038409450559055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03571391328898937,\n        \"min\": 0.2894018037599289,\n        \"max\": 0.38820344092438763,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.37625913831958,\n          0.3534269956525958,\n          0.38820344092438763\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_test_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009476982734837611,\n        \"min\": 0.020233348637459608,\n        \"max\": 0.046178119073375795,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.02203764438313007,\n          0.046178119073375795,\n          0.02173589253268029\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_balanced_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019175226916971305,\n        \"min\": 0.33888888888888885,\n        \"max\": 0.3931442985502638,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.3872182191983352,\n          0.36902419667539804,\n          0.3931442985502638\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03940630841448031,\n        \"min\": 0.602,\n        \"max\": 0.718,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.654,\n          0.664,\n          0.6439999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_macro_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011605946483485462,\n        \"min\": 0.5716674975880623,\n        \"max\": 0.6037856526403113,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5844574928243871,\n          0.5821753718086178,\n          0.5835393375675191\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {}
        }
      ],
      "source": [
        "display(search_results_table(forest_search, top_n=16))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f86f8603",
      "metadata": {
        "id": "f86f8603"
      },
      "source": [
        "Random Forest depth and minimum leaf size control the bias–variance trade-off. Deep trees with very small leaves can memorise a small outcome cohort, whereas larger leaves and restricted depth produce smoother estimates. `max_features` controls tree diversity, while class weighting changes the effective penalty assigned to minority-class errors. The search results should be read jointly with the training–validation gap rather than by validation rank alone."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f2e4449",
      "metadata": {
        "id": "3f2e4449"
      },
      "source": [
        "## 12. Validation comparison and one-time hold-out evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "43aea0e7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "43aea0e7",
        "outputId": "9785a149-2064-4fab-d461-e24148acafab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     cv_train_macro_f1  cv_macro_f1_mean  cv_macro_f1_std  \\\n",
              "model                                                                       \n",
              "Logistic Regression             0.6068            0.3575           0.0468   \n",
              "Random Forest                   0.9085            0.3830           0.0365   \n",
              "\n",
              "                     cv_balanced_accuracy  cv_accuracy  cv_macro_roc_auc  \\\n",
              "model                                                                      \n",
              "Logistic Regression                0.3591       0.6010            0.5632   \n",
              "Random Forest                      0.3897       0.6490            0.5859   \n",
              "\n",
              "                     search_runtime_seconds  \\\n",
              "model                                         \n",
              "Logistic Regression                  8.4768   \n",
              "Random Forest                       85.4388   \n",
              "\n",
              "                                                       best_parameters  \n",
              "model                                                                   \n",
              "Logistic Regression  {'model__C': 0.1, 'model__class_weight': {'Rep...  \n",
              "Random Forest        {'model__class_weight': 'balanced', 'model__ma...  "
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              "      <th>model</th>\n",
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              "      <th>Logistic Regression</th>\n",
              "      <td>0.6068</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "validation_summary",
              "summary": "{\n  \"name\": \"validation_summary\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Random Forest\",\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_train_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21335285480864952,\n        \"min\": 0.6067752323710437,\n        \"max\": 0.9085017332124536,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9085017332124536,\n          0.6067752323710437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_macro_f1_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01800909065310148,\n        \"min\": 0.3574944413327673,\n        \"max\": 0.38296314158038997,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.38296314158038997,\n          0.3574944413327673\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_macro_f1_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007302413180994737,\n        \"min\": 0.036517826385656774,\n        \"max\": 0.04684499814427159,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.036517826385656774,\n          0.04684499814427159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_balanced_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021654601498179032,\n        \"min\": 0.3591223614214501,\n        \"max\": 0.3897465925479596,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.3897465925479596,\n          0.3591223614214501\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.033941125496954314,\n        \"min\": 0.601,\n        \"max\": 0.649,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.649,\n          0.601\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_macro_roc_auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01603002168141726,\n        \"min\": 0.5632292765706177,\n        \"max\": 0.5858991506376128,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5858991506376128,\n          0.5632292765706177\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"search_runtime_seconds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.420376463324054,\n        \"min\": 8.476752996444702,\n        \"max\": 85.43878746032715,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          85.43878746032715,\n          8.476752996444702\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"best_parameters\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Repeated-CV-selected candidate: Random Forest\n"
          ]
        }
      ],
      "source": [
        "best_logistic = logistic_search.best_estimator_\n",
        "best_forest = forest_search.best_estimator_\n",
        "\n",
        "def repeated_validation_row(\n",
        "    estimator,\n",
        "    model_name,\n",
        "    runtime_seconds,\n",
        "    best_parameters,\n",
        "):\n",
        "    repeated_results = cross_validate(\n",
        "        estimator,\n",
        "        X_train,\n",
        "        y_train,\n",
        "        cv=repeated_cv,\n",
        "        scoring=MULTI_SCORING,\n",
        "        n_jobs=N_JOBS,\n",
        "        return_train_score=True,\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"model\": model_name,\n",
        "        \"cv_train_macro_f1\": repeated_results[\n",
        "            \"train_macro_f1\"\n",
        "        ].mean(),\n",
        "        \"cv_macro_f1_mean\": repeated_results[\n",
        "            \"test_macro_f1\"\n",
        "        ].mean(),\n",
        "        \"cv_macro_f1_std\": repeated_results[\n",
        "            \"test_macro_f1\"\n",
        "        ].std(),\n",
        "        \"cv_balanced_accuracy\": repeated_results[\n",
        "            \"test_balanced_accuracy\"\n",
        "        ].mean(),\n",
        "        \"cv_accuracy\": repeated_results[\n",
        "            \"test_accuracy\"\n",
        "        ].mean(),\n",
        "        \"cv_macro_roc_auc\": repeated_results[\n",
        "            \"test_macro_roc_auc\"\n",
        "        ].mean(),\n",
        "        \"search_runtime_seconds\": runtime_seconds,\n",
        "        \"best_parameters\": best_parameters,\n",
        "    }\n",
        "\n",
        "\n",
        "validation_summary = pd.DataFrame([\n",
        "    repeated_validation_row(\n",
        "        best_logistic,\n",
        "        \"Logistic Regression\",\n",
        "        logistic_runtime,\n",
        "        logistic_search.best_params_,\n",
        "    ),\n",
        "    repeated_validation_row(\n",
        "        best_forest,\n",
        "        \"Random Forest\",\n",
        "        forest_runtime,\n",
        "        forest_search.best_params_,\n",
        "    ),\n",
        "]).set_index(\"model\")\n",
        "\n",
        "display(validation_summary)\n",
        "\n",
        "champion_name = validation_summary[\"cv_macro_f1_mean\"].idxmax()\n",
        "champion = (\n",
        "    best_logistic\n",
        "    if champion_name == \"Logistic Regression\"\n",
        "    else best_forest\n",
        ")\n",
        "\n",
        "print(\"Repeated-CV-selected candidate:\", champion_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "5edfd215",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "5edfd215",
        "outputId": "c998aece-0a74-4d42-fd1e-2dc72a515006"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     train_macro_f1  test_accuracy  test_balanced_accuracy  \\\n",
              "model                                                                        \n",
              "Majority baseline            0.2782         0.7120                  0.3333   \n",
              "Logistic Regression          0.5675         0.6160                  0.3675   \n",
              "Random Forest                0.9114         0.6560                  0.4359   \n",
              "\n",
              "                     test_macro_precision  test_macro_recall  test_macro_f1  \\\n",
              "model                                                                         \n",
              "Majority baseline                  0.2373             0.3333         0.2773   \n",
              "Logistic Regression                0.3673             0.3675         0.3667   \n",
              "Random Forest                      0.4432             0.4359         0.4319   \n",
              "\n",
              "                     test_weighted_f1  test_macro_roc_auc_ovr  \\\n",
              "model                                                           \n",
              "Majority baseline              0.5922                  0.5000   \n",
              "Logistic Regression            0.6034                  0.6107   \n",
              "Random Forest                  0.6355                  0.6079   \n",
              "\n",
              "                     focus_precision  focus_recall  focus_f1  \\\n",
              "model                                                          \n",
              "Majority baseline             0.0000        0.0000    0.0000   \n",
              "Logistic Regression           0.0833        0.0667    0.0741   \n",
              "Random Forest                 0.2941        0.3333    0.3125   \n",
              "\n",
              "                     focus_average_precision  focus_brier_score  \n",
              "model                                                            \n",
              "Majority baseline                     0.1200             0.1200  \n",
              "Logistic Regression                   0.1649             0.1260  \n",
              "Random Forest                         0.1904             0.1212  "
            ],
            "text/html": [
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              "  <div id=\"df-0e969945-d4e9-4ef5-85ab-99816dc414fe\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>train_macro_f1</th>\n",
              "      <th>test_accuracy</th>\n",
              "      <th>test_balanced_accuracy</th>\n",
              "      <th>test_macro_precision</th>\n",
              "      <th>test_macro_recall</th>\n",
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              "      <th>test_weighted_f1</th>\n",
              "      <th>test_macro_roc_auc_ovr</th>\n",
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              "      <th>focus_recall</th>\n",
              "      <th>focus_f1</th>\n",
              "      <th>focus_average_precision</th>\n",
              "      <th>focus_brier_score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</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>Majority baseline</th>\n",
              "      <td>0.2782</td>\n",
              "      <td>0.7120</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.2373</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.2773</td>\n",
              "      <td>0.5922</td>\n",
              "      <td>0.5000</td>\n",
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              "      <td>0.1200</td>\n",
              "      <td>0.1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Logistic Regression</th>\n",
              "      <td>0.5675</td>\n",
              "      <td>0.6160</td>\n",
              "      <td>0.3675</td>\n",
              "      <td>0.3673</td>\n",
              "      <td>0.3675</td>\n",
              "      <td>0.3667</td>\n",
              "      <td>0.6034</td>\n",
              "      <td>0.6107</td>\n",
              "      <td>0.0833</td>\n",
              "      <td>0.0667</td>\n",
              "      <td>0.0741</td>\n",
              "      <td>0.1649</td>\n",
              "      <td>0.1260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest</th>\n",
              "      <td>0.9114</td>\n",
              "      <td>0.6560</td>\n",
              "      <td>0.4359</td>\n",
              "      <td>0.4432</td>\n",
              "      <td>0.4359</td>\n",
              "      <td>0.4319</td>\n",
              "      <td>0.6355</td>\n",
              "      <td>0.6079</td>\n",
              "      <td>0.2941</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.3125</td>\n",
              "      <td>0.1904</td>\n",
              "      <td>0.1212</td>\n",
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              "\n",
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0.12,\n        \"max\": 0.12602630703062895,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.12,\n          0.12602630703062895,\n          0.1211745279692344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model_evaluations = {\n",
        "    \"Majority baseline\": dummy_evaluation,\n",
        "    \"Logistic Regression\": evaluate_fitted_model(\n",
        "        \"Logistic Regression\",\n",
        "        best_logistic,\n",
        "        X_train,\n",
        "        y_train,\n",
        "        X_test,\n",
        "        y_test,\n",
        "        PRIMARY_CLASS_ORDER,\n",
        "        PRIMARY_FOCUS_CLASS,\n",
        "    ),\n",
        "    \"Random Forest\": evaluate_fitted_model(\n",
        "        \"Random Forest\",\n",
        "        best_forest,\n",
        "        X_train,\n",
        "        y_train,\n",
        "        X_test,\n",
        "        y_test,\n",
        "        PRIMARY_CLASS_ORDER,\n",
        "        PRIMARY_FOCUS_CLASS,\n",
        "    ),\n",
        "}\n",
        "\n",
        "test_summary = pd.DataFrame(\n",
        "    [\n",
        "        evaluation[\"summary\"]\n",
        "        for evaluation in model_evaluations.values()\n",
        "    ]\n",
        ").set_index(\"model\")\n",
        "\n",
        "display(test_summary)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "695f3907",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "695f3907",
        "outputId": "2084cd1a-0576-4903-98fd-965c75542f0f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Result-specific critical analysis\n\n**Logistic Regression.** Mean repeated-CV macro F1 was 0.357 ± 0.047; hold-out macro F1 was 0.367, balanced accuracy was 0.368, and Severe Delinquency recall was 0.067. The training–validation macro-F1 gap is 0.249, which indicates substantial overfitting. On the hold-out set it exceeded the majority baseline by 0.089 macro-F1 points.\n\n**Random Forest.** Mean repeated-CV macro F1 was 0.383 ± 0.037; hold-out macro F1 was 0.432, balanced accuracy was 0.436, and Severe Delinquency recall was 0.333. The training–validation macro-F1 gap is 0.526, which indicates substantial overfitting. On the hold-out set it exceeded the majority baseline by 0.155 macro-F1 points.\n\nModel selection is based on training-set cross-validation, whereas deployment suitability is judged separately from the untouched test set, minority-class errors, calibration and governance constraints."
          },
          "metadata": {}
        }
      ],
      "source": [
        "automated_model_commentary(\n",
        "    validation_summary,\n",
        "    test_summary,\n",
        "    baseline_name=\"Majority baseline\",\n",
        "    candidate_names=[\n",
        "        \"Logistic Regression\",\n",
        "        \"Random Forest\",\n",
        "    ],\n",
        "    focus_class=PRIMARY_FOCUS_CLASS,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "686ecb05",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "686ecb05",
        "outputId": "4cc1143a-9687-4954-bf75-4fbf76799024"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Logistic Regression: per-class hold-out performance\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    precision  recall  f1-score  support\n",
              "Repaid                 0.7553  0.7978    0.7760  89.0000\n",
              "Early Delinquency      0.2632  0.2381    0.2500  21.0000\n",
              "Severe Delinquency     0.0833  0.0667    0.0741  15.0000\n",
              "accuracy               0.6160  0.6160    0.6160   0.6160\n",
              "macro avg              0.3673  0.3675    0.3667 125.0000\n",
              "weighted avg           0.5920  0.6160    0.6034 125.0000"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Repaid</th>\n",
              "      <td>0.7553</td>\n",
              "      <td>0.7978</td>\n",
              "      <td>0.7760</td>\n",
              "      <td>89.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Early Delinquency</th>\n",
              "      <td>0.2632</td>\n",
              "      <td>0.2381</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>21.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Severe Delinquency</th>\n",
              "      <td>0.0833</td>\n",
              "      <td>0.0667</td>\n",
              "      <td>0.0741</td>\n",
              "      <td>15.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.6160</td>\n",
              "      <td>0.6160</td>\n",
              "      <td>0.6160</td>\n",
              "      <td>0.6160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.3673</td>\n",
              "      <td>0.3675</td>\n",
              "      <td>0.3667</td>\n",
              "      <td>125.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.5920</td>\n",
              "      <td>0.6160</td>\n",
              "      <td>0.6034</td>\n",
              "      <td>125.0000</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-40aabb64-e842-4079-944a-9f67a1df447a');\n",
              "        const dataTable =\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest: per-class hold-out performance\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    precision  recall  f1-score  support\n",
              "Repaid                 0.7629  0.8315    0.7957  89.0000\n",
              "Early Delinquency      0.2727  0.1429    0.1875  21.0000\n",
              "Severe Delinquency     0.2941  0.3333    0.3125  15.0000\n",
              "accuracy               0.6560  0.6560    0.6560   0.6560\n",
              "macro avg              0.4432  0.4359    0.4319 125.0000\n",
              "weighted avg           0.6243  0.6560    0.6355 125.0000"
            ],
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              "      <th>precision</th>\n",
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              "      <th>Repaid</th>\n",
              "      <td>0.7629</td>\n",
              "      <td>0.8315</td>\n",
              "      <td>0.7957</td>\n",
              "      <td>89.0000</td>\n",
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              "    <tr>\n",
              "      <th>Early Delinquency</th>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.1429</td>\n",
              "      <td>0.1875</td>\n",
              "      <td>21.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Severe Delinquency</th>\n",
              "      <td>0.2941</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.3125</td>\n",
              "      <td>15.0000</td>\n",
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              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.6560</td>\n",
              "      <td>0.6560</td>\n",
              "      <td>0.6560</td>\n",
              "      <td>0.6560</td>\n",
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              "      <th>macro avg</th>\n",
              "      <td>0.4432</td>\n",
              "      <td>0.4359</td>\n",
              "      <td>0.4319</td>\n",
              "      <td>125.0000</td>\n",
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              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.6243</td>\n",
              "      <td>0.6560</td>\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-595e8e9b-0e40-47f3-ab71-3ba3e1f21c7d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-595e8e9b-0e40-47f3-ab71-3ba3e1f21c7d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "<Figure size 640x480 with 2 Axes>"
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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HsFKlSjnWrVKlisqF4MGDByhbtqxKQARk/SF4dVu57Tv7j6Kjo6Pa8rxeZOvXr682UT17/1WqVMnxWrVq1bB///4cCd6v/85u374NIYTa3wcAaejAxcUF/v7+WLRoETZv3gwvLy907NgR/fr1U/njr0mvt/XBgweQy+WoWLGiSrmdnR0sLCxUzoeXl5f0R/T48eOoV68e6tWrh9KlS+P48eOwtbXFlStX0KdPH2md8PBwle2am5ur/MHI63vr9fOhr6+PChUq5Hi/ODg45JqgXJj30rVr1zBlyhQcPnw4R3CQnf+WV/PmzYOPjw8cHR3h4eGBjz/+GP3790eFChUAQApQfXx8ct1GfHw80tLS8OLFi1w/d2/LhcnOs3zTXa8F+Ty86Zxq4o+0gYEB5s6di7Fjx8LW1hYNGjTAp59+iv79+8POzu6Nx1KpUiWV4Dz7OLJff9Xb3ptvk5/32+vb3L17N2bNmoXLly+r5Dbmd34pddfz3PTq1QubNm3Cnj17MHToULRq1Spf+3ofMaiiQns1WOjcuTOaNGmCPn36IDQ0FKampgCyEqgHDBiAzp07Y/z48ShTpgx0dHQQGBioktCeTUdHR+2+RC5j9JqU276Lsk2vezVIALJ6/2QyGf7++2+17cw+DwCwcOFCDBgwAH/++ScOHDiAUaNGITAwEGfOnMmRj6aNtmbLy8W6SZMm+Pnnn3Hv3j0cP34cXl5ekMlkaNKkCY4fP46yZctCqVRK3+gBwN7eXmUba9euVUm01/R5zO343rSvt7UhLi4OzZo1g5mZGWbMmAFXV1cYGhri4sWLmDhxIpRKZb7a2KNHD3h5eeGPP/7AgQMHMH/+fMydOxc7duxA+/btpe3Nnz8/R35jNlNTU5U/tu+Tgp7T3N6D6m4GGDNmDDp06ICdO3di//79mDp1KgIDA3H48GHUqVMn/41Wo7Dvzfy8317d5vHjx9GxY0c0bdoUy5cvh729PfT09LB27Vps2bIlT/vO9qbPw+tiYmJw4cIFAMD169ehVCpzBKDFDYMq0qjsQKlFixZYunSpNDfJ9u3bUaFCBezYsUPlQhYQEFCg/Tg5OQF4+Q37VaGhoTnqHjp0CImJiSq9VdlDKNnbKirZ+3+93UBWG62trd86DYGrqyuEEHBxcUHlypXfus+aNWuiZs2amDJlCk6dOoXGjRtjxYoVmDVrFoD8fzvNDycnJyiVSty+fVv6xg5k3cUXFxencj6yg6WDBw/i/Pnz0vupadOm+PHHH1G2bFmYmJjAw8NDWid7WDVb9erV890+IOt8ZPfkAFl3sIaFhcHb2ztf2yuIo0ePIiYmBjt27FBJwA8LCyvwNu3t7TF8+HAMHz4ckZGRqFu3LmbPno327dtLycZmZmZvPD4bGxsYGRnl6XOnTvZ+rl69mut+NPF5yKvsnqDX7+p8vQcpm6urK8aOHYuxY8fi9u3bcHd3x8KFC7Fp0ya19Z2cnPDvv//mCBbel2tPtt9//x2GhobYv3+/yijD2rVrc9TV5LVhxIgRSExMRGBgICZNmoSgoKAcaQHFTfEOCem91Lx5c9SvXx9BQUFITU0F8PKb0qvfjs6ePYvTp08XaB/29vZwd3fH+vXrVYZCDh48iOvXr6vU/fjjj6FQKLB06VKV8sWLF0Mmk6F9+/YFaoOmvHosr17cr169igMHDuTpTpiuXbtCR0cH06dPz/GtVgiBmJgYAFl3YWZmZqq8XrNmTcjlcpVeCBMTkxx/aLLldUqF3GQfT1BQkEr5okWLAEC60wjIGkrInrAxIyMDjRs3BpAVbN29exfbt29HgwYNVPKTvL29VZbXe67extvbG/r6+vjhhx9UfperV69GfHy8Svu0Rd3nJT09HcuXL8/3thQKRY7hwjJlyqBs2bLSOffw8ICrqysWLFiApKSkHNvInnJCR0cHbdu2xc6dO1XeAzdu3MD+/fvf2pa6devCxcUFQUFBOd5f2ceqic9DXjk5OUFHR0fK28v2+u85JSVFupZlc3V1RalSpd7Ye/fxxx8jPDxc5c7SzMxMLFmyBKampmjWrJkGjqLwdHR0IJPJVHro7t+/r3bm9DddG/Jj+/bt2LZtG7777jt8/fXX6NWrF6ZMmYJbt24VettFiT1VpBXjx49H9+7dsW7dOnz55Zf49NNPsWPHDnTp0gWffPIJwsLCsGLFCri5uam9iOdFYGAgPvnkEzRp0gQDBw7E8+fPsWTJElSvXl1lmx06dECLFi0wefJk3L9/H7Vr18aBAwfw559/YsyYMTluCS4K8+fPR/v27dGwYUMMGjRIuoXc3Nw8T89wc3V1xaxZszBp0iTcv38fnTt3RqlSpRAWFoY//vgDQ4cOxbhx43D48GH4+fmhe/fuqFy5MjIzM7Fx40bo6OigW7du0vY8PDxw6NAhLFq0CGXLloWLi4uUZJ3fKRVeV7t2bfj4+OCnn36ShrnOnTuH9evXo3PnzmjRooVKfS8vL2zduhU1a9aUehbq1q0LExMT3Lp1SyWfShNsbGwwadIkTJ8+He3atUPHjh0RGhqK5cuX46OPPkK/fv00uj91GjVqBEtLS/j4+GDUqFGQyWTYuHFjgYYoExMTUa5cOXz22WeoXbs2TE1NcejQIZw/fx4LFy4EAMjlcqxatQrt27dH9erV4evrCwcHBzx58gRHjhyBmZkZ/vrrLwDA9OnTsW/fPnh5eWH48OFSkFC9enX8+++/b2yLXC7Hjz/+iA4dOsDd3R2+vr6wt7fHzZs3ce3aNSkwK+znIa/Mzc3RvXt3LFmyBDKZDK6urti9ezciIyNV6t26dQutWrVCjx494ObmBl1dXfzxxx+IiIhAr169ct3+0KFDsXLlSgwYMAAhISFwdnbG9u3bcfLkSQQFBeXI8ywqn3zyCRYtWoR27dqhT58+iIyMxLJly1CxYsUc5/RN14a8ioyMxLBhw9CiRQv4+fkBAJYuXYojR45gwIABOHHiRPEdBnzHdxvSByT7Nt7z58/neE2hUAhXV1fh6uoqMjMzhVKpFHPmzBFOTk7CwMBA1KlTR+zevTvH7eDZtxaru3UZr90mLoQQv//+u6hWrZowMDAQbm5uYseOHWpvMU9MTBRfffWVKFu2rNDT0xOVKlUS8+fPz3EbPoAct57n1qbs27HfNj3Bm35Przp06JBo3LixMDIyEmZmZqJDhw7i+vXrKnWyb02OiopSu43ff/9dNGnSRJiYmAgTExNRtWpVMWLECBEaGiqEEOLevXti4MCBwtXVVRgaGorSpUuLFi1aiEOHDqls5+bNm6Jp06bCyMhIAFC5hRr5nFJBXVszMjLE9OnThYuLi9DT0xOOjo5i0qRJKlMFZFu2bJkAIIYNG6ZS7u3tLQCI4ODgt7ZFiNzPQ2631S9dulRUrVpV6OnpCVtbWzFs2LAc0wCou01fiPy/Z9S17eTJk6JBgwbCyMhIlC1bVkyYMEHs378/R1vfNqVCWlqaGD9+vKhdu7YoVaqUMDExEbVr15amqnjVpUuXRNeuXYWVlZUwMDAQTk5OokePHjl+x//884/w8PAQ+vr6okKFCmLFihVqb5vPzYkTJ0Tr1q2l9tSqVUssWbJEpU5hPg/Zv89XpzvJ7VxFRUWJbt26CWNjY2FpaSm++OILcfXqVZUpFaKjo8WIESNE1apVhYmJiTA3Nxeenp7i119/VdnW61MqCCFERESE8PX1FdbW1kJfX1/UrFlT2m62/F73Xpfbezu334+Pj48wMTFRKVu9erWoVKmSMDAwEFWrVhVr165Ve05zuza86fP++na6du0qSpUqJe7fv69SL3sKiblz577xeN9nMiGKIMuWiIiI6ANTTPvXiIiIiN4vDKqIiIiINIBBFREREZEGMKgiIiIi0gAGVUREREQawKCKiIiISAM4+ScVe0qlEk+fPkWpUqW0+ngVIqIPlRACiYmJKFu2rFYm3kxNTUV6enqhtqGvrw9DQ0MNtUg7GFRRsff06dMcT2EnIqL8e/TokcYfrJ6amgoXJ1OER+Z8UHV+2NnZISws7L0OrBhUUbGX/aiHBxedYWbKEe3iqmt1j7dXoveWTE+nqJtAhZApMnDsxe9aeXROeno6wiMVeBDiDLNSBbtGJyQq4eRxH+np6QyqiLQpe8jPzFRe4A8sFT1dmV5RN4EKQSbjn5MPgTZTKExLyWBaqmDbV6J4pHbwU0BERERapxBKKAr4YDyFUGq2MVrCoIqIiIi0TgkBJQoWVRV0vXeNYyVEREREGsCeKiIiItI6JZQo6CBewdd8txhUERERkdYphIBCFGwYr6DrvWsMqoiIiEjrmFNFRERERHnCoIqIiIi0TgkBRQGXgvZULVu2DM7OzjA0NISnpyfOnTv3xvpBQUGoUqUKjIyM4OjoiK+++gqpqal53h+DKiIiItK67OG/gi75tW3bNvj7+yMgIAAXL15E7dq10bZtW0RGRqqtv2XLFnz99dcICAjAjRs3sHr1amzbtg3ffPNNnvfJoIqIiIi0LjtRvaBLfi1atAhDhgyBr68v3NzcsGLFChgbG2PNmjVq6586dQqNGzdGnz594OzsjDZt2qB3795v7d16FYMqIiIiKhYSEhJUlrS0NLX10tPTERISAm9vb6lMLpfD29sbp0+fVrtOo0aNEBISIgVR9+7dw969e/Hxxx/nuX28+4+IiIi0Tvn/paDrAoCjo6NKeUBAAL799tsc9aOjo6FQKGBra6tSbmtri5s3b6rdR58+fRAdHY0mTZpACIHMzEx8+eWX+Rr+Y1BFREREWpeddF7QdQHg0aNHMDMzk8oNDAw00jYAOHr0KObMmYPly5fD09MTd+7cwejRozFz5kxMnTo1T9tgUEVERERapxAoxAOVs/41MzNTCapyY21tDR0dHURERKiUR0REwM7OTu06U6dOxeeff47BgwcDAGrWrInk5GQMHToUkydPhlz+9owp5lQRERGR1ikLueSHvr4+PDw8EBwc/HL/SiWCg4PRsGFDteukpKTkCJx0dHQAACKPifLsqSIiIqIPjr+/P3x8fFCvXj3Ur18fQUFBSE5Ohq+vLwCgf//+cHBwQGBgIACgQ4cOWLRoEerUqSMN/02dOhUdOnSQgqu3YVBFREREWqeEDArICrxufvXs2RNRUVGYNm0awsPD4e7ujn379knJ6w8fPlTpmZoyZQpkMhmmTJmCJ0+ewMbGBh06dMDs2bPzvE+ZyGufFtF7KiEhAebm5oi9VQFmpTiiXVy1c6pf1E2gQpDp8Tt6cZYp0nE4ZSvi4+PzlLOUH9nX6AvXbGFawGt0UqIS9apHaKV9msRPAREREWmdohA9VQVd713j13oiIiIiDWBPFREREWldSeipYlBFREREWqcUMihFARPVC7jeu8agioiIiLSuJPRUMaeKiIiISAPYU0VERERap4AcigL25Sg03BZtYVBFREREWicKkVMlmFNFRERElIU5VURERESUJ+ypIiIiIq1TCDkUooA5VcXkgXoMqoiIiEjrlJBBWcABMiWKR1TFoIqIiIi0jjlVRERERJQn7KkiIiIirStcThWH/4iIiIgAZOdUFfDZf8Vk+I9BFREREWmdshAzqheXRHXmVBERERFpAHuqiIiISOuYU0VERESkAUrIOU8VERERUWEphAyKAj4YuaDrvWvMqSIiIiLSAPZUERERkdYpCnH3n4LDf0RERERZlEIOZQET1ZVMVCciIiLKUhJ6qphTRURERKQB7KkiIiIirVOi4HfxKTXbFK1hUEVERERaV7h5qorHwBqDKiIiItK6ws2ozqCKiIiICACghAxKFHT4r3hM/smgit6p5s2bw93dHUFBQbnWcXZ2xpgxYzBmzJh31q53bddaa2z/sQyeR+migtsLDJ/1BFXrpORaf8fPNtiz3gqRT/VhZpkJr0/jMHDSM+gbZt0R89d6K+zZYI2IR/oAAKcqqej7VTg+apn4To6nJOrQPwKfDQ2HpU0G7t0wxvKA8rh1xTTX+l4fP0f/sU9gWy4NT+4bYs135XD+iIX0uqGxAgO/foyGbWJhZpmJ8EcG+HOtLfZuLvMOjqbk+bRfOD4b/BSWNum4d8MEP85wxq1/S+Vav0n7GPQf81A6f2vnOeH8P5bS6xZW6Rg44SHqNomDiZkCV8+Xwo/TXfD0gdG7OBx6TxSP/jQqtAEDBkAmk0Emk0FPTw8uLi6YMGECUlNT32k7duzYgZkzZ77Tfb5vjv5pgZ+ml0Vf/3As2x+KCm4vMLlPBcRFq/+Oc3iHBdbMsUdf/3D8/M9N+C98hH92WWLtd/ZSHRv7DAz85imW7gvFkr9voXbjRHzr64L7oYbv6rBKlKafxmDIlEfY9H1Z+H1aHfduGGP2xlswt8pQW7+aRyK+XnIX+3+1xohPquP0AQtM++kOnCq/DKSHTn2Ees3iMX9MBQxtVRM7V9tixIwHaOAd+64Oq8Ro+nE0hn5zH5uXlMPITrUQdtMYs9begHnpXM5fnUR8vfgW9v9WBn4da+H0wdKY+mMonCplnz+BaStCYeeYihlfVoVfx1qIfGKAORuuw8BI8e4O7D2XPfxX0KU4KB6tJI1o164dnj17hnv37mHx4sVYuXIlAgIC3mkbSpcujVKlcv82WBLs+MkG7frEoG2v53CqnIZRcx/DwEiJ/b+UVlv/+gUTVP8oGS27xsHOMR0ezRPRvHMsQi8ZS3UatElA/VaJcKiQjnKuafD9OhyGJkrcDDFWu00qnK6DI7Bvqw0O/maDh7eNsOQbJ6S9kKNtj2i19Tv7RuDCP+bYvtIej+4YYcPCcrhz1RgdfSKlOm4eSTj0uzX+PWOGiMcG+PuXMrh3wxhV3JPf1WGVGF0GPsPf28rg4O9l8PCOMZZMrYC0F3K06R6ptn6nAc9w4ZgFfl/lgEd3jbExqDzuXjdBh8/DAQAOzqmoVicJSwMq4NZ/pngSZoSl0yrAwFCJ5h3UvydKoux5qgq6FAfFo5WkEQYGBrCzs4OjoyM6d+4Mb29vHDx4EACgVCoRGBgIFxcXGBkZoXbt2ti+fbu07tGjRyGTybBnzx7UqlULhoaGaNCgAa5evSrViYmJQe/eveHg4ABjY2PUrFkTv/zyi0obmjdvrjKsFxkZiQ4dOsDIyAguLi7YvHmzdn8JRSwjXYbb/xqjrleSVCaXA3W8knA9xETtOm71knH7X2Pc/H8Q9eyBPs4Hm+GjVglq6ysUwNGdFkhLkaNaPf5B1jRdPSUq1UzGpRNmUpkQMlw6YYZqdZPUrlOtrmp9AAg5Zq5S/3qIKRp4x8LKNh2AQK2GCXBwSUXIMXOtHEdJpaunRKUaSbh80kIqE0KGy6csUK2O+uHyanUScfmUhUpZyPGX9fX0s274z0h7+SdVCBky0uWo7sEh+GxKISvUUhDLli2Ds7MzDA0N4enpiXPnzuVat3nz5tKIzqvLJ598kuf9MaeqhLp69SpOnToFJycnAEBgYCA2bdqEFStWoFKlSjh27Bj69esHGxsbNGvWTFpv/Pjx+P7772FnZ4dvvvkGHTp0wK1bt6Cnp4fU1FR4eHhg4sSJMDMzw549e/D555/D1dUV9evXV9uOAQMG4OnTpzhy5Aj09PQwatQoREaq/7b4IUh4rgOlQgYLG9VhBkvrDDy6Y6B2nZZd45DwXBdjO1eEEDIoMmX4pH80eo9S/T2F3TDEmA6VkJ4mh5GJEtNWh8GpcprWjqWkMrPMhI4uEBetp1IeF60HR1f1w+mWNhlq61u+8j74MaA8RgXex+ZzV5CZIYNSCXz/tTOunivZPbualn3+YmNUz0dstB7KVXihdh1L6wzERuesn33+Ht0zQsQTfQwY9xBLplRA6gs5uvg+g419OkqXSdfOgdBbbdu2Df7+/lixYgU8PT0RFBSEtm3bIjQ0FGXK5MxV3LFjB9LTX56vmJgY1K5dG927d8/zPhlUlSC7d++GqakpMjMzkZaWBrlcjqVLlyItLQ1z5szBoUOH0LBhQwBAhQoVcOLECaxcuVIlqAoICEDr1q0BAOvXr0e5cuXwxx9/oEePHnBwcMC4ceOkuiNHjsT+/fvx66+/qg2qbt26hb///hvnzp3DRx99BABYvXo1qlWr9sbjSEtLQ1ray2AhIUF9j82H4sopU2xdYgu/OY9RtW4Knt43wI9THbB5sS36fhUh1SvnmoblB0ORkqiD47stsGC0E+bvuM3AqpjoOCAC1eokI2BgJUQ+0UcNz0SMmPkAzyP0cOkke6veZ4pMOWYNr4IxgXfx28XzUGQCl06Z4/xRCxSTm9beCWUhhvEKMk/VokWLMGTIEPj6+gIAVqxYgT179mDNmjX4+uuvc9QvXVo1BWPr1q0wNjZmUEXqtWjRAj/++COSk5OxePFi6Orqolu3brh27RpSUlKkYClbeno66tSpo1KWHXQBWW/AKlWq4MaNGwAAhUKBOXPm4Ndff8WTJ0+Qnp6OtLQ0GBurz+u5ceMGdHV14eHhIZVVrVoVFhYWbzyOwMBATJ8+PT+H/t4wK62AXEcgLkrdt95Mteusn2eHVt1i0b7vcwCAS7VUpKbI8f14R/QeHQH5/681evoCDi5Z37Iq1XqB0MvG2LnKBqPnPdbeAZVACbG6UGQCFtaqvY0W1hmIfe28ZouN0ntjfX0DJQaMf4KZX1TEucMWAICwm8ZwdUtBt6HhDKo0KPv8WVrl7C1+vTcqW2y0Hiyt1dR/5XzfuWYKv461YWyaCT19gfjneli8/T/cvqp+WL8kKtwDlbPWe/1LtIGBAQwMcvbyp6enIyQkBJMmTZLK5HI5vL29cfr06Tztc/Xq1ejVqxdMTPJ+DplTVYKYmJigYsWKqF27NtasWYOzZ89i9erVSErKyuvYs2cPLl++LC3Xr19Xyat6m/nz5+P777/HxIkTceTIEVy+fBlt27ZV6U7VhEmTJiE+Pl5aHj16pNHta5OevkClWim4dOLlrfdKJXD5hCncPNTnP6W9kEMmV32YqPz/P7/pwe1CABnp/IhrWmaGHLf/M4F745cXd5lMwL1xAm5cVD+lwo2LqvUBoK5XvFRfV09AT19A+dqzOJQKGWQ8hRqVmSHH7aumcG8UL5XJZALujeJx45L6odYbl0qp1AeAOo3j1NZPSdJF/HM9lHV6gUo1k3DmkPobUEoiBWSFWgDA0dER5ubm0hIYGKh2X9HR0VAoFLC1tVUpt7W1RXh4+Fvbeu7cOVy9ehWDBw/O1zGyp6qEksvl+Oabb+Dv749bt27BwMAADx8+VBnqU+fMmTMoX748ACA2Nha3bt2ShutOnjyJTp06oV+/fgCykt9v3boFNzc3tduqWrUqMjMzERISIg3/hYaGIi4u7o1tyO2bSXHRdWgUFowpj8q1U1ClTgr++NkGqSlytOmV1RM1b1R5WNtlYOA3zwAADVonYMdPNqhY4wWq1k3BkzB9rJ9vD8/W8dDRydrmmjn2+KhlAmwcMvAiSY4jf1ji31OmmL3lblEd5gdtxypbjFsYhtv/miD0igm6DIyAobESB36zBgCMW3QPMeF6WDvPEQCwc60t5m8LRdch4Th32BzNOzxHpZop+P5rZwBASpIO/j1dCoO/eYz0VDkinhiglmciWnWLxk8zyxfVYX6w/lhjj7Hz7+D2fyYI/dcUnQc8g4GRAge32wAAxs6/jZgIfaxbkJVz+uc6e8zbcg1dBz3FuSOWaPZpNCrVSMYPk12lbTZpH4P457qIemoA5yop+HLKfZw+WBoXT1gUxSF+sB49egQzs5c3fWjrb8Hq1atRs2bNXPOBc8OgqgTr3r07xo8fj5UrV2LcuHH46quvoFQq0aRJE8THx+PkyZMwMzODj4+PtM6MGTNgZWUFW1tbTJ48GdbW1ujcuTMAoFKlSti+fTtOnToFS0tLLFq0CBEREbkGVVWqVEG7du3wxRdf4Mcff4Suri7GjBkDI6MPe7K85p3iEB+jiw3z7REbpYsK1V9g9uZ70vBf1BN9aUgPAPqMCYdMJrBunj1iwvVgXjoTDVrHY8DXL79txUXrYv4oJzyP1IVxKQVcqqVi9pa78Gim/m40Kpxju61gbpWJz/2fZE3+ed0YU/pXlpLRy5RNh3il1+lGSCnMHVUBPuOeYMD4x3h63xAzhlbEg1svh8YDR7rCd8JjTPj+HkpZZCLysQHWzy+HPZts3vXhffCO7bWGuVUG+o15hNI2Gbh73QRTB1ZDXEzW5LlZ5+9lMtSNS6Uw178SfL56iAFjH+LJfUPMHFYFD26/PH+lbdIx9Jv7sLDKwPMoPQT/YYNflpV758f2PtPE8J+ZmZlKUJUba2tr6OjoICIiQqU8IiICdnZ2b1w3OTkZW7duxYwZM/LdTgZVJZiuri78/Pwwb948hIWFwcbGBoGBgbh37x4sLCxQt25dfPPNNyrrfPfddxg9ejRu374Nd3d3/PXXX9DXz7oQTZkyBffu3UPbtm1hbGyMoUOHonPnzoiPj1e3ewDA2rVrMXjwYDRr1gy2traYNWsWpk6dqtXjfh90GhiNTgPVz18z//c7Kj/r6AL9xkag39gItfUBwH9R8RkC/VD8td4Wf623VfvahF5Vc5Qd31sax/fmPhQUG6WHReNdNNY+erO/Ntrjr432al+b2Ld6jrITf1vhxN9WuW5v1wZ77NqgfnuURQFIw3gFWTc/9PX14eHhgeDgYOmLv1KpRHBwMPz8/N647m+//Ya0tDRp1CU/ZEK8KSuDKMvRo0fRokULxMbGvjWR/F1LSEiAubk5Ym9VgFkpJqAUV+2c8tfNTu8XmR6/oxdnmSIdh1O2Ij4+Pk89QfmRfY2ecqYNDE3V3wzwNqlJGZjV4EC+2rdt2zb4+Phg5cqVqF+/PoKCgvDrr7/i5s2bsLW1Rf/+/eHg4JAjL8vLywsODg7YunVrvtvJTwERERF9cHr27ImoqChMmzYN4eHhcHd3x759+6Tk9YcPH0IuV/0iHhoaihMnTuDAgQMF2ieDKiIiItK6wjzDr6Dr+fn55Trcd/To0RxlVapUQWEG8BhUUZ40b968UG80IiIq2QRkUBYwp0oUk1lUGVQRERGR1hVFT9W7VjxaSURERPSeY08VERERaZ1SyKAUBRvGK+h67xqDKiIiItI6RSEeqFzQ9d41BlVERESkdSWhp6p4hH5ERERE7zn2VBEREZHWKSGHsoB9OQVd711jUEVERERapxAyKAo4jFfQ9d41BlVERESkdcypIiIiIqI8YU8VERERaZ0QcigLODO6KCYzqjOoIiIiIq1TQAZFAZ/hV9D13jUGVURERKR1SlHw3Cil0HBjtIRBFREREWmdshDDfwVd710rHq0kIiIies+xp4qIiIi0TgkZlAXMjSroeu8agyoiIiLSOk7+SURERKQBzKkiIiIiojxhTxURERFpnRKFeEwNc6qIiIiIsohCJKoLBlVEREREWfhAZSIiIiLKE/ZUERERkdaVhLv/GFQRERGR1pWE4T8GVURERKR1JWFG9eLRn0ZERET0nmNPFREREWkdh/+IiIiINIBBFREREZEGlISgijlVRERERBrAnioiIiLSOvZUEREREWmAwMtpFfK7iALuc9myZXB2doahoSE8PT1x7ty5N9aPi4vDiBEjYG9vDwMDA1SuXBl79+7N8/7YU0VERERa9657qrZt2wZ/f3+sWLECnp6eCAoKQtu2bREaGooyZcrkqJ+eno7WrVujTJky2L59OxwcHPDgwQNYWFjkeZ8MqoiIiOiDs2jRIgwZMgS+vr4AgBUrVmDPnj1Ys2YNvv766xz116xZg+fPn+PUqVPQ09MDADg7O+drnxz+IyIiIq3L7qkq6JIf6enpCAkJgbe3t1Qml8vh7e2N06dPq11n165daNiwIUaMGAFbW1vUqFEDc+bMgUKhyPN+2VNFREREWqeJ4b+EhASVcgMDAxgYGOSoHx0dDYVCAVtbW5VyW1tb3Lx5U+0+7t27h8OHD6Nv377Yu3cv7ty5g+HDhyMjIwMBAQF5aid7qoiIiEjrNNFT5ejoCHNzc2kJDAzUXPuUSpQpUwY//fQTPDw80LNnT0yePBkrVqzI8zbYU0VERETFwqNHj2BmZib9rK6XCgCsra2ho6ODiIgIlfKIiAjY2dmpXcfe3h56enrQ0dGRyqpVq4bw8HCkp6dDX1//re1jTxURERFpnRCyQi0AYGZmprLkFlTp6+vDw8MDwcHBUplSqURwcDAaNmyodp3GjRvjzp07UCqVUtmtW7dgb2+fp4AKYFBFRERE70BB56jKXvLL398fP//8M9avX48bN25g2LBhSE5Olu4G7N+/PyZNmiTVHzZsGJ4/f47Ro0fj1q1b2LNnD+bMmYMRI0bkeZ8c/iMiIiKte9fzVPXs2RNRUVGYNm0awsPD4e7ujn379knJ6w8fPoRc/rJvydHREfv378dXX32FWrVqwcHBAaNHj8bEiRPzvE8GVURERPRB8vPzg5+fn9rXjh49mqOsYcOGOHPmTIH3x6CKiIiItO7V3KiCrFscMKgiIiIirSsJD1RmUEVERERax54qomLkm/DaMEjSK+pmUAHpOJYt6iZQIWRalyrqJlAhKDNTgRDt7kMUoqequARVnFKBiIiISAPYU0VERERaJwAIUfB1iwMGVURERKR1SsggK8AkntnrFgcMqoiIiEjrSkKiOnOqiIiIiDSAPVVERESkdUohg4zzVBEREREVjhCFSFQvJpnqDKqIiIhI65hTRURERER5wp4qIiIi0rqS0FPFoIqIiIi0jonqRERERBpQEhLVmVNFREREpAHsqSIiIiKty+qpKmhOlYYboyUMqoiIiEjrmKhOREREpAHi/0tB1y0OmFNFREREpAHsqSIiIiKt4/AfERERkSaUgPE/BlVERESkfYXoqUIx6aliThURERGRBrCnioiIiLSuJMyozqCKiIiItI6J6kRERESaIGQFz436kIKqXbt25XmDHTt2LHBjiIiIiIqrPAVVnTt3ztPGZDIZFApFYdpDREREHyDmVP2fUqnUdjuIiIjoQ8Z5qt4sNTUVhoaGmmoLERERfaBKQqJ6vuepUigUmDlzJhwcHGBqaop79+4BAKZOnYrVq1drvIFERET0gRAFXIqJfAdVs2fPxrp16zBv3jzo6+tL5TVq1MCqVas02jgiIiKiglq2bBmcnZ1haGgIT09PnDt3Lte669atg0wmU1nyOxqX76Bqw4YN+Omnn9C3b1/o6OhI5bVr18bNmzfzuzkiIiIqAbKH/wq65Ne2bdvg7++PgIAAXLx4EbVr10bbtm0RGRmZ6zpmZmZ49uyZtDx48CBf+8x3UPXkyRNUrFgxR7lSqURGRkZ+N0dEREQlQUGH/go4BLho0SIMGTIEvr6+cHNzw4oVK2BsbIw1a9bkuo5MJoOdnZ202Nra5muf+Q6q3NzccPz48Rzl27dvR506dfK7OSIiIioRZIVc8i49PR0hISHw9vaWyuRyOby9vXH69Olc10tKSoKTkxMcHR3RqVMnXLt2LV/7zffdf9OmTYOPjw+ePHkCpVKJHTt2IDQ0FBs2bMDu3bvzuzkiIiKiPElISFD52cDAAAYGBjnqRUdHQ6FQ5OhpsrW1zTVVqUqVKlizZg1q1aqF+Ph4LFiwAI0aNcK1a9dQrly5PLUv3z1VnTp1wl9//YVDhw7BxMQE06ZNw40bN/DXX3+hdevW+d0cERERlQQaGP5zdHSEubm5tAQGBmqseQ0bNkT//v3h7u6OZs2aYceOHbCxscHKlSvzvI0CzVPl5eWFgwcPFmRVIiIiKok0MPnno0ePYGZmJhWr66UCAGtra+jo6CAiIkKlPCIiAnZ2dnnapZ6eHurUqYM7d+7kuZn57qnKduHCBWzcuBEbN25ESEhIQTdDREREJUH2A5ULuiDr7rxXl9yCKn19fXh4eCA4OFgqUyqVCA4ORsOGDfPUXIVCgf/++w/29vZ5PsR891Q9fvwYvXv3xsmTJ2FhYQEAiIuLQ6NGjbB169Y8jzsSERERaYu/vz98fHxQr1491K9fH0FBQUhOToavry8AoH///nBwcJCGEGfMmIEGDRqgYsWKiIuLw/z58/HgwQMMHjw4z/vMd1A1ePBgZGRk4MaNG6hSpQoAIDQ0FL6+vhg8eDD27duX300SERHRB+5dP1C5Z8+eiIqKwrRp0xAeHg53d3fs27dPSl5/+PAh5PKXA3axsbEYMmQIwsPDYWlpCQ8PD5w6dQpubm553qdMiPw11cjICKdOncoxfUJISAi8vLyQkpKSn80RFVpCQgLMzc0x7FgXGJjqFXVzqIBudi9f1E2gQsi0LlXUTaBCyMxMxdGQQMTHx6vkLGlC9jW63JLpkBsV7HnByhepeDwyQCvt06R891Q5OjqqneRToVCgbNmyGmkUERERfWBeyY0q0LrFQL4T1efPn4+RI0fiwoULUtmFCxcwevRoLFiwQKONIyIiIiou8tRTZWlpCZnsZZSYnJwMT09P6OpmrZ6ZmQldXV0MHDgQnTt31kpDiYiIqPiSiayloOsWB3kKqoKCgrTcDCIiIvqgaWCeqvddnoIqHx8fbbeDiIiIPmQlIKeqQDOqZ0tNTUV6erpK2fuclU9ERESkLflOVE9OToafnx/KlCkDExMTWFpaqixEREREOWjg2X/vu3wHVRMmTMDhw4fx448/wsDAAKtWrcL06dNRtmxZbNiwQRttJCIiouKuBARV+R7+++uvv7BhwwY0b94cvr6+8PLyQsWKFeHk5ITNmzejb9++2mgnERERFWclIFE93z1Vz58/R4UKFQBk5U89f/4cANCkSRMcO3ZMs60jIiIiKiby3VNVoUIFhIWFoXz58qhatSp+/fVX1K9fH3/99Zf0gGUierOYXwWiNwhkxgCGlQD7CTIY18j97hZFokDEMoGEw4AiAdCzB+zHylCqSdY6oZ8qkfEs53qluwNlv873dyfKg0+63kO33ndgWToNYXfNsGJxLdy6oT6vtLxLAvoNuomKVeJga/8CP31fA3/+5prrtrv3u4UBX97Azl8r4OcfamrrEEq0Du1D8VmX6yht8QL37lti+c8fIfS2tdq6To5x6N/nCiq6PoddmWSsWO2BP/6qplLHyDADPn2voJHnI1iYp+JumCV+XFUPt+6o32aJVALu/sv31dbX1xdXrlwBAHz99ddYtmwZDA0N8dVXX2H8+PEab+C7MmDAgPdq4lJnZ2eV+cFkMhl27txZZO0hzYk/IBC+SKDMUBlcN8tgWBm47yeQ+Vx9/7YyQ+D+cIH0p4DjPBkq7ZDBYYoMumVe1nHdKEOV/S8X5+VZFyAz7+JxISpuvFo+wRC/a9iytgpGDWqGsDvmmLnoNMwt0tTWNzBQIPypCdatcMPzaIM3brtS1Vi06/gA9+7wTmptadb4PoYODMHmrbUwwv9j3LtvidkBh2Funqq2voFBJp6Fm2LNhjqIea7+2XVf+Z1B3drPMC+oEb4c/SlCLtvju+nBsCrN5+Fmy578s6BLcZDvoOqrr77CqFGjAADe3t64efMmtmzZgkuXLmH06NEab+CrBgwYAJlMlmNp166dVvebV6+2ycTEBJUqVcKAAQMQEhJS6G0/e/YM7du310ArqahFbxKw7AJYdpTBsIIMZb+RQW4IxP6pvn7cn0BmPOC0UAYTdxn0y8pg4iGDUeWXAZOupQx61i+XxOMC+uUAE493dFAlTJded7DvLycc2uuER/fNsHR+baSm6qDNpw/U1r990xJrllfHseByyMjI/bJraJSJ8QEhWDKvNpIS+XBwbena6Qb2HaiIA4dd8fCxBX740RNpaTpo2+qO2vq37lhj1XoP/HPCGRmZOjle19fPRJOGD7FqfR1cvW6Lp+GlsGlrbTwNL4VP293S9uEUHyUgUb3Q4wJOTk7o2rUratWqpYn2vFW7du3w7NkzleWXX34p8PYUCgWUSqXG2rd27Vo8e/YM165dw7Jly5CUlARPT89C3xlpZ2cHA4M3f8Ol958yQ+DFTcC0/suASCaXwbQ+kPKf+qtGwjEB41rA07kCN1orcbuHEpFrBIQi956tuL2ARSeZyuOlSDN0dZWoWDkely/YSGVCyHD5gg2qVo8t1LaH+f+L86dscflCmbdXpgLR1VWgkutzXPzXXioTQoZLV+zhViW6QNvUkQvo6AikZ6gGXGlpOqjuFlmo9lLxkqeg6ocffsjzom0GBgaws7NTWV6dH2vRokWoWbMmTExM4OjoiOHDhyMpKUl6fd26dbCwsMCuXbvg5uYGAwMDPHz4UGUfGzZsgJWVFdLSVLvyO3fujM8///yN7bOwsICdnR2cnZ3Rpk0bbN++HX379oWfnx9iY19ecE+cOAEvLy8YGRnB0dERo0aNQnJycq7bfXX47/79+5DJZNixYwdatGgBY2Nj1K5dG6dPn1ZZZ926dShfvjyMjY3RpUsXLFy4UCXvTd2Q55gxY9C8eXPpZ6VSicDAQLi4uMDIyAi1a9fG9u3bpdePHj0KmUyG4OBg1KtXD8bGxmjUqBFCQ0NVtvvXX3/ho48+gqGhIaytrdGlSxcAwIwZM1CjRo0cx+vu7o6pU6fm+vsorhRxABSArpVqua4VkJnL9Tz9MZAQDAgl4PyDDGUGyxCzSSBqtfr6iUcARRJg2UGTLadsZuZp0NEViHuu+iUn7rkBLK3UDx/lRdNWj1GxchzWrXQrbBPpDcxKpUFHRyAuTnUYLzbeEJaWLwq0zReperh+0xp9evyH0pYpkMuVaNnsHqpViUbpAm6Tiqc8JaovXrw4TxuTyWTS0GBRkcvl+OGHH+Di4oJ79+5h+PDhmDBhApYvXy7VSUlJwdy5c7Fq1SpYWVmhTBnVb4Xdu3fHqFGjsGvXLnTv3h0AEBkZiT179uDAgQP5btNXX32FDRs24ODBg+jRowfu3r2Ldu3aYdasWVizZg2ioqLg5+cHPz8/rF27Ns/bnTx5MhYsWIBKlSph8uTJ6N27N+7cuQNdXV2cPXsWgwYNQmBgIDp37ox9+/YhICAg320PDAzEpk2bsGLFClSqVAnHjh1Dv379YGNjg2bNmqm0ZeHChbCxscGXX36JgQMH4uTJkwCAPXv2oEuXLpg8eTI2bNiA9PR07N27FwAwcOBATJ8+HefPn8dHH30EALh06RL+/fdf7NixQ22b0tLSVALehISEfB9XsSIAXUvAYbIMMh0ZjKoBGZFA9IasvKzXxf4pUKoRoGfDXqriwrrMCwwdfRVTvmqIjPScw0v0/psX1Bj+fqfxy9odUChkuHO3NI4ed0Il1+dF3bT3hgyFeKCyRluiPXkKqsLCwrTdjjzbvXs3TE1NVcq++eYbfPPNNwCyelqyOTs7Y9asWfjyyy9VgqqMjAwsX74ctWvXVrsPIyMj9OnTB2vXrpWCqk2bNqF8+fIqvTh5VbVqVQBZPUxAVqDSt29fqa2VKlXCDz/8gGbNmuHHH3+EoaH6RMjXjRs3Dp988gkAYPr06ahevTru3LmDqlWr4vvvv0e7du0wYcIEAEDlypVx6tQp7Nu3L8/tTktLw5w5c3Do0CE0bNgQQNbdnydOnMDKlStVgqrZs2dLP3/99df45JNPkJqaCkNDQ8yePRu9evXC9OnTpfrZv/ty5cqhbdu2WLt2rRRUrV27Fs2aNZOm7nhdYGCgyraKEx0LADpAZoxqeWYMoJvLTUK61oBMF5DpvLysGLhkraPMEJDrvSxPfyaQdA4oP7+4XIKKn4R4AygyZbAordqTbVE6DbExefvsvq5ilThYlk7DD6v/kcp0dAVq1I5Bh65h6NyyA5RKnlNNSEg0gEIhg4WFaq+ipXkqYmONCrzdZ+GlMH5KGxgYZMLEOB3PY43xzbjjeBZh+vaVSwre/ff+adGiBS5fvqyyfPnll9Lrhw4dQqtWreDg4IBSpUrh888/R0xMDFJSXt6Boa+v/9YcsCFDhuDAgQN48uQJgKyhtOxE+fwSIis0z173ypUrWLduHUxNTaWlbdu2UCqV+QpgXz0Ge/us/IDIyKzx+xs3bsDT01OlfnZglFd37txBSkoKWrdurdLWDRs24O7du3luy+XLl9GqVatc9zNkyBD88ssv0rMkt2zZgoEDB+Zaf9KkSYiPj5eWR48e5eu4ipJcTwajqkDS+Zdf14RSIOk8YFxT/XvLuDaQ/iirXra0B1nB1qsBFQDE7hLQtQRKNdFO+wnIzJTjzi1zuHtESWUymYC7RxRuXivYo7quXLDG8M9bYKRvc2m5dcMCRw+Uw0jf5gyoNCgzUwe375ZGnVrhUplMJuBeKxzXQws//UFami6exxrD1CQNHnWe4vQ5x0Jvk4qPQj1QuSiYmJigYsWKal+7f/8+Pv30UwwbNgyzZ89G6dKlceLECQwaNAjp6ekwNjYGkNUT9bbgqE6dOqhduzY2bNiANm3a4Nq1a9izZ0+B2nzjxg0AgIuLCwAgKSkJX3zxhdqh0vLly+d5u3p6L+8Oyj6e/CTdy+VyKeDLlpGRIf0/Oxdtz549cHBwUKn3etL8m9piZPTmb38dOnSAgYEB/vjjD+jr6yMjIwOfffZZrvUNDAyKddK+dT8ZHgcIGFUTMKoBxGwRUL4ALDtmvf54mhK6NoDdyKzvPKU/k+H5rwLPFghY9QTSHwJRawWseqm+h4VSIG4XYPEpINPlH2Ft+mNrRfhPvojbNy1w64YlOvW4C0MjBQ7uyfr8+k8JQUyUEdb/Pz9KV1eJ8s6JWf/XU8LKJhUVKsbjxQsdPHtiihcv9PAgTPVuv9RUHSQk6ONBGKdW0LQdf1bDuNGncOtOaYTetkaXDjdgaJiJA8FZc4eNH30S0THGWLupDoCs5PbyjvEAAD1dJaxKp6CCy3OkvtDD0/BSAAAP96eQyYBHT8zgYJ+IwQMu4tFjc2mbhBIxo3qxC6reJCQkBEqlEgsXLoRcnvUH6ddffy3w9gYPHoygoCA8efIE3t7ecHQs2DeOoKAgmJmZwdvbGwBQt25dXL9+PdfgUBOqVauGs2fPqpSdOXNG5WcbGxtcvXpVpezy5ctSgPRqIv+rQ335VatWLQQHB8PX11ft67q6uvDx8cHatWuhr6+PXr16vTUQK87M28iQGQtErvj/5J+VAeclMuhaZQVC6eFQSSDQt5PBeSnwbKHAnV4CujaAVW8ZbHxUt5t0FsgIByw7MaDStuOHHWBukYZ+g2/CsnQa7t0xw7SxDRAXmzX8Z2P7AuKV3qXS1qlYsu6o9HO3PnfQrc8d/HvJCpNGslvxXfvnpDPMzdPQv/e/sLR8gXthlpg8vSXi4rOuOzY2yVC+MtxkVfoFfly8V/q5e5cb6N7lBq5cLYMJU9oAAExMMuD7+SVYW6UgMVEfJ0+Xx9rN7lAoit2AkPYwqHr/pKWlITw8XKVMV1cX1tbWqFixIjIyMrBkyRJ06NABJ0+exIoVKwq8rz59+mDcuHH4+eef8zwlQlxcHMLDw5GWloZbt25h5cqV2LlzJzZs2CDdeTdx4kQ0aNAAfn5+GDx4MExMTHD9+nUcPHgQS5cuLXB7XzVq1Cg0btwYCxYsQKdOnbB///4c+VQtW7bE/PnzsWHDBjRs2BCbNm3C1atXUadO1rezUqVKYdy4cfjqq6+gVCrRpEkTxMfH4+TJkzAzM4OPj4+6XecQEBCAVq1awdXVFb169UJmZib27t2LiRMnSnUGDx6MatWyZijOTnD/kFn1lMGqp/rgp8JPOS/CxrVkcF3/5mCpVEMZaoQwoHpXdu+ogN071Of9vR4oRYYb45MmnfK1fQZb2rVrbxXs2ltF7WvZgVK2iEhTtO3c743bO3bSCcdOOmmsfR+iwkzi+cFO/lnU9u3bB3t7e5WlSZOsi0/t2rWxaNEizJ07FzVq1MDmzZsRGBhY4H2Zm5ujW7duMDU1zfNs676+vrC3t0fVqlUxbNgwmJqa4ty5c+jTp49Up1atWvjnn39w69YteHl5oU6dOpg2bRrKli1b4La+rkGDBvj555/x/fffo3bt2jhw4ACmTJmiUqdt27aYOnUqJkyYgI8++giJiYno37+/Sp2ZM2di6tSpCAwMRLVq1dCuXTvs2bNHGsrMi+bNm+O3337Drl274O7ujpYtW+LcuXMqdSpVqoRGjRqhatWqOXLBiIjoA1ACJv+UideTavLg+PHjWLlyJe7evYvt27fDwcEBGzduhIuLixTgfChatWqF6tWrv5M5uLRt3bp1GDNmDOLi4oq6KTkIIVCpUiUMHz4c/v7++Vo3ISEB5ubmGHasCwxMOQt1cXWze97zCen9k2ldqqibQIWQmZmKoyGBiI+Ph5mZZvP4sq/RzrNmQ57Hu9tfp0xNxf0pk7XSPk3Kd0/V77//jrZt28LIyAiXLl2S5guKj4/HnDlzNN7AohIbG4s//vgDR48exYgRI4q6OR+0qKgoLF26FOHh4bnmXRERUTFXAnqq8p1TNWvWLKxYsQL9+/fH1q1bpfLGjRtj1qxZGm1cUapTpw5iY2Mxd+5cVKmiftydNKNMmTKwtrbGTz/9pDI7PhERfThKQk5VvoOq0NBQNG3aNEe5ubn5ezmsVFDZE3V+SAYMGIABAwYUdTNyKMAINBERFTec/DMnOzs73LmT80neJ06cyHUGbCIiIqIPXb6DqiFDhmD06NE4e/YsZDIZnj59is2bN2PcuHEYNmyYNtpIRERExR1zqnL6+uuvoVQq0apVK6SkpKBp06YwMDDAuHHjMHLkSG20kYiIiIo55lSpIZPJMHnyZIwfPx537txBUlIS3NzccjzkmIiIiEjCGdVzp6+vDzc3N022hYiIiKjYyndQ1aJFizc+jPjw4cOFahARERF9gAox/PfB9lS5u7ur/JyRkYHLly/j6tWreX4WHBEREZUwHP7LafHixWrLv/32WyQlJRW6QURERPQBKoKgatmyZZg/fz7Cw8NRu3ZtLFmyBPXr13/relu3bkXv3r3RqVMn7Ny5M8/709gDlfv164c1a9ZoanNEREREBbZt2zb4+/sjICAAFy9eRO3atdG2bVtERka+cb379+9j3Lhx8PLyyvc+NRZUnT59GoYFfFAiERERfdiyp1Qo6JJfixYtwpAhQ+Dr6ws3NzesWLECxsbGb+wAUigU6Nu3L6ZPn16gCc3zPfzXtWtXlZ+FEHj27BkuXLiAqVOn5rsBRERERJqUnp6OkJAQTJo0SSqTy+Xw9vbG6dOnc11vxowZKFOmDAYNGoTjx4/ne7/5DqrMzc1VfpbL5ahSpQpmzJiBNm3a5LsBREREVAJoIKcqISFBpdjAwAAGBgY5qkdHR0OhUMDW1lal3NbWFjdv3lS7ixMnTmD16tW4fPlyARuZz6BKoVDA19cXNWvWhKWlZYF3SkRERJRfjo6OKj8HBATg22+/LfR2ExMT8fnnn+Pnn3+GtbV1gbeTr6BKR0cHbdq0wY0bNxhUERERUZ5p4jE1jx49gpmZmVSurpcKAKytraGjo4OIiAiV8oiICNjZ2eWof/fuXdy/fx8dOnSQypRKJQBAV1cXoaGhcHV1fWs7852oXqNGDdy7dy+/qxEREVFJV8iHKZuZmaksuQVV+vr68PDwQHBwsFSmVCoRHByMhg0b5qhftWpV/Pfff7h8+bK0dOzYES1atMDly5dz9JDlJt85VbNmzcK4ceMwc+ZMeHh4wMTEROX1VyNIIiIiIgDvfJ4qf39/+Pj4oF69eqhfvz6CgoKQnJwMX19fAED//v3h4OCAwMBAGBoaokaNGirrW1hYAECO8jfJc1A1Y8YMjB07Fh9//DEAoGPHjiqPqxFCQCaTQaFQ5HnnRERERNrQs2dPREVFYdq0aQgPD4e7uzv27dsnJa8/fPgQcrnGZpYCkI+gavr06fjyyy9x5MgRjTaAiIiIPnyayKnKLz8/P/j5+al97ejRo29cd926dfneX56DKiGyjqhZs2b53gkRERGVcHz2n6pXh/uIiIiI8qooeqretXwFVZUrV35rYPX8+fNCNYiIiIioOMpXUDV9+vQcM6oTERERvRWH/1T16tULZcqU0VZbiIiI6EPFoOol5lMRERFRQZWEnKo8T9CQffcfEREREeWU556q7GfgEBEREeUbh/+IiIiINIBBFREREVHhlYScKgZVREREpH0loKdKs08SJCIiIiqh2FNFREREWsfhPyIiIiJNKAHDfwyqiIiISPtKQFDFnCoiIiIiDWBPFREREWmd7P9LQdctDhhUERERkfaVgOE/BlVERESkdSXh7j/mVBERERFpAHuqiIiISPs4/EdERESkIcUkOCooBlVERESkdcypIiIiIqI8YU8VERERaR9zqoiIiIgKryQM/zGoIiIiIu0rAT1VzKkiIiIi0gD2VNEH41q8PXQzDYq6GVRA8nv3i7oJVAiR7RoVdROoEBTpekCIdvfB4T8iIiIiTSgBw38MqoiIiEj7SkBQxZwqIiIiIg1gTxURERFpHXOqiIiIiDSBw39EREREhScTolBLQSxbtgzOzs4wNDSEp6cnzp07l2vdHTt2oF69erCwsICJiQnc3d2xcePGfO2PQRURERF9cLZt2wZ/f38EBATg4sWLqF27Ntq2bYvIyEi19UuXLo3Jkyfj9OnT+Pfff+Hr6wtfX1/s378/z/tkUEVERETaJwq55NOiRYswZMgQ+Pr6ws3NDStWrICxsTHWrFmjtn7z5s3RpUsXVKtWDa6urhg9ejRq1aqFEydO5HmfDKqIiIhI67IT1Qu6AEBCQoLKkpaWpnZf6enpCAkJgbe3t1Qml8vh7e2N06dPv7WtQggEBwcjNDQUTZs2zfMxMqgiIiIi7dNAT5WjoyPMzc2lJTAwUO2uoqOjoVAoYGtrq1Jua2uL8PDwXJsYHx8PU1NT6Ovr45NPPsGSJUvQunXrPB8i7/4jIiIirdPElAqPHj2CmZmZVG5goNlHk5UqVQqXL19GUlISgoOD4e/vjwoVKqB58+Z5Wp9BFRERERULZmZmKkFVbqytraGjo4OIiAiV8oiICNjZ2eW6nlwuR8WKFQEA7u7uuHHjBgIDA/McVHH4j4iIiLTvHSaq6+vrw8PDA8HBwVKZUqlEcHAwGjZsmOftKJXKXPO21GFPFREREWndu55R3d/fHz4+PqhXrx7q16+PoKAgJCcnw9fXFwDQv39/ODg4SHlZgYGBqFevHlxdXZGWloa9e/di48aN+PHHH/O8TwZVREREpH3veEb1nj17IioqCtOmTUN4eDjc3d2xb98+KXn94cOHkMtfDtglJydj+PDhePz4MYyMjFC1alVs2rQJPXv2zPM+GVQRERHRB8nPzw9+fn5qXzt69KjKz7NmzcKsWbMKtT8GVURERPROFJcHIxcUgyoiIiLSPiGyloKuWwwwqCIiIiKte9eJ6kWBUyoQERERaQB7qoiIiEj73vHdf0WBQRURERFpnUyZtRR03eKAQRURERFpXwnoqWJOFREREZEGsKeKiIiItK4k3P3HoIqIiIi0j/NUERERERVeSeipYk4VERERkQawp4qIiIi0rwTc/cegioiIiLSuJAz/MagiIiIi7SsBierMqSIiIiLSAPZUERERkdZx+I+IiIhIE5ioTkRERFR4JaGnijlVRERERBrAnioiIiLSPqXIWgq6bjHAoIqIiIi0jzlVRERERIUnQyFyqjTaEu1hThURERGRBrCnioiIiLSvBMyozqCKiIiItK4kTKnAoIqIiIi0j4nqRERERIUnEwKyAg7jFXS9d42J6kREREQawJ4qIiIi0j7l/5eCrlsMMKgiIiIirSsJw38MqoiIiEj7SkCiOnOqiIiIiDSAQRURERFpX/bknwVdCmDZsmVwdnaGoaEhPD09ce7cuVzr/vzzz/Dy8oKlpSUsLS3h7e39xvrqcPjvPXX//n24uLjg0qVLcHd3x9GjR9GiRQvExsbCwsKiqJtHhbUzEbJfE4HnCsBVH2KkBVDVIPf6SUrIVscDJ1KARCVQRhdihAXgaVTwbVKhdBgQjc+GRaK0TSbuXTfC8ikOCL1snGt9r0/j4DMhHLbl0vEkzACrZ9vj/GEz6fX9T6+oXe/nmfbY/mMZjbe/pOvhcRU+DS/DyjQFtyKsMHd/E1x7aqu2bssq9zCo8UU4lo6HrlyJh8/NsfFsbez5r4pU54um59HW7Q7szJKQoZDjRrgNlh7xxNVctlkSvevJP7dt2wZ/f3+sWLECnp6eCAoKQtu2bREaGooyZXJ+po4ePYrevXujUaNGMDQ0xNy5c9GmTRtcu3YNDg4OedpnkfZURUVFYdiwYShfvjwMDAxgZ2eHtm3b4uTJk0XZrAI7evQoZDIZZDIZ5HI5zM3NUadOHUyYMAHPnj0r1LYbNWqEZ8+ewdzcXEOtpSJzJAWyFXEQ/c0gVtgBrnqQTYwCYhXq62cIyCZEAhGZEAHWEOvsIcZaAtY6Bd8mFUqzjrEYGvAUmxfZYUTbyrh33RCzt9yDuVWG2vpu9ZIxafkD7PulNIa3qYxT+8wQsOY+nKq8kOr0qu2msiz8yhFKJXBiDz/zmtbG7Q7Gtj6Jlcfroc+qz3ArwgrLe++GpXGK2vrxqQZYdbIufNZ2RY+fe+DPK1XxbYcjaFjhoVTnQYw55u73QvefesJ3fRc8jSuF5X12w9L4hdptlkjvuKdq0aJFGDJkCHx9feHm5oYVK1bA2NgYa9asUVt/8+bNGD58ONzd3VG1alWsWrUKSqUSwcHBed5nkQZV3bp1w6VLl7B+/XrcunULu3btQvPmzRETE1OUzUJ6enqh1g8NDcXTp09x/vx5TJw4EYcOHUKNGjXw33//FXib+vr6sLOzg0xWXJ7VTbmRbU8EPjYF2pkCznoQYywBAzmwL1n9CvuSgQQlxAxroIYBYKcL1DYEXPULvk0qlK5Do7FvS2kc2FYaD28b4oeJ5ZD2Qoa2vZ+rrd95cBQuHCmF7T+WwaM7htgw3x53/jNCJ9+X17rYKD2VpWHbeFw5aYrwh+xt1LR+nlew45Ibdl2pinvRpTF7bzOkZuihs/tNtfVDHjjgSGgFhMVY4nGsOX45Xwu3I6xQxzFcqrPvWmWcDSuHJ3FmuBddGgsPNkYpw3RUKlO0f88+NAkJCSpLWlqa2nrp6ekICQmBt7e3VCaXy+Ht7Y3Tp0/naV8pKSnIyMhA6dKl89y+Iguq4uLicPz4ccydOxctWrSAk5MT6tevj0mTJqFjx44q9QYPHgwbGxuYmZmhZcuWuHIlq5v81q1bkMlkuHlT9YOwePFiuLq6Sj9fvXoV7du3h6mpKWxtbfH5558jOjpaer158+bw8/PDmDFjYG1tjbZt2+ZpvdyUKVMGdnZ2qFy5Mnr16oWTJ0/CxsYGw4YNU6m3atUqVKtWDYaGhqhatSqWL1+e6zaze8Hi4uIAAOvWrYOFhQX279+PatWqwdTUFO3atVPpEVMoFPD394eFhQWsrKwwYcIE+Pj4oHPnzlIdZ2dnBAUFqezL3d0d3377rfTzm84BAHz77bdwd3fHxo0b4ezsDHNzc/Tq1QuJiYlSHaVSiXnz5qFixYowMDBA+fLlMXv2bABAy5Yt4efnp9KGqKgo6Ovr5+sbQrGQIYBb6RB1X/lDKZcBdQ0gu67+4iA79QJwM4Dsh1jIuj2BbNAzYHMCoBAF3iYVnK6eEpVqpeDi8VJSmRAyXDpeCm4e6ns6qnmk4NIr9QEg5J9SqOahPui1sM5A/VYJ2L817xdzyhtduQLV7KNwNqycVCYgw9n7DqjlEJGHLQjUd34MZ6s4hDy0z3UfXeteR2KqPm5FWGmo5cWfTFm4BQAcHR1hbm4uLYGBgWr3FR0dDYVCAVtb1eFXW1tbhIeHq13ndRMnTkTZsmVVArO3KbKgytTUFKampti5c2eukSYAdO/eHZGRkfj7778REhKCunXrolWrVnj+/DkqV66MevXqYfPmzSrrbN68GX369AGQFRC0bNkSderUwYULF7Bv3z5ERESgR48eKuusX78e+vr6OHnyJFasWJHn9fLCyMgIX375JU6ePInIyEipjdOmTcPs2bNx48YNzJkzB1OnTsX69evzvN2UlBQsWLAAGzduxLFjx/Dw4UOMGzdOen3hwoVYt24d1qxZgxMnTuD58+f4448/8t3+N52DbHfv3sXOnTuxe/du7N69G//88w++++476fVJkybhu+++w9SpU3H9+nVs2bJFerMPHjwYW7ZsUXkfbNq0CQ4ODmjZsmW+2/tei1dmXRwsdVTLLXWA57nMbvcsEziWAigAEWgD0c8cst8SswKrgm6TCsystAI6ukBclGpKamy0LixtMtWuY2mTidjo1+pH6cKyjPr6rXvE4kWSDk7s5dCfplkap0JXLvA82UilPCbJGFam6oNiADA1SMPJCT/j3KSf8EOvvZi7vwnOhjmq1PGqeB8nJ/yMs5N+Qr/6/+LLzR0Q98Ioly2WQBoY/nv06BHi4+OlZdKkSVpp6nfffYetW7fijz/+gKGhYZ7XK7JEdV1dXaxbtw5DhgzBihUrULduXTRr1gy9evVCrVq1AAAnTpzAuXPnEBkZCQODrG/hCxYswM6dO7F9+3YMHToUffv2xdKlSzFz5kwAWb1XISEh2LRpEwBg6dKlqFOnDubMmSPte82aNXB0dMStW7dQuXJlAEClSpUwb948qc6sWbPytF5eVa1aFUBWAnqZMmUQEBCAhQsXomvXrgAAFxcXXL9+HStXroSPj0+etpmRkYEVK1ZIvXJ+fn6YMWOG9HpQUBAmTZok7WPFihXYv39/vtqdl3MAZPVErVu3DqVKZX0b//zzzxEcHIzZs2cjMTER33//PZYuXSodm6urK5o0aQIA6Nq1K/z8/PDnn39KQeu6deswYMAAtcOdaWlpKgFYQkJCvo6p2Pl/wCT8LQEdGVBZHyJaAdmvCRD9+Uf3Q9S213Mc/sMCGWm8Qft9kZymj14/94CRfgY8nR9jbOtTeBxnhpAHLxOYzz9wQK+fe8DC+AW61rmBed0O4PM1XRGbkvsNDCWKBuapMjMzg5mZ2ZvrArC2toaOjg4iIlR7HyMiImBnZ/fGdRcsWIDvvvsOhw4dkuKRvCrynKqnT59i165daNeuHY4ePYq6deti3bp1AIArV64gKSkJVlZWUs+WqakpwsLCcPfuXQBAr169cP/+fZw5cwZAVg9Q3bp1pSDmypUrOHLkiMr62a9lbwMAPDw8VNqW1/XySvw/ypbJZEhOTsbdu3cxaNAgle3PmjUrX9s2NjZWGea0t7eXesLi4+Px7NkzeHp6Sq/r6uqiXr16+Wp3Xs4BkDWMmB1Qvd6WGzduIC0tDa1atVK7D0NDQ3z++edS8uDFixdx9epVDBgwQG39wMBAle5fR0dHtfXeS+ZyCDlyJpDHKoDSuXwcrXSAcrpZAVW28rqQPVdmDf0VZJtUYAnPdaDIBCxe65WytM5EbJT676mxUbqwtH6tvk0mYiNz1q9RPwmOFdOwbwuHjbQhNsUQmUoZSpuoJpBbmaYgJin34EdAhkex5rgVYY2NZ91x6EYFDGx0SaVOaoYeHsWa478ndpi+uwUUSjm65JKnRdqlr68PDw8PlRSS7KTzhg0b5rrevHnzMHPmTOzbty/ffy+B92BKBUNDQ7Ru3RqtW7fG1KlTMXjwYAQEBGDAgAFISkqCvb09jh49mmO97GkF7Ozs0LJlS2zZsgUNGjTAli1bVHKXkpKS0KFDB8ydOzfHNuztX46Hm5iYqLyW1/Xy6saNGwCygo+kpCQAWXNivBr0AICOjk6OdXOjp6en8rNMJpOCt7ySy+U51snIeHkHU17OQW5tUSqzhp6MjN7e/T148GC4u7vj8ePHWLt2LVq2bAknJye1dSdNmgR/f3/p54SEhOITWOll9TTJLqVBNPn/BVwpgEtpEJ1N1a9TXR84nJJVT/7/wOpxJoSVPGt7QP63SQWWmSHH7X+NUadJIk7vy+oplMkE3JskYdc69YHQjRBjuHsl4Y9VNlJZ3aaJuBFikqNu297PceuKEe5d57CRNmQqdXDjmQ08XR7j6C0XAIAMAvWdn2DbhRp53o5MBujrvvnuWplMQO8tdUqSd/2YGn9/f/j4+KBevXqoX78+goKCkJycDF9fXwBA//794eDgIOVlzZ07F9OmTcOWLVvg7Ows5V5ldybkRZEHVa9zc3PDzp07AQB169ZFeHg4dHV14ezsnOs6ffv2xYQJE9C7d2/cu3cPvXr1kl6rW7cufv/9dzg7O0NXN++HW9D11Hnx4gV++uknNG3aFDY2WRfVsmXL4t69e+jbt2+htp0bc3Nz2Nvb4+zZs2jatCkAIDMzU8qJymZjY6OS3J6QkICwsDDp57yegzepVKkSjIyMEBwcjMGDB6utU7NmTdSrVw8///wztmzZgqVLl+a6PQMDA2kosjgSn5WCbG4MUFkfqKoP2e+JQKoSaJv1B1b2XQxgrQMx2CKrfkdTyP5MgmxZXFaQ9CQTsi0JEF1L5XmbpFk7frLGuKBHuHXFGKGXjNFlSBQMjZU48P/E8vHfP0R0uB7WBmZ9Adu5ygbzf7+Dbl9E4lywGZp1ikOlWi8QNL6cynaNTRVo2iEeP03P/xc3yrtNZ2tjRsfDuP7MBlef2KKP578w0svAn1eyRiNmdgxGZKIJlhxpAAAY2Ogirj2zweNYc+jrKNCk4gN8UvMWAv/2AgAY6mVgcJMQ/HPLGdFJJrAwSkWPeldRplQyDl53zbUdJU4hJvEsyHo9e/ZEVFQUpk2bhvDwcLi7u2Pfvn1SPu/Dhw8hl7/szf/xxx+Rnp6Ozz77TGU7AQEBKjdvvUmRBVUxMTHo3r07Bg4ciFq1aqFUqVK4cOEC5s2bh06dOgEAvL290bBhQ3Tu3Bnz5s1D5cqV8fTpU+zZswddunSRuua6du2KYcOGYdiwYWjRogXKli0r7WfEiBH4+eef0bt3b0yYMAGlS5fGnTt3sHXrVqxatSrXnqGCrgcAkZGRSE1NRWJiIkJCQjBv3jxER0djx44dUp3p06dj1KhRMDc3R7t27ZCWloYLFy4gNjZWpRemMEaPHo3vvvsOlSpVQtWqVbFo0SLp7sFsLVu2xLp169ChQwdYWFhg2rRpKseW13PwJoaGhpg4cSImTJgAfX19NG7cGFFRUbh27RoGDRok1Rs8eDD8/PxgYmKCLl26aOR38F5qYQwRr4BsXXzWEJ2rPsR3NkDp///eIxXAq6lkZXQhvrOB7Mc4yIaEZwVcXUsBvUrlfZukUf/ssoS5lQL9x4fD0iYT964ZYXJfF8RFZ/XY2jikQ/nKPQLXL5jguxFO8JkYjgFfh+NpmAGmD3TGg1DV3qhmneIAmcCRnZbv8GhKngPXK8LS+AWGNTsPK5MUhEZYY8Qvn+J5clZPr515EpTi5YfQUD8D37Q/jjKlkpCWqYv70RaY8mcrHLheEQCgVMrgbBWHDt0OwML4BeJfGOLa0zIYuL4z7kXzDk6JQFaOaEHXLQA/P78cd5dne30E5v79+wXbySuKLKgyNTWFp6cnFi9ejLt37yIjIwOOjo4YMmQIvvnmGwBZQ0h79+7F5MmT4evri6ioKNjZ2aFp06Yqt0mWKlUKHTp0wK+//ppjUq+yZcvi5MmTmDhxItq0aYO0tDQ4OTmhXbt2KhHq6wq6HgBUqVIFMpkMpqamqFChAtq0aQN/f3+V5LjBgwfD2NgY8+fPx/jx42FiYoKaNWtizJgxBfhtqjd27Fg8e/YMPj4+kMvlGDhwILp06YL4+HipzqRJkxAWFoZPP/0U5ubmmDlzpkpPVV7PwdtMnToVurq6mDZtGp4+fQp7e3t8+eWXKnV69+6NMWPGoHfv3vm626JY6lwKonMptS+JRWpmz65uALH0Lb/vN2yTNG/XWmvsWmut9rUJn1XMUXZ8twWO77Z44zb/3myFvzczl+pd2HahJrZdqKn2tSEbO6n8vPyoJ5Yf9VRbFwDSFboYt72dRttHxZNM5DcJh4q1AQMGIC4uThpifZ/cv38frq6uOH/+vMoQ5dskJCTA3NwcTf8aDl2T4jssWNLJWz0q6iZQIUQOb1TUTaBCUKSn4trP3yA+Pj5Pd9flR/Y1umWdr6GrU7AvzJmKVBy+9J1W2qdJ711OFZU8GRkZiImJwZQpU9CgQYN8BVRERFRMCBQip0qjLdEaBlVU5E6ePIkWLVqgcuXK2L59e1E3h4iItOEdJ6oXBQZVJUz2HGDvk+bNm+d7KggiIqL3DYMqIiIi0j4lVO9szu+6xQCDKiIiItK6dz35Z1FgUEVERETaVwJyqvhgMCIiIiINYE8VERERaV8J6KliUEVERETax6CKiIiISAN49x8RERFR4ZWEu/+YqE5ERESkAeypIiIiIu1jThURERGRBigFICtgcKRkUEVERESUpQT0VDGnioiIiEgD2FNFRERE70AheqpQPHqqGFQRERGR9pWA4T8GVURERKR9SoEC9zgVk0R15lQRERERaQB7qoiIiEj7hDJrKei6xQCDKiIiItI+5lQRERERaQBzqoiIiIgoL9hTRURERNrH4T8iIiIiDRAoRFCl0ZZoDYMqIiIi0r4S0FPFnCoiIiIiDWBPFREREWmfUgmggPNNKTlPFREREVGWEjD8x6CKiIiItK8EBFXMqSIiIqIP0rJly+Ds7AxDQ0N4enri3Llzuda9du0aunXrBmdnZ8hkMgQFBeV7fwyqiIiISPuUonBLPm3btg3+/v4ICAjAxYsXUbt2bbRt2xaRkZFq66ekpKBChQr47rvvYGdnV6BDZFBFREREWieEslBLfi1atAhDhgyBr68v3NzcsGLFChgbG2PNmjVq63/00UeYP38+evXqBQMDgwIdI4MqIiIi0j5RiF6q/+dUJSQkqCxpaWlqd5Weno6QkBB4e3tLZXK5HN7e3jh9+rTWDpFBFRERERULjo6OMDc3l5bAwEC19aKjo6FQKGBra6tSbmtri/DwcK21j3f/ERERkfYJgQI/b+b/PVWPHj2CmZmZVFzQYTptYVBFRERE2qdUArICTuL5/5wqMzMzlaAqN9bW1tDR0UFERIRKeURERIGT0POCw39ERESkfdnzVBV0yQd9fX14eHggODhYKlMqlQgODkbDhg01fWQS9lQRERHRB8ff3x8+Pj6oV68e6tevj6CgICQnJ8PX1xcA0L9/fzg4OEh5Wenp6bh+/br0/ydPnuDy5cswNTVFxYoV87RPBlVERESkdUKphCjg8F9BplTo2bMnoqKiMG3aNISHh8Pd3R379u2TktcfPnwIufzlgN3Tp09Rp04d6ecFCxZgwYIFaNasGY4ePZqnfTKoIiIiIu3TQKJ6fvn5+cHPz0/ta68HSs7OzhCFfBwOgyoiIiLSPqUAZB/2s/8YVBEREZH2CQGgoHf/FY+ginf/EREREWkAe6qIiIhI64RSQBRw+K+wuU7vCoMqIiIi0j6hRMGH/wq43jvGoIqIiIi0riT0VDGnioiIiEgD2FNFxV72N5jMlPQibgkVhlxkFHUTqBAU6alF3QQqhOzzp80eoUyRVuBhvEwUj+uDTBSXPjWiXDx+/BiOjo5F3QwiomLv0aNHKFeunEa3mZqaChcXF4SHhxdqO3Z2dggLC4OhoaGGWqZ5DKqo2FMqlXj69ClKlSoFmUxW1M3RuISEBDg6OuLRo0d5ejo7vX94Dou/D/0cCiGQmJiIsmXLqjy6RVNSU1ORnl640QR9ff33OqACOPxHHwC5XK7xb1bvIzMzsw/yYl6S8BwWfx/yOTQ3N9fatg0NDd/7gEgTmKhOREREpAEMqoiIiIg0gEEV0XvOwMAAAQEBMDAwKOqmUAHxHBZ/PIeUF0xUJyIiItIA9lQRERERaQCDKiIiIiINYFBFREREpAEMqohKkEePHiEjo3g87oGIih7TrvOHQRVRCTF58mR8+umnCA0NBcCLJWmOUlmw57nR++9DfEqFNjGoIvrAZWZmAgDat2+P+Ph43LhxAwAvlqQ5crkciYmJSE5OLuqmUCG8/kVLCIHk5GRs3LgRz58/L6JWFS8Mqog+IGFhYQBeXhyVSiV0dbOeRtWkSRNYWVnh+PHjiIuLK6omUjGWW+/mnj170KhRI9y+ffsdt4gK69Vz+voXLZlMhkuXLsHHxwf6+vrvumnFEoMqog9AUlISvLy8sGnTJgghpItj9oNR161bh927d6NWrVq4dOmS9MePQ4D0JkIIZGZmSu+T7PfVkydPAEDKzzMxMcHdu3dRu3btomko5Vv2eX01kDp//jx27dqlUi8yMhK1atVCZGTku25iscSgiqiYE0LA1NQUmzdvxtSpU1Uukvfu3YO7uzu+/fZbHD16FCdOnMDly5dx5coVABwCpNxl/8HV1dWFTCZDTEwMhBAYO3Ys6tWrh+fPn0NPTw9AVlBfqVIl3L17t4hbTXmVfV6vX7+O3bt3AwDWr1+PYcOGYceOHVK9mJgYyOVylC5duqiaWqwwqCIqpl7tPRBCoHz58nj8+DEuXLgg1dm5cyfS0tJw8uRJLFiwAMHBwbC3t8eBAwcQFRVVVE2nYkAmkyE2NhYzZsxArVq10LlzZyQmJmLGjBmwsbHBV199hevXrwMAnj59CgAwMzMryiaTGurypBQKBTZv3oyKFSuiSZMm2LRpEy5duoSlS5fC19cX48aNw+HDhwEAjo6OuHfvHkqVKlUUzS92GFQRFSNKpRIKhQKAai+TTCaDQqFA9+7dMW/ePMTHxwMAdu/ejebNm8PBwQEZGRkoX748Ro0ahXPnzkl3AVLJpVQqpRsZXhcdHY3PP/8cf/31F8aOHYsvvvgCT58+hYmJCZYvX46oqCj4+/sDAFxdXREWFoYyZcq8y+bTG6i7TiiVSshkMly+fBkLFy7E0KFDcevWLSxevBguLi4AgBkzZqBRo0aYOHEiHj16hMTERNSsWRMPHz4skuMobhhUERUjcrkcOjo6AIC///4bZ8+eRWpqKgBAR0cHbdu2RXR0NP777z8AWd8yr127BuDlRbZv374IDw/HmTNneCt8CZUdSMnlculGhpSUFJU6q1atwpUrV/Dbb7/Bx8cH/fr1Q9WqVQFk3fSwcOFCnD17FgsWLEBoaCjq1avHRPX3SPZ14vTp0zhz5gyArPOdkZGBn376CTo6Ohg6dCisra1hb28PCwsLCCEgl8sRFBQEExMTTJ8+HWfPnoVCoYCdnR1zMPOAQRXRe0gIoTbgefz4MUaOHAlLS0sMHz4c/fv3x+eff45Hjx4BAD7++GPExcXh3LlzAIAWLVrg/PnziI+Ph6GhIYCsC6ulpSXOnj2LBw8eSPujkiM7kIqLi8OwYcPg7OwMX19fKUk5KSkJly5dQosWLeDs7Cytl/2eVCgUqFatGgIDA3Hy5ElMnz4dJiYmsLOze+fHUtKp++yGh4fj33//hZOTE7p27YoePXrg22+/RXJyMvT09HD69Gm0aNECFhYW0jrZvVhCCFhbW2P58uWIjIzE5s2b8fjxYxgZGTEHMw8YVBG9Z7Ivbtl37gEvL5ynTp1CWFgYdu3ahbCwMPz++++IjIxEUFAQAKB+/fpwdHTEuXPn8Pz5c3To0AH29vaYOHGi1H2/detWmJiYIDg4mHNWfeAUCoXUQ/mqY8eOoUuXLjh48CDi4+Mxc+ZMJCcno1+/fnj27BlMTU3x8OFD6OjoICEhAQCkXgzg5V2lgwYNwtChQxEVFYXLly8z7+YdyS0NAAB++ukn1K9fH9999x1mzpyJhw8f4vPPP8eOHTvw66+/AgBq1qwp9V5lX1uyz2n29tzc3BAYGAgrKyvY2NggIiLinRxbsSeI6L0TFxcnvv/+e7FkyRLx8OFDIYQQSqVS3L9/X1y/fl0IIcTt27fF9OnThampqahbt664cOGCEEKIoKAgUb9+fbF3714hhBA7d+4Urq6uokaNGqJ+/fqiatWq4syZM2Lv3r1CqVQWzQHSO5eWlib9/9ChQ0Imk4kqVaqIGzduCCGEiIiIENWrVxd+fn5CCCECAwNFpUqVpPeVQqEQQggREhIibSf7/TNixAjRqFEj8fjx43dyLJRFoVCIv//+W4SFhUnn96+//hI1a9YUDRo0kMoiIyNF//79RYsWLYQQQuzfv1/IZDJx6tQple39/vvv4vbt20IIITIyMoQQQsybN0+4ublJ+6M3Y08VUREQuQzvHT58GLdv34a7uztWr16NlStXwsPDA2fOnIFMJoOTkxOcnJwwevRoeHl54dy5cxg5ciQUCgWCg4MBAJ988gnkcjnOnj0LAOjUqRMOHz6MPn36oHPnzjhy5Ag8PT3Rvn179lB9QISaYaATJ06gY8eOcHNzw5AhQ7Bv3z4AwEcffYQmTZqgatWqUp5UmTJl4OPjI/VmDBkyBLq6upg5cybOnj2LlJQUnD59GpMnT8alS5cAAOnp6QAAZ2dnpKamwsHBgXl6GqaupzEmJgYjRoyAubk5Ro0ahS5duuCLL74AADRu3BgVK1ZEWlqaNGGnjY0NmjVrhtDQUISGhqJNmzbw9vbGwIEDMXHiROzbtw+DBw9GUFAQHj9+DOBlTpahoaE0XPxq7znloqijOqIPkVKpVNsLpFAocu0dOn/+vJDJZOKjjz4SP/30kxAi6xtmly5dRKNGjaQegi1btohKlSqJf/75R1rX0dFRdO3aVaSmpgohhOjQoYNo2rSpuH//fr7aRx+OI0eOCHd3dzFkyBDx999/i0GDBgkvLy+xZs0aIYQQM2fOFDY2NkKIlz1Ot27dEvr6+uKvv/4SQghx6tQp8dFHH4lq1aoJZ2dnYWpqKr788kvx9OlTlX2NGTNGfPzxx+/w6Eqe9PR06f/r168Xnp6e4syZM0IIIa5cuSKcnZ3FnDlzhBBCLFu2TFSuXFlcvXpVWickJER4eHiIqVOnCiGECAsLE/PmzRPNmjUT5cqVE507dxYnT55U2WdYWJioUqWKmD17trYP74PBoIpIQ5RKpcjMzFQpy+5Cf11oaKiYNWuW+PXXX8WjR4+EEEI8f/5cdO7cWVhZWUlDMkJkXTBr1qwpfvjhByFE1lCLt7e3SEpKEkIIsXfvXlGuXDnh5OQkdu7cKYQQ4sSJE+L8+fM59puZmclgqphTKpU5hmHS0tLEL7/8IoKDg4UQQiQnJ4uBAweKL774Qqpz6dIlUbFiRVG3bl2hUCjEf//9J/T19cXBgwelOgqFQrRv3150795dKouNjRVHjx4Vf/75p9r2tGvXTshkMrFv3z5NHmaJpG547fHjx6Jjx47i559/FkJkXVPc3d3F/v37hRBZ53XGjBlCJpOJvn37iuTkZHH27FnRsGFDERAQIG0nISFBjB49Wri6uqpsPzIyUm1bkpKSxJAhQ0TLli3FvXv3NHSEHz4GVUQalpmZKX766SfRqVMnMXToUHHo0CHx4sULIYQQ0dHRYtCgQaJUqVKiVatWwtPTU9StW1ccP35cCCHE4sWLhampqXj+/LlKb1Lz5s3FkCFDhBBCzJkzR1SuXFlMmzZNbN68WXTt2lVMmjRJBAQEiIsXLxbNQZNWvS2X5ebNm8LT01P07t1bCCFEfHy8cHJyEsHBwWLRokWiSpUqwtzcXHTr1k3KtUtKShLe3t6iR48eQoiXvVUrVqwQMplMxMfHq91XZmamUCgU0heGy5cvM9emELJ/d9m/f6VSmSM3rVatWsLPz0/ExsaKzMxMUaFCBdGzZ09Rp04dYW5uLjp06CC2b98uEhMThRBZOZnDhw8XTZs2VdnO5s2bRbt27cSzZ89ytCP7vFLhMKgi0pAHDx6Ifv36CZlMJtzc3MQ333wjWrZsKRwcHKRvmdu2bRM1a9aUkkGVSqXo16+f8PDwEC9evBC3b98WRkZGYuvWrUKIlxfc/v37i7Zt2wohhHj48KGYO3euqFKlinBxcRHz5s0TKSkpOdrDHqniTV3PpxBCpKSkiHnz5okxY8aolE+ZMkU0bNhQPHjwQAghRJ06dYRMJhNNmzYVP/zwg8qQXfb7as2aNUJXV1fl/ZOYmKi2Z4LvJ83JyMjI9fdZr149MXDgQJWg9ttvvxUNGzYUFy5cECkpKaJXr17C3NxcbNiwQSUAS0tLE0+ePBFCCLF27VphZWWl0hOZW885aQ6zzog05Pnz5wgODsaUKVNw7do1zJ49G6tWrYKdnR02b94MAPjjjz8wcuRIVKxYEdu2bUOHDh3w22+/wc7ODjExMShfvjzatGmDRYsW4dq1a5DL5Xj69Cn+/fdf1K1bF0DWhJ4TJkzA8ePHce/ePYwfPx5GRkY5kt+ZhF58if8/dy87WTgxMRFz5swBABgZGeH69eu4cOGCyizX9erVQ0ZGBvbv3w8AaNmyJcqXL4/Dhw9j5MiRsLe3BwAcPXoUa9asQUZGBpo2bQorKysp8RwATE1N4eLikiPxne8nzcl+7l50dDQOHTqE7t27459//gGQdd5u3LiBe/fuSfW7du2KmJgYnDt3DkZGRqhTpw50dXXRvn17ODg4AMhKaF+4cCG2b98OIOtmhIkTJ8LV1VVlv9l1STsYVBFpSI0aNdCwYUOVP3S2trZ49uyZNPHmw4cPMXv2bNjb2+Prr79GtWrVcO7cOezevRsODg7Q19dH7969cf78efTu3RuTJk1Cy5Yt8eLFCwwdOlTarhACNjY2AF7Ojv363FZUfMlkMsTFxWH27Nnw9fXF999/jylTpmDPnj0AgHbt2kGhUODQoUPSOvXq1UOFChVw4MABAMCwYcPw7NkzDB8+HP/88w+ePn2KH3/8EVOmTEF8fDyEEHB1dUV4eDgaNWqktg1UOK/fCSn+/9y9DRs2wNHREX369MGcOXNw9OhRhISEAAD69++PBw8eqAS6NWvWRPny5XHs2DEkJCRg0KBBqFq1Kho2bIjJkydj6dKlaNq0KTZs2ABLS0sAQPXq1TF+/Hjp8TOvyg7WSfN4BSbSEF1dXbRs2RL379/Hvn37MHv2bNSqVQvPnj1D8+bNAWRNb/D06VNs2rQJt2/fxvz581GrVi0kJCTg+PHjSEtLQ+PGjeHk5IT27dujcuXKGDlyJP777z+Vma1f/YOX/e2TihchRK7P3QsPD0e3bt2wdetWVK9eXXrUUHaPZ6NGjWBtbS31bgCAg4MDypcvj6tXr+LatWtwdXXF+vXrERoaCn9/f9SsWROLFi1Cr169MGzYMOl2ewC5toMKJjuYen0CX5lMhps3b2LmzJkYPnw4fvjhB3Ts2BHp6en4559/8OLFC1SvXh0VKlTA8ePHVR563qRJExw/fhwXL16ElZUVfvnlFwwdOhT//fcfNmzYgPbt2+PUqVP4/PPP1baF3pGiG3kk+vA8ePBA1KpVS8hkMtGuXTsREBAgBg4cKHr06CFOnTolrly5IuRyuVizZo14/vy5ECIrYfi7774TY8eOFREREUKhUAhfX1/x0UcfqWybSaTFS3bOzJUrV1Tu5lSX1xIdHa3y8+bNm4WZmZkICwsTQmTdzTds2DBhYWEhJSNPmTJFNGrUSPz333/SeqNHjxZGRkZi3rx5Kvs7e/aslMdHmpGWliaGDh0qfv/9dyGE+mlK/vnnH7Fjxw5pOgSlUikWLlwoKleuLMLDw6V6U6ZMEXXq1JFuIvj+++9FrVq1pHyoFy9eiK5duwoLCwsxefJklX1k3wWcjdeJosWeKiINcnR0RPXq1dG6dWvs3r0b3377LVavXg1HR0cMHDgQz549Q2BgIGbPno0OHTpg0KBBqFKlCtavX486derAysoKcrkcffr0weXLl3H37l0AWd82ObRXfKSlpUn/79KlC06ePCnlKL36AONJkybB3t4eHTt2xNy5cwFk9Wg8ePAAtWvXhpOTEwDA2NgY/fr1g6mpKTZs2AAgK/dGV1cXs2bNQnp6Oi5duoSIiAjUrVtXpQdLV1cX9evXR8WKFd/YO0ZvFx8fj2PHjkk/ly9fHuXKlQOQ1Xssk8nw4MEDREZGwsPDA927d8fIkSPRs2dP3Lt3DzKZDJcuXUKVKlVUnrvXoUMH6OrqSuetb9++sLCwwLhx47B3714sWLAAVapUQcuWLWFvby+9v4QQMDExAZDV2yheeZQQFZEiDemIPkC//fabaNCggThy5IhUlv0N1dXVVYwcOVLcvHlTzJo1SwwaNEjt/D8RERHCwsJCmsyPio+ZM2eK9u3bi9DQUCGE6uNhhBDizp07onHjxmL9+vWib9++4vfffxcTJ04UMplM/Pbbb0IIISZMmCBatWolrl27Jq0XHh4umjZtKt0mn5qaKrZs2SKsra1FtWrVhLGxsVi4cKG4c+eO2nbx7r3CmzNnjtDV1RWxsbFqXz9x4oTQ19cXHTp0EIsXLxZCCPHrr78Kd3d3ac6wVatWCWtr6xwTqLq5uYmmTZtK80YdP35c9OrVSzg6Ooq6deuqPB6I3l8Mqog0LDo6WjRv3lx88803QgjV7vjvvvtOtGzZUpq36lWvzhOjVCpz/eNI76fs6Q+2bdsmnJ2dxd9//y299ttvv0nPWUtMTBTm5ubCyMhIZcJMHx8f4enpKYQQ4vDhw6JKlSpi3bp10uuJiYmiatWqQkdHR5owVgghLly4ILZu3SoiIiJU2sMgSvPu378vjIyMpHP74sUL0bNnTzF//nwhRNaM9J06dRK2trbS0G1mZqZYuHChMDExEUJkTYlhaGgolixZIg0F3717V1SsWFG4urpK06kIkTXsmz3cm40T+L7f2E9IpGFWVlaoWbMmTp8+jejoaMjlcilZdPz48QgODpbuBnz1afM6OjoqT4p/9VZoer8JIaQ7qrp37w49PT2cOnUKKSkpAICvvvoKc+fORVJSEkxNTeHj4wNbW1t4eHhI2xg2bBjOnz+PkJAQtGjRAtWrV8fy5ctx8OBBZGRkYPfu3ahWrRpcXFywcuVKAFnvHw8PD/Ts2RNlypSBUqmUhhl5917hiP/fqffqz05OTvD09JSGYPX09KCnp4f169cDyBr+b9iwIRISEqQbS3R0dNCyZUsIIbB9+3YYGRlh2LBh+OGHHzBy5EgcP34cgYGBaNasGerWratyR6exsTFMTU1zXCd4bt9fDKqItKBDhw7Q09PDs2fPALy8C0gul6tcrOVyOW9vLqZe/UOX/UdO/P8OL29vbxw7dkzKiZs2bRrOnDkjPay2e/fuePjwIZ48eSJtz9PTE+XLl5ceaLxo0SLY2dlh6NChcHZ2xoQJE9C1a1d0794dv/32G4CX7yuRNeoAuVzOP7gaoFQqVeYJyz6vADBw4EDs2bMHz549g46ODgYNGoTr168jNDQUhoaGaNKkCUxNTaVzBAAVKlRAq1atpGB48uTJmDRpEi5evIiOHTsiLi4O06dPh5OTE+7evYs7d+5I+wV4nShWiqiHjOiDpm4mbCo+1D1fLzcxMTFi+/bt4uLFi9J5P3HihChXrpz45ZdfhBBZD8PV1dUVq1evlrbr4uIiPdw2ezgnICBAVK5cWRrKS0xMFIcOHRLbtm2T1vv777+FmZmZ9PBb3u2Vd7nNUq9OTEyMmDNnjujevbtYtGiRePjwoRAi6xEwpqam0oOpY2JiRLVq1cTYsWOFEEI8ffpUdOnSRXoCQvZdgRs2bBAymUwkJydL+3j27JnKUN7OnTuFm5ubWLBggbQuFS/sqSLSguxvlZy5uHjJHj57dSLVV+/ke1VUVBR69OgBBwcHzJ07F3369MGQIUMAAI0bN4aNjQ1OnTqF6Oho6OnpoXXr1vj1118RHx8PAOjVqxe2bduG5ORkqRekX79+uH37ttTDZWpqilatWqFHjx5Se1xdXVGlShWcOXMGAHi3Vx68el6zP5uXLl3CtWvXkJGRkaP+uXPn0Lx5c+zYsQM1atTA6tWrMWLECJw4cQLm5uZo06YNNm3aBAAoXbo0evTogV9++QUAUKZMGXz88ce4cOEC4uLipLsC69evj+bNm0u9UABgZ2cHmUyG9PR0AFnzj3Xv3h2NGzcGwCHcYqmoozoiovfNhQsXxIgRI4S3t7fw9fUVY8eOFVeuXBFCvOw9+O6770SnTp2k8n///VeULVtWzJo1SwghxPTp00XdunXFmTNnhBBC7NmzRxgYGIgLFy4IIbIegiyTycSBAwdUtnv//v0c7Xl1DiSFQqH2WY/0dufOnRP9+vUTDg4OwsPDQ9SsWVOULVtWrFq1SqUHqU+fPmLAgAHSz6GhoaJmzZqiTZs2Qggh9u7dK/T09MStW7eEEEKEhIQIXV1dcezYMSFE1vunVKlSvHu3BOJXHCIq8YQQSEtLw5QpU+Ds7IxmzZohOjoanTt3hpGREbZs2YKWLVvi5MmTUu/B9u3b0bt3b9SqVQtHjhzB4sWL8ezZM9y+fRtA1lxDERER+PfffwEAH3/8MUxNTaXE8ypVqqBBgwZ49OgRgJe9Ek5OTmqfu5f9ulwuh5GR0Tv5vXwoHj58iFq1aqFRo0ZQKpVYtWoVNm7ciMWLF6N169YYMWIEZs+eDQC4d+8eIiIiMGLECJw+fRo9e/ZEgwYNoFAo0LZtWyiVSrRq1Qo2Njb4/fffAQBVqlSBp6cn5s+fDwBwcXHB4sWL0a5duxxt4TxhHzaZeP3TS0RUAqWlpcHIyAi+vr5YsmQJjI2NpUlX7969i379+kGhUGDbtm0QQsDHxwdmZma4fv06UlJS8PHHH2PAgAHw8vKShuS8vb1Rrlw5zJ49Gw4ODujWrRtu376Nf/75R3pGG2nf06dP0bRpU/To0UN6MLX4/3BgQkICpkyZgjVr1uD69euwt7dH6dKloaurCwMDA3z88cfo378/mjRpovJIKH9/fxw+fBjnzp2Dvr4+lixZgoMHD2LXrl1FdZj0PijKbjIiovdB9nxB7dq1E926dRNxcXFCCNUk8KNHjwqZTCbWrFkjFAqFqF+/vjSX1KvDcXFxceLmzZtCCCF++OEHUbp0abF//34hhBBRUVE5HknDmxq0T6lUismTJ4vy5csLIXIm91+6dEmYm5tLj/f59NNPRb169XLM/XX58mXx66+/CiGEOHPmjJDJZNJjgtTdMMCbCEoeDv8RUYkh/j/1wOuyh9a+/PJL/P333wgLCwOgmgTerFkzODg4IDg4GHK5HI0bN4a5uTkqV64sDcclJyfjf+3deXhN1/rA8e9JJEcmU0SEJKaIBBnMQtHUPFcMbWNIKmgN11AU7Q1RF6VIxVOklKjHlKchMStaxA1xlfQqEU3IoGJ6DG2oDOes3x/5ZTdHopc2RXk//+Q5e6+99jrn5Ml6s9a71/rss8+IiooCYOjQoUyePJkWLVoAhWuY2dvbm7RBHpX/c9RD60mVRqfT0bdvX3766SeOHz9eIrm/Tp06NG/enL179wKFU7cpKSlER0dz8+ZNDAYDhw4dYsGCBWRmZpKbm0urVq24ePEijRs3Bn5bLqX4BsbyEMHLR7a3F0K8NIqCpzt37lCpUqUST4T17dsXMzMzDh48iJeXl3a8oKCAcuXK4e/vzw8//ADAiBEjuHr1Kl26dGHChAkAbN26FQsLC2bNmoXRaKRSpUp88MEHJe4vT3WVjaLp2ccJTD08PGjRogWrVq2idevWJvtp6vV6qlWrxsmTJ4HCJzMvXLhAWFgYsbGxZGZmcvXqVQIDA+nfvz96vR6A2rVrm6xhVTz3TbycJKdKCPFCyMjIIDw8nPbt2xMQEEBeXh6WlpYmnZ7BYOCDDz4gMzOTL7/8EgsLC+36osApODiYlJQUtm/fjoODg8n1/fv310Y7oHBkavXq1Zw8eZLs7GzefPNN3n777RKdfPE6xB9XtChn8c/yzp07rF69mtTUVPr06UOrVq2wt7cvdRPyiIgIZs6cya1bt0qc8/b2pmXLlnz++efaudTUVBISErCwsGDQoEEyqij+JxmpEkK8EHJycjh27BgFBQUEBARgaWkJYBJQmZubc+LECby9vbGwsDDpeIt+jh07ltatW5OcnIyDgwM6nQ6lFEeOHCEuLo4lS5YAhYGSjY0NEyZMID8/3yRAe7hDl4Dqj1PFVot/OBCKi4tj3LhxODs74+HhwbRp03B2dmbPnj2lTr117dqVGTNmEBsbS0BAAFD4gEJERARXrlwhICDA5Do3Nzfc3Ny01waDwWQNMyEeJr8ZQoi/tYKCApRSNGrUiLZt23Lu3DmysrL46aefaNiwITNnzgR+y12qX78+V65cAUyDnaKOskWLFjg5ObFnzx7t3JEjR1i0aBHvvPMO77zzTolrLSwsSmw/JMpG8SDmhx9+YODAgSxevBilFAsXLmTixIkcO3aMtWvXsnfvXvbt28e6detMcpuK1KpVi44dO2oLdyYlJTFp0iS2bNnCRx99RI8ePUptQ1FdxffnFKI0MlIlhPhbKhp5KnrM3WAw0L59e+Lj4/n2228ZNmwYkyZNYt68eTg6OjJ27Fjy8/O5efMmbm5u2vXFFU0Bvv3226xfvx6dTse2bdvIzs5myJAhTJkyRcuneVjx3Czxx5Q2EpSbm8v69evJycnhxIkTVK5cmTZt2nD58mXu3LlDcHAw//nPf1i4cCFHjx7F1dUVe3v7UusvX748gYGBBAYGUrduXbKysujRoweffvopr7zyyiPbJYGUeFwSVAkh/pbMzc3Jzc3l8uXLvPrqq9SvX581a9bg5OTEgQMHGDZsGCNHjuT27dvMmzcPd3d3OnfuzK1bt7TrH56mKwqKRo0axdy5c9m/fz/jx49n5MiRJmsUib9G0edflA8HhUnkX3zxBcnJyQwePJhly5ZhZmbGjh07gMKRR0tLS7p168amTZto167d7wa3LVu2ZNSoUTRt2pSQkBCTspL7Jv4s+SshhHgulJZYXDSlVhTQFHV6RqORjRs3MmXKFEaMGMH48eN54403cHV1xdfXl2+++YbTp0/TpEkT3n//fdLS0pg1axa5ubm0aNGC7OxsoGSuU1HdLi4u3L59m4oVK2rnJJ+m7BTPkypu8+bNrFmzBp1OR58+fejZsye1a9cmKCiIqVOn4uPjo13j4eFB+fLl6dixI5s2bTIJenft2oWPjw/Ozs4lAqW6deuycuVK7XXRlK25ubkEVOJPk78OQohnpvi6PsU72KJjOp1O6yyvXbtmslVL5cqVsbCwIC4ujiFDhuDi4gJA+/btUUpx8OBBrb758+fTtGlTJkyYwPHjx2nUqJH2JNnDitYbqlixIgaDwaTTlYCqbJQWnM6ZM4d//vOftG7dmtdee43NmzcTEhICQOfOnalTp462fhgUjlA1b96clJQUEhMTtU2J4+PjWbJkCUePHtXuVZri36tM24qyIiNVQoinrmhUqujx+KtXr7J8+XLu3bvH4sWLtQ43KyuL8PBwdu7cSeXKlenatSuBgYF4eHjQuHFjPD09MRqNODk5aU/gtWrVCldXVxISErRjVapU4ZNPPiE9PZ3du3fTs2dPzMzMSs2rgt86Yuls/7xH5a6tWrUKg8HAuHHjSExMJDIykh07dtCkSROgMGgaMGAA27Zto1+/fjRu3JiUlBQyMzNxdXUFYPr06YSGhtK7d2/8/Py4dOkS2dnZBAcH/26OFMh3K/4a8m+XEOKpKwpoVq5cibe3N87Ozhw4cAB/f3+tTFJSEu+++y4pKSnMnTuXkJAQjh07xsSJEwGoXr063bt3JzExEYPBoC2RYGdnR/Pmzbl27RrHjh0DCnN0rKysmDdvHi1btuTq1auAdKxPQ2mf8Z07d4iOjiYjIwOAn3/+mXr16mFra8v06dNxdnZm7NixBAcH07BhQwC6detGVlaWtkCn0WikTp06fP7553z99de0atWKadOmcePGDcLDw3F2dn56b1KI/ydBlRDiqdu6dSsODg6EhYUREhJCRkYGCQkJ9OrVSytTqVIlunfvzq5duxg4cCCBgYE0btyYr7/+mtTUVPR6Pa1bt6ZChQps2LABKBwBgcItZYxGIzExMQDaFKK3tzd5eXl4e3s/5Xf84lNKaZ9/cRkZGQwYMIAbN25ox6pWrcqVK1dwd3cH4NKlS6SmpuLl5cWZM2dYuHAh58+fZ82aNTRo0AAoDKr0ej3x8fEm+VjW1tY0b96cmTNnEhQURLly5TAYDKUuqSDEX02CKiHEU6fX66lbty6hoaFMmDCBGjVqlChTu3Ztxo0bx7lz5xg4cCAuLi7Ex8dToUIFli1bBhQmHbdr147169cDv42KNGnShNatW9O4cWOTBPj79+9z+/btRy6LIB7t4X3tHlY8/+3ChQtajpO9vT0JCQlMnjyZtLQ0rXzNmjVJSUkBwMvLi6pVqzJ+/Hh27dpFYGAgFStW5P79+8ydO5dr165RrVo1XF1dyc7O5vr166W2QdaTEs+a/NYJIZ66V155hXr16hEfHw8Udsjfffcdw4YNIyQkRHs67/bt24SFhQFw4MABEhMTGT58uDYC5eDgQPfu3Tl48CDZ2dnaMgnm5uaEh4czcuRIrXNNTk7GxcUFR0dH2rVr9/Tf9N9cUXL5zZs32bdvH+np6Sbn7969S1hYGDVr1qR79+706tWLqKgobG1tiY6OJjMzkxkzZgCF36uNjY22EbWfnx9+fn7ExMTw1VdfkZ2dTUZGBosWLWLHjh1a8LVs2TI2b96Mo6NjqW2UQEo8c0oIIZ6Bjz/+WDVv3lwNHTpU1a9fXzk4OKh+/fqpQ4cOqYKCAqWUUocPH1Y6nU6lpaUppZT69ddfVZcuXZROp1MHDhxQSil1+vRpNWTIEHX27NkS9zAYDMpgMCillMrLy1N79ux5Su/uxWI0GtXGjRtVs2bNVLVq1VTbtm3VypUr1S+//KKV+eyzz1SzZs3Utm3bVFpamlqwYIFyd3dXcXFxSimlEhMTlZWVlVq+fLlSSikfHx+1dOlS7fqbN2+q4cOHq3r16ikfHx9lbW2t2rRpo7Zv366MRqNJe4q+UyGeN/L0nxDimXjttdeIjo7mm2++ITw8nA4dOlCtWjWTMlZWVuj1eo4fP46TkxNxcXG4uLjg6elJXFwcHTt2xNfXV5v+e1jxkYty5crRrVu3v/Q9vajCw8NZt24db775JgMGDCAnJ4cqVapga2sLwNWrV1m5ciVLly7F39+f+/fv4+DgwI8//si+ffvw9/enZcuWfPjhh0RGRmJnZ0fjxo216cCCggLs7e2JjIzk+vXrJCYm4ufnR/Xq1Uttj4xIieeVTimlnnUjhBAvn4KCAkaNGsW1a9eIiYmhfPnyGAwGbakFKNyiZPLkyezcuZO7d+9ia2vLkiVL6N69u9ahF3nU8gjizzl79iyvv/46o0eP5r333iu1THZ2No0aNWLBggXExsZy5MgR6tevz7Bhw3jrrbe06bqff/6ZFStWEBoaSr169ejfvz//+te/Hnlvo9GIUkq+V/G3IeG+EOKZKFeuHK1atdJydOC3Va2vX7/Orl270Ov1LFq0iKioKGJiYsjKymLgwIFaQFX8f0LpeP8aFhYWpKen06FDB+3Y9evX+eWXX7TX9+/fp0GDBkycOJGWLVty+PBhTp06xcSJE3F0dCQnJweAChUqMG3aNDp27EhKSgq+vr486v969f9P+Mn3Kv5OZKRKCPHMJCcnM3HiRJo2bcr8+fPZtGkTkZGRHD16lLZt27J7925sbGxMrpERqafP09MTS0tLGjZsSHZ2NtbW1vz6669YWVkxffp02rRpw6hRozh58iT//e9/tevy8/P59NNPcXd3p3fv3kDh1N21a9ewt7eX/RTFC0eCKiHEMzV69GiioqIwGAxUqVKFoKAg/vGPf5RYvFHJZrfPzIULF9i0aRMnTpzA29ubihUrcu/ePQ4fPsz58+e5dOkS58+fp1OnTnTq1Ik33ngDW1tboqKiOHfuHGFhYQQEBJSoVwJk8aKRoEoI8Uzt3LmTU6dO0bNnT5o1a6YdL21PQPHsFQ9uk5KS6NWrFwsXLiQwMJAdO3awYcMGUlNTuXLlCl27dmXq1KnaquhCvOgkqBJCPFcKCgq03CrxfIuKimLq1Kls2LCBLl26aMczMjKoVavWM2yZEM+GBFVCiGdOKWWy9Yh4Pl28eBEbGxsMBgMHDhxg9erV+Pr6EhERoZUpvoJ9QUEBZmZm8r2Kl4YEVUIIIf6nnJwcevXqhV6v58yZM+j1ekaNGsXYsWOpUKHCs26eEM8FCaqEEEI8lkOHDnHx4kVatGiBl5fXs26OEM8dCaqEEEL8IZL/JoQpCaqEEEI8tqIuQwIpIUqSldeEEEI8NgmmhHg0eSRDCCGEEKIMSFAlhBBCCFEGJKgSQgghhCgDElQJIYQQQpQBCaqEEEIIIcqABFVCCCGEEGVAgiohhACCg4N5/fXXtdevvvoqEydOfOrtOHToEDqdjjt37jyyjE6nIzY29rHrDAsLw9fX90+1Kz09HZ1OR1JS0p+qR4gXmQRVQojnVnBwMDqdDp1Oh6WlJW5ubnz00UcUFBT85ffeunUrc+bMeayyjxMICSFefLL4pxDiudatWzfWrl1Lbm4uu3fvZuzYsVhYWDBjxowSZfPy8rC0tCyT+1apUqVM6hFCvDxkpEoI8VzT6/VUr16dWrVqMXr0aDp16sT27duB36bs5s6dS40aNWjQoAEAWVlZDBo0iEqVKlGlShX69u1Lenq6VqfBYOC9996jUqVK2Nvb8/777/Pwjl0PT//l5uYybdo0XFxc0Ov1uLm58cUXX5Ceno6/vz8AlStXRqfTERwcDIDRaGT+/PnUqVMHKysrfHx8+Oqrr0zus3v3btzd3bGyssLf39+knY9r2rRpuLu7Y21tTd26dQkNDSU/P79EucjISFxcXLC2tmbQoEHcvXvX5Pzq1avx9PSkfPnyeHh4sHz58iduixAvMwmqhBB/K1ZWVuTl5WmvDx48SEpKCvv372fnzp3k5+fTtWtX7OzsiI+P59///je2trZ069ZNu27x4sVERUWxZs0ajh49yq1bt9i2bdvv3nfYsGFs2rSJiIgIkpOTiYyMxNbWFhcXF2JiYgBISUkhOzubpUuXAjB//ny+/PJLVq5cydmzZ5k0aRJDhgzh8OHDQGHwFxAQQO/evUlKSmLEiBFMnz79iT8TOzs7oqKiOHfuHEuXLmXVqlWEh4eblElNTSU6OpodO3awd+9eTp8+zZgxY7TzGzZsYObMmcydO5fk5GTmzZtHaGgo69ate+L2CPHSUkII8ZwKCgpSffv2VUopZTQa1f79+5Ver1dTpkzRzjs6Oqrc3FztmvXr16sGDRooo9GoHcvNzVVWVlZq3759SimlnJyc1MKFC7Xz+fn5ytnZWbuXUkp16NBBTZgwQSmlVEpKigLU/v37S23nt99+qwB1+/Zt7diDBw+UtbW1SkhIMCkbEhKi3nrrLaWUUjNmzFANGzY0OT9t2rQSdT0MUNu2bXvk+U8++UQ1a9ZMez1r1ixlbm6uLl++rB3bs2ePMjMzU9nZ2UopperVq6c2btxoUs+cOXOUn5+fUkqpS5cuKUCdPn36kfcV4mUnOVVCiOfazp07sbW1JT8/H6PRSGBgIGFhYdp5Ly8vkzyq77//ntTUVOzs7EzqefDgAWlpady9e5fs7GxatWqlnStXrhzNmzcvMQVYJCkpCXNzczp06PDY7U5NTeX+/ft07tzZ5HheXh5NmjQBIDk52aQdAH5+fo99jyJbtmwhIiKCtLQ0cnJyKCgooEKFCiZlXF1dqVmzpsl9jEYjKSkp2NnZkZaWRkhICCNHjtTKFBQUULFixSdujxAvKwmqhBDPNX9/f1asWIGlpSU1atSgXDnTP1s2NjYmr3NycmjWrBkbNmwoUZeDg8MfaoOVldUTX5OTkwPArl27TIIZKMwTKyvHjh1j8ODBzJ49m65du1KxYkU2b97M4sWLn7itq1atKhHkmZubl1lbhXjRSVAlhHiu2djY4Obm9tjlmzZtypYtW6hWrVqJ0ZoiTk5OJCYm0r59e6BwROa7776jadOmpZb38vLCaDRy+PBhOnXqVOJ80UiZwWDQjjVs2BC9Xk9mZuYjR7g8PT21pPsix48f/99vspiEhARq1arFhx9+qB3LyMgoUS4zM5MrV65Qo0YN7T5mZmY0aNAAR0dHatSowcWLFxk8ePAT3V8I8RtJVBdCvFAGDx5M1apV6du3L/Hx8Vy6dIlDhw4xfvx4Ll++DMCECRP4+OOPiY2N5fz584wZM+Z315iqXbs2QUFBDB8+nNjYWK3O6OhoAGrVqoVOp2Pnzp3cuHGDnJwc7OzsmDJlCpMmTWLdunWkpaVx6tQpli1bpiV/v/vuu/z4449MnTqVlJQUNm7cSFRU1BO93/r165OZmcnmzZtJS0sjIiKi1KT78uXLExQUxPfff098fDzjx49n0KBBVK9eHYDZs2czf/58IiIiuHDhAmfOnGHt2rUsWbLkidojxMtMgiohxAvF2tqaI0eO4OrqSkBAAJ6enoSEhPDgwQNt5Gry5MkMHTqUoKAg/Pz8sLOzo1+/fr9b74oVKxgwYABjxozBw8ODkSNHcu/ePQBq1qzJ7NmzmT59Oo6OjowbNw6AOXPmEBoayvz58/H09KRbt27s2rWLOnXqAIV5TjExMcTGxuLj48PKlSuZN2/eE73fPn36MGnSJMaNG4evry8JCQmEhoaWKOfm5kZAQAA9evSgS5cueHt7myyZMGLECFavXs3atWvx8vKiQ4cOREVFaW0VQvxvOvWozEwhhBBCCPHYZKRKCCGEEKIMSFAlhBBCCFEGJKgSQgghhCgDElQJIYQQQpQBCaqEEEIIIcqABFVCCCGEEGVAgiohhBBCiDIgQZUQQgghRBmQoEoIIYQQogxIUCWEEEIIUQYkqBJCCCGEKAMSVAkhhBBClIH/A9M7UBCEhH3SAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for model_name in [\n",
        "    \"Logistic Regression\",\n",
        "    \"Random Forest\",\n",
        "]:\n",
        "    print(f\"\\n{model_name}: per-class hold-out performance\")\n",
        "    display(model_evaluations[model_name][\"report\"])\n",
        "\n",
        "    plot_confusion_matrices(\n",
        "        y_test,\n",
        "        model_evaluations[model_name][\"predictions\"],\n",
        "        PRIMARY_CLASS_ORDER,\n",
        "        model_name,\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5228d610",
      "metadata": {
        "id": "5228d610"
      },
      "source": [
        "The cross-validation-selected model is called a **candidate**, not automatically a deployable champion. Test performance is used to assess whether the apparent validation gain generalises. A model that fails to exceed the naïve baseline or cannot identify severe delinquency is rejected for deployment even when its ROC-AUC or training score appears acceptable."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9a48c57",
      "metadata": {
        "id": "f9a48c57"
      },
      "source": [
        "## 13. ROC, precision–recall and calibration analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "5cda2422",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5cda2422",
        "outputId": "11215a44-4179-46c2-bd4e-cfd0c9d17840"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def plot_multiclass_roc(\n",
        "    model_name,\n",
        "    evaluation,\n",
        "    y_true,\n",
        "):\n",
        "    probability_frame = evaluation[\"probabilities\"]\n",
        "    model_classes = probability_frame.columns.to_numpy()\n",
        "    y_binary = label_binarize(\n",
        "        y_true,\n",
        "        classes=model_classes,\n",
        "    )\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "\n",
        "    for class_index, class_name in enumerate(model_classes):\n",
        "        false_positive_rate, true_positive_rate, _ = roc_curve(\n",
        "            y_binary[:, class_index],\n",
        "            probability_frame.iloc[:, class_index],\n",
        "        )\n",
        "        class_auc = roc_auc_score(\n",
        "            y_binary[:, class_index],\n",
        "            probability_frame.iloc[:, class_index],\n",
        "        )\n",
        "        plt.plot(\n",
        "            false_positive_rate,\n",
        "            true_positive_rate,\n",
        "            label=f\"{class_name} (AUC={class_auc:.3f})\",\n",
        "        )\n",
        "\n",
        "    plt.plot(\n",
        "        [0, 1],\n",
        "        [0, 1],\n",
        "        linestyle=\"--\",\n",
        "        label=\"No-skill reference\",\n",
        "    )\n",
        "    plt.xlabel(\"False-positive rate\")\n",
        "    plt.ylabel(\"True-positive rate\")\n",
        "    plt.title(f\"{model_name}: one-vs-rest ROC curves\")\n",
        "    plt.legend()\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def plot_multiclass_precision_recall(\n",
        "    model_name,\n",
        "    evaluation,\n",
        "    y_true,\n",
        "):\n",
        "    probability_frame = evaluation[\"probabilities\"]\n",
        "    model_classes = probability_frame.columns.to_numpy()\n",
        "    y_binary = label_binarize(\n",
        "        y_true,\n",
        "        classes=model_classes,\n",
        "    )\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "\n",
        "    for class_index, class_name in enumerate(model_classes):\n",
        "        precision, recall, _ = precision_recall_curve(\n",
        "            y_binary[:, class_index],\n",
        "            probability_frame.iloc[:, class_index],\n",
        "        )\n",
        "        average_precision = average_precision_score(\n",
        "            y_binary[:, class_index],\n",
        "            probability_frame.iloc[:, class_index],\n",
        "        )\n",
        "        plt.plot(\n",
        "            recall,\n",
        "            precision,\n",
        "            label=f\"{class_name} (AP={average_precision:.3f})\",\n",
        "        )\n",
        "\n",
        "    plt.xlabel(\"Recall\")\n",
        "    plt.ylabel(\"Precision\")\n",
        "    plt.title(\n",
        "        f\"{model_name}: one-vs-rest precision–recall curves\"\n",
        "    )\n",
        "    plt.legend()\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "for model_name in [\n",
        "    \"Logistic Regression\",\n",
        "    \"Random Forest\",\n",
        "]:\n",
        "    plot_multiclass_roc(\n",
        "        model_name,\n",
        "        model_evaluations[model_name],\n",
        "        y_test,\n",
        "    )\n",
        "    plot_multiclass_precision_recall(\n",
        "        model_name,\n",
        "        model_evaluations[model_name],\n",
        "        y_test,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "91a2b8bd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 997
        },
        "id": "91a2b8bd",
        "outputId": "3f5c601c-55d7-4cfa-fa88-e9f3e4378e19"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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wePFivvrqKy5cuMDWrVsZM2aMzTkAGDhwICEhIcnu7+4/hMmdR7CMc/L390+yfeKdV/PmzaNr16427z3Mz8Hx48dp0qQJ5cuX59NPPyUwMBA3NzdWrlzJZ599Zs1lMplYsGABO3bsYPny5axevZpu3boxYcIEduzYQe7cufH19WXfvn2sXr2aX3/9lV9//ZWZM2fSuXNnZs+eneKMjuRMK3d/v8Dx3y2OsHdSzIz0u0Eco8JJnC7xF1bjxo2ZPHmydW6UBQsWULJkSRYtWmTzyyi5ywL2KF68OPBfD8Gdjhw5kmTddevWcePGDZtep8OHD9vsy1kSj393brBkLFiw4ANvky9VqhSGYRAUFGT91/r9VK5cmcqVK/PBBx+wbds26tWrx9SpUxk1ahRg/x+MBwkICKB379707t2bixcv8sgjjzB69GhatmxpHXDu7e1N06ZNH7ivDh06MHv2bNavX09YWBiGYVgv0wHWXoucOXPatb/kJGby9fW97z5CQkKSvRSWuP2BAwccyrB8+XJiYmJYtmyZTe/FvS4NPvbYYzz22GOMHj2aH3/8kZdeeom5c+fSo0cPANzc3GjdujWtW7fGbDbTu3dvpk2bxpAhQ5IUj3dmX716NVevXr1nr5OjOe1hNps5ceKEzc/t33//DWDXEwDs/d3iyM908eLFMZvNHD161NozDXDhwgWuX7/u9N8Zkno0xkkyhEaNGlGrVi0mTpzI7du3gf/+RXbnv8B27tzJ9u3bU3SMgIAAqlWrxuzZs20umaxdu5ZDhw7ZrNuqVSsSEhKYPHmyTftnn32GyWSiZcuWKcqQWu78LNevX7e2HzhwgDVr1tCqVasH7qNt27a4uroyYsSIJP/KNQyDK1euAJa7G+Pj423er1y5Mi4uLja3WXt5edlkuZM90xEkJCQkuZTl6+tL4cKFrcepUaMGpUqV4pNPPiEqKirJPi5dumSz3LRpU/Lnz8+8efOYN28etWrVsrkU4+vrS6NGjZg2bRrh4eEP3F9yQkJC8Pb2ZsyYMcmOIUrcR0BAAE2bNrV5ATzyyCMEBQUxceLEJOfvfr0Pyf3/ERERwcyZM23Wu3btWpL9JPbWJZ7XxO91IhcXF+vdY3ffSn+n5557DsMwrJNEJpfd3pyOuvP/TcMwmDx5Mjlz5rRelr0fe3+35MqVC+CeP9d3Svx/buLEiTbtn376KQBPPvnkA/chmYN6nCTDePvtt3n++eeZNWsWvXr14qmnnmLRokU8++yzPPnkk5w8eZKpU6cSHByc7B9Ne4wdO5Ynn3ySxx9/nG7dunH16lW++OILKlasaLPP1q1b07hxYwYPHsypU6eoWrUqa9asYenSpfTv39/mVntn+fjjj2nZsiV16tShe/fu1ukIfHx8rHPN3E+pUqUYNWoU7733HqdOnaJNmzbkyZOHkydPsnjxYl555RUGDhzIb7/9Rt++fXn++ecpW7Ys8fHxfP/997i6uvLcc89Z91ejRg3WrVvHp59+SuHChQkKCrIOhrdnOoIbN25QtGhR2rVrR9WqVcmdOzfr1q3jjz/+YMKECYDlD/o333xDy5YtqVixIl27dqVIkSKcPXuWDRs24O3tzfLly637zJkzJ23btmXu3LncvHmTTz75JMlxv/zySx5//HEqV65Mz549KVmyJBcuXGD79u38+++//PXXX/c9j97e3kyZMoVOnTrxyCOP8MILL1CoUCFOnz7NL7/8Qr169ZIU4HdycXFhypQptG7dmmrVqtG1a1cCAgI4fPgwBw8eZPXq1clu17x5c2sv0auvvkpUVBTTp0/H19fXpgicPXs2X331Fc8++yylSpXixo0bTJ8+HW9vb+sf+x49enD16lWeeOIJihYtyj///MMXX3xBtWrVbHpP7ta4cWM6derEpEmTOHr0KC1atMBsNrN582YaN25M37597c7pCA8PD1atWkVoaCi1a9fm119/5ZdffuH9999PMhA8Ofb+bvH09CQ4OJh58+ZRtmxZ8ufPT6VKlZIdj1a1alVCQ0P5+uuvuX79Og0bNmTXrl3Mnj2bNm3a0Lhx4xR9VsmA0vUePsn2Em/pTu7W6ISEBKNUqVJGqVKljPj4eMNsNhtjxowxihcvbri7uxvVq1c3VqxYYYSGhtpMHZB42/HHH3+cZJ8kcyvxwoULjQoVKhju7u5GcHCwsWjRoiT7NAzLbeZvvvmmUbhwYSNnzpxGmTJljI8//tjmNuvEY9x9C/29Mm3YsMGuW/vvd57utG7dOqNevXqGp6en4e3tbbRu3do4dOiQzTqJt00n3qZ9t4ULFxqPP/644eXlZXh5eRnly5c3+vTpYxw5csQwDMM4ceKE0a1bN6NUqVKGh4eHkT9/fqNx48bGunXrbPZz+PBho0GDBoanp6cB2ExNgB3TEcTExBhvv/22UbVqVSNPnjyGl5eXUbVqVeOrr75Ksu7evXuNtm3bGgUKFDDc3d2N4sWLG+3btzfWr1+fZN21a9cagGEymWymu7jT8ePHjc6dOxv+/v5Gzpw5jSJFihhPPfWUsWDBAus6D/qebNiwwQgJCTF8fHwMDw8Po1SpUkaXLl2MP//8876fO9GWLVuMZs2aWT97lSpVjC+++ML6fnLTESxbtsyoUqWK4eHhYZQoUcIYN26cMWPGDJspL/bs2WN07NjRKFasmOHu7m74+voaTz31lE2uBQsWGM2bNzd8fX0NNzc3o1ixYsarr75qhIeHPzB3fHy88fHHHxvly5c33NzcjEKFChktW7Y0du/e7VBOw7B/OgIvLy/j+PHjRvPmzY1cuXIZfn5+xrBhw4yEhIQk2yb3e8He3y2GYRjbtm0zatSoYbi5udn8Pknu+xEXF2eMGDHCCAoKMnLmzGkEBgYa7733ns1UF4ZhmY7gySefTJLr7s8vGZPJMDQSTURERMQeGuMkIiIiYicVTiIiIiJ2UuEkIiIiYicVTiIiIiJ2UuEkIiIiYicVTiIiIiJ2ynYTYJrNZs6dO0eePHlS7RERIiIiknkZhsGNGzcoXLgwLi7371PKdoXTuXPnkjyxXkREROTMmTMPfHB5tiucEh/YeubMGby9vZ2cRkRERJwtMjKSwMBAm4e630u2K5wSL895e3urcBIREREre4bwaHC4iIiIiJ1UOImIiIjYSYWTiIiIiJ2y3RgneyUkJBAXF+fsGCJ2y5kzJ66urs6OISKSpalwuothGJw/f57r1687O4qIw/LmzYu/v7/mKBMRSSMqnO6SWDT5+vqSK1cu/QGSTMEwDKKjo7l48SIAAQEBTk4kIpI1qXC6Q0JCgrVoKlCggLPjiDjE09MTgIsXL+Lr66vLdiIiaUCDw++QOKYpV65cTk4ikjKJP7sanycikjZUOCVDl+cks9LProhI2lLhJCIiImInFU6SKkwmE0uWLHF2DBERkTTl1MJp06ZNtG7dmsKFC9v9h3fjxo088sgjuLu7U7p0aWbNmpXmOVMiwWyw/fgVlu47y/bjV0gwG2l6vC5dumAymTCZTOTMmZOgoCDeeecdbt++nabHdbY7P/edr2PHjjk1U5s2bZx2fBERSTtOvavu5s2bVK1alW7dutG2bdsHrn/y5EmefPJJevXqxZw5c1i/fj09evQgICCAkJCQdEhsn1UHwhmx/BDhEf8VLQE+HgxrHUyLSml3m3iLFi2YOXMmcXFx7N69m9DQUEwmE+PGjUuzY2YEiZ/7ToUKFUrRvmJjY3Fzc0uNWCIikgU5tcepZcuWjBo1imeffdau9adOnUpQUBATJkygQoUK9O3bl3bt2vHZZ5+lcVL7rToQzms/7LEpmgDOR9zmtR/2sOpAeJod293dHX9/fwIDA2nTpg1NmzZl7dq11vevXLlCx44dKVKkCLly5aJy5cr89NNPNvto1KgRr7/+Ou+88w758+fH39+f4cOH26xz9OhRGjRogIeHB8HBwTbHSLR//36eeOIJPD09KVCgAK+88gpRUVHW9xN7ZcaMGYOfnx958+Zl5MiRxMfH8/bbb5M/f36KFi2apCC63+e+85V4K/7vv/9OrVq1cHd3JyAggEGDBhEfH2/zefv27Uv//v0pWLCgtQA/cOAALVu2JHfu3Pj5+dGpUycuX75s3W7BggVUrlzZ+vmaNm3KzZs3GT58OLNnz2bp0qXW3q+NGzc+8DOIiEjmkKnGOG3fvp2mTZvatIWEhLB9+/Y0O6ZhGETHxtv1unE7jmHLDpLcRbnEtuHLDnHjdpxd+zOMlF/eO3DgANu2bbPpPbl9+zY1atTgl19+4cCBA7zyyit06tSJXbt22Ww7e/ZsvLy82LlzJ+PHj2fkyJHW4shsNtO2bVvc3NzYuXMnU6dO5d1337XZ/ubNm4SEhJAvXz7++OMPfv75Z9atW0ffvn1t1vvtt984d+4cmzZt4tNPP2XYsGE89dRT5MuXj507d9KrVy9effVV/v333xSdg7Nnz9KqVSseffRR/vrrL6ZMmcK3337LqFGjknxeNzc3tm7dytSpU7l+/TpPPPEE1atX588//2TVqlVcuHCB9u3bAxAeHk7Hjh3p1q0bYWFhbNy4kbZt22IYBgMHDqR9+/a0aNGC8PBwwsPDqVu3boryi4jI/zMnODuBVaaaAPP8+fP4+fnZtPn5+REZGcmtW7esEwDeKSYmhpiYGOtyZGSkQ8e8FZdA8NDVKQt8FwM4H3mbysPX2LX+oZEh5HKz/1u0YsUKcufOTXx8PDExMbi4uDB58mTr+0WKFGHgwIHW5X79+rF69Wrmz59PrVq1rO1VqlRh2LBhAJQpU4bJkyezfv16mjVrxrp16zh8+DCrV6+mcOHCAIwZM4aWLVtat//xxx+5ffs23333HV5eXgBMnjyZ1q1bM27cOOv3MH/+/EyaNAkXFxfKlSvH+PHjiY6O5v333wfgvffe46OPPmLLli288MILD/zciVq2bMnPP//MV199RWBgIJMnT8ZkMlG+fHnOnTvHu+++y9ChQ3FxcbF+xvHjx1u3HzVqFNWrV2fMmDHWthkzZhAYGMjff/9NVFQU8fHxtG3bluLFiwNQuXJl67qenp7ExMTg7+9//2+YiIg82KW/YX5naDEGSj3h7DSZq3BKibFjxzJixAhnx0gXjRs3ZsqUKdy8eZPPPvuMHDly8Nxzz1nfT0hIYMyYMcyfP5+zZ88SGxtLTExMkgk/q1SpYrMcEBBgfZRHWFgYgYGB1qIJoE6dOjbrh4WFUbVqVWvRBFCvXj3MZjNHjhyxFk4VK1a0Fi9gKYIrVapkXXZ1daVAgQLWYz/ocydKPG5YWBh16tSxmduoXr16REVF8e+//1KsWDEAatSoYbO/v/76iw0bNtgUY4mOHz9O8+bNadKkCZUrVyYkJITmzZvTrl078uXLd9+cIiKSQhFnYO1QCGoELs69WJapCid/f38uXLhg03bhwgW8vb2T7W0CS6/FgAEDrMuRkZEEBgbafUzPnK4cGmnfwPNdJ6/SZeYfD1xvVtdHqRWU365jO8LLy4vSpUsDlh6SqlWr8u2339K9e3cAPv74Yz7//HMmTpxI5cqV8fLyon///sTGxtrsJ2fOnDbLJpMJs9nsUBZ7JHeclBz7zs+dEncWeABRUVHW3rG7BQQE4Orqytq1a9m2bRtr1qzhiy++YPDgwezcuZOgoKAU5xARkf9nGJD4j95CZaHjT1CogtOLJshkY5zq1KnD+vXrbdrWrl2bpMfjTu7u7nh7e9u8HGEymcjllsOuV/0yhQjw8eBeczebsNxdV79MIbv29zCzQLu4uPD+++/zwQcfcOvWLQC2bt3KM888w8svv0zVqlUpWbIkf//9t0P7rVChAmfOnCE8/L9B7jt27Eiyzl9//cXNmzetbVu3brVekksvFSpUYPv27TZjxbZu3UqePHkoWrToPbd75JFHOHjwICVKlKB06dI2r8Qiy2QyUa9ePUaMGMHevXtxc3Nj8eLFALi5uZGQkHGux4uIZCo3zsPs1nBy839tQQ0gd8rulk5tTi2coqKi2LdvH/v27QMs0w3s27eP06dPA5beos6dO1vX79WrFydOnOCdd97h8OHDfPXVV8yfP58333zTGfGTcHUxMax1MECS4ilxeVjrYFxd0uexGM8//zyurq58+eWXgGUsT2JPSVhYGK+++mqSHrwHadq0KWXLliU0NJS//vqLzZs3M3jwYJt1XnrpJTw8PAgNDeXAgQNs2LCBfv360alTpyRj1NJS7969OXPmDP369ePw4cMsXbqUYcOGMWDAAJtLhHfr06cPV69epWPHjvzxxx8cP36c1atX07VrVxISEti5cydjxozhzz//5PTp0yxatIhLly5RoUIFAEqUKMH//vc/jhw5wuXLl/XcOBERe/2zHaY1gFObYfkbGWpQeCKnFk5//vkn1atXp3r16gAMGDCA6tWrM3ToUMBy91JiEQUQFBTEL7/8wtq1a6latSoTJkzgm2++yVBzOLWoFMCUlx/B38fDpt3fx4MpLz+SpvM43S1Hjhz07duX8ePHc/PmTT744AMeeeQRQkJCaNSoEf7+/g5P1Oji4sLixYu5desWtWrVokePHowePdpmnVy5crF69WquXr3Ko48+Srt27WjSpInNQPX0UKRIEVauXMmuXbuoWrUqvXr1onv37nzwwQf33a5w4cJs3bqVhIQEmjdvTuXKlenfvz958+bFxcUFb29vNm3aRKtWrShbtiwffPABEyZMsA6Q79mzJ+XKlaNmzZoUKlSIrVu3psfHFRHJvAwDdkyB2U9B1AXLZbkX54OLY0NW0oPJeJh73jOhyMhIfHx8iIiISHLZ7vbt25w8eZKgoCA8PDzusQf7JJgNdp28ysUbt/HN40GtoPzp1tMk2Vdq/gyLiKSLmChY1g8OLrIsV2oHrT8H96Q36KSV+9UGd8tUg8MzE1cXE3VKFXB2DBERkYwr+irMbAmXDoNLDmg+Gmq/+t/A8AxIhZOIiIg4h2c+8K8Mt65D+9lQ7DFnJ3ogFU4iIiKSfhLiIf625VKcyWS5LBcTBXnS7+ahh5GppiMQERGRTCzqInzfBha9Aolz9Ll5ZZqiCdTjJCIiIunhzC6YHwo3zkFOL7hyFAql39x+qUU9TiIiIpJ2DAN2fg0zW1mKpoJl4ZUNmbJoAvU4iYiISFqJvQnL+8P++Zbl4GfgmS/BPY9TYz0MFU4iIiKSNn7uAkfXgMkVmo2EOn0y9FQD9lDhJCIiImmj4btwMQyenQolHnd2mlShMU7yQMOHD8fPzw+TycSSJUucHcdus2bNIm/evNbl4cOHU61aNetyly5dHH7kzMMqUaIEEydOTNdjioikG3MCnNv333LRmtBvT5YpmkCFU5bRpUsXTCYTJpMJNzc3SpcuzciRI4mPj3+o/YaFhTFixAimTZtGeHi49XlsD+PuAia9DBw4kPXr16fLse4u2hL98ccfvPLKK+mSQUQkXd28Aj+0hRkhEP6//9pzuDkvUxrQpbospEWLFsycOZOYmBhWrlxJnz59yJkzJ++9957D+0pISMBkMnH8+HEAnnnmGUyZ/Lp07ty5yZ374Z59FBsbi5tbyn8JFCpU6KGOLyKSIf27G+Z3hsh/IWcuiDgDAVWcnSpNqMcpC3F3d8ff35/ixYvz2muv0bRpU5YtWwZATEwMAwcOpEiRInh5eVG7dm02btxo3Taxh2TZsmUEBwfj7u5Ot27daN26NQAuLi42hdM333xDhQoV8PDwoHz58nz11Vc2Wf799186duxI/vz58fLyombNmuzcuZNZs2YxYsQI/vrrL2sP2axZs+75mWbMmEHFihVxd3cnICCAvn37Wt/79NNPqVy5Ml5eXgQGBtK7d2+ioqLuua979XSNGDGCQoUK4e3tTa9evYiNjbW+16hRI/r27Uv//v0pWLAgISEhDzz2xo0b6dq1KxEREdbPOHz4cCDppbrTp0/zzDPPkDt3bry9vWnfvj0XLlxIkvn777+nRIkS+Pj48MILL3Djxo17fk4RkXRjGPDHtzCzhaVoyl8KeqyH8k86O1maUY+TvWJv3vs9kyvk9LBzXRfI6fngdd28HMuXDE9PT65cuQJA3759OXToEHPnzqVw4cIsXryYFi1asH//fsqUKQNAdHQ048aN45tvvqFAgQIEBATQqFEjunbtSnh4uHW/c+bMYejQoUyePJnq1auzd+9eevbsiZeXF6GhoURFRdGwYUOKFCnCsmXL8Pf3Z8+ePZjNZjp06MCBAwdYtWoV69atA8DHxyfZ/FOmTGHAgAF89NFHtGzZkoiICLZu3Wp938XFhUmTJhEUFMSJEyfo3bs377zzTpIi7n7Wr1+Ph4cHGzdu5NSpU3Tt2pUCBQowevRo6zqzZ8/mtddes/vYdevWZeLEiQwdOpQjR44AJNvTZTabrUXT77//Tnx8PH369KFDhw42Re3x48dZsmQJK1as4Nq1a7Rv356PPvrIJqOISLqLuwUrBsBfP1qWyz8Fbb4Cj+R/p2cZRjYTERFhAEZERESS927dumUcOnTIuHXrVtINh3nf+/VDO9t1R/nfe90ZrWzXHReU/HoOCg0NNZ555hnDMAzDbDYba9euNdzd3Y2BAwca//zzj+Hq6mqcPXvWZpsmTZoY7733nmEYhjFz5kwDMPbt22ezzuLFi427f0xKlSpl/PjjjzZtH374oVGnTh3DMAxj2rRpRp48eYwrV64km3XYsGFG1apVH/iZChcubAwePPiB6yX6+eefjQIFCliXZ86cafj4+NzzuKGhoUb+/PmNmzdvWtumTJli5M6d20hISDAMwzAaNmxoVK9e/aGPnah48eLGZ599ZhiGYaxZs8ZwdXU1Tp8+bX3/4MGDBmDs2rXLmjlXrlxGZGSkdZ23337bqF27drI57vszLCKSmnZMtfy9Gp7XMDZ/Zhhms7MTpdj9aoO7qccpC1mxYgW5c+cmLi4Os9nMiy++yPDhw9m4cSMJCQmULVvWZv2YmBgKFChgXXZzc6NKlftfk7558ybHjx+ne/fu9OzZ09oeHx9v7Tnat28f1atXJ3/+/Cn+LBcvXuTcuXM0adLknuusW7eOsWPHcvjwYSIjI4mPj+f27dtER0eTK1cuu45TtWpVm3Xr1KlDVFQUZ86coXjx4gDUqFEjTY4dFhZGYGAggYGB1rbg4GDy5s1LWFgYjz76KGC5vJcnz3+TxQUEBHDx4kW7jiEikmYe7QH//gnVX4aSDZ2dJt2ocLLX++fu/Z7J1Xb57WP3WfeuYWX996c8010aN27MlClTcHNzo3DhwuTIYfn2RkVF4erqyu7du3F1tc165yUkT0/PBw4ATxzHM336dGrXrm3zXuK+PT09k2znqAft49SpUzz11FO89tprjB49mvz587Nlyxa6d+9ObGys3cWLPby8bC+bpuexAXLmzGmzbDKZMCc+HFNEJL2YE2D3TKj2smV4iosrPDfd2anSnQonezky5iit1n0ALy8vSpcunaS9evXqJCQkcPHiRerXr/9Qx/Dz86Nw4cKcOHGCl156Kdl1qlSpwjfffMPVq1eT7XVyc3MjISHhvsfJkycPJUqUYP369TRu3DjJ+7t378ZsNjNhwgRcXCzF6Pz58x3+PH/99Re3bt2yFmo7duwgd+7cNr1AKTm2PZ+xQoUKnDlzhjNnzliPd+jQIa5fv05wcLDDn0VEJM1EX4WFPeD4estUA09PcnYip9FdddlA2bJleemll+jcuTOLFi3i5MmT7Nq1i7Fjx/LLL784vL8RI0YwduxYJk2axN9//83+/fuZOXMmn376KQAdO3bE39+fNm3asHXrVk6cOMHChQvZvn07YLn0dPLkSfbt28fly5eJiYlJ9jjDhw9nwoQJTJo0iaNHj7Jnzx6++OILAEqXLk1cXBxffPEFJ06c4Pvvv2fq1KkOf5bY2Fi6d+/OoUOHWLlyJcOGDaNv377Wgig59hy7RIkSREVFsX79ei5fvkx0dHSS/TRt2pTKlSvz0ksvsWfPHnbt2kXnzp1p2LAhNWvWdPiziIikiXN7YVpDS9GUwxOK13N2IqdS4ZRNzJw5k86dO/PWW29Rrlw52rRpwx9//EGxYsUc3lePHj345ptvmDlzJpUrV6Zhw4bMmjWLoKAgwNLbsmbNGnx9fWnVqhWVK1fmo48+sl7Ke+6552jRogWNGzemUKFC/PTTT8keJzQ0lIkTJ/LVV19RsWJFnnrqKY4ePQpYxiZ9+umnjBs3jkqVKjFnzhzGjh3r8Gdp0qQJZcqUoUGDBnTo0IGnn37aOnXAvdhz7Lp169KrVy86dOhAoUKFGD9+fJL9mEwmli5dSr58+WjQoAFNmzalZMmSzJs3z+HPISKSJvZ8B9+GQMRpyBcEPdZB1Q7OTuVUJsMwDGeHSE+RkZH4+PgQERGBt7e3zXu3b9/m5MmTBAUF4eHhcY89iGRc+hkWkVQRdxtWDoS931uWy7a0PG/OM69TY6WV+9UGd9MYJxEREbF18yKELQdM8MQH8PgAuM8QhuxEhZOIiIjYylsM2s2wfF363tPCZEcqnERERLI7sxk2fwIB1aBsc0ubCqZkqXASERHJzm5dg0WvwtHVlsel9NsLXgUevF02pcJJREQkuwr/H8zvBNdOQQ4PCBmroukBVDglQ7MyS2aln10Rsdu+n2BFf4i/DXmLQ4fvIaCqs1NleCqc7uDm5oaLiwvnzp2jUKFCuLm5PfARJCIZgWEYxMbGcunSJVxcXHBzc3N2JBHJqMwJlqkG/kwc/N0M2n4NuVL+fNHsRIXTHVxcXAgKCiI8PJxz5+7zbDqRDCpXrlwUK1bsvjOfi0g25+IKhhkwQaNB0OAdTTXgABVOd3Fzc6NYsWLEx8c/8FljIhmJq6srOXLkUC+piCTPbP6vQGo5Hiq1g6CHe35pdqTCKRkmk4mcOXMmeSq9iIhIpmM2w9bP4J9t8OJ8S49TDncVTSmkwklERCSruh0Bi1+DI///QPfDv0Dw087NlMmpcBIREcmKLhyEeS/D1RPg6gatPlHRlApUOImIiGQ1/5sPy9+AuGjwCYT2s6FIDWenyhJUOImIiGQlmyfA+pGWr0s2hue+1aSWqUj3H4qIiGQlZZpDzlzQ4G14eaGKplSmHicREZHMLuoS5C5k+dq/Mry+F/L4OzdTFqUeJxERkczKMGDrJJhYGc788V+7iqY043DhNGzYMP7555+0yCIiIiL2uh0J8zvD2iEQfwvCljo7UbbgcOG0dOlSSpUqRZMmTfjxxx+JiYlJi1wiIiJyLxcPw/QnIGwZuOSEJz+FZh86O1W24HDhtG/fPv744w8qVqzIG2+8gb+/P6+99hp//PHHgzcWERGRh3NgoaVounIUvItAt1XwaHfQ45bSRYrGOFWvXp1JkyZx7tw5vv32W/7991/q1atHlSpV+Pzzz4mIiEjtnCIiInJiIyzoBnE3IagBvLoJitZ0dqps5aEGhxuGQVxcHLGxsRiGQb58+Zg8eTKBgYHMmzcvtTKKiIgIQFBDqNAaHn8TXl4MXgWdnSjbSVHhtHv3bvr27UtAQABvvvkm1atXJywsjN9//52jR48yevRoXn/99dTOKiIikv2c+QNib1q+Npng+dnQdDi4akYhZ3C4cKpcuTKPPfYYJ0+e5Ntvv+XMmTN89NFHlC5d2rpOx44duXTpUqoGFRERyVYMA7Z/BTNCLI9PMQxLu4urc3Nlcw6Xq+3bt6dbt24UKVLknusULFgQs9n8UMFERESyrZgoWNYPDi76ry0hDnK4OS+TAGAyjMQSNnuIjIzEx8eHiIgIvL29nR1HRETE1qW/YX4nuHQYXHJAyFio1VN3zaUhR2oDhy/VPffcc4wbNy5J+/jx43n++ecd3Z2IiIgkOrQUpje2FE15AqDLSqj9ioqmDMThwmnTpk20atUqSXvLli3ZtGlTqoQSERHJdmKi4JeBEBsFxR+3TDVQrLazU8ldHB7jFBUVhZtb0musOXPmJDIyMlVCiYiIZDvuueH5mfD3amgyTHfNZVApuqsuuTma5s6dS3BwcKqEEhERyRZO74TDK/9bLvE4NP9QRVMG5vB3ZsiQIbRt25bjx4/zxBNPALB+/Xp++uknfv7551QPKCIikuUYBuyaDqvfgxwe0HMDFCrr7FRiB4cLp9atW7NkyRLGjBnDggUL8PT0pEqVKqxbt46GDRumRUYREZGsI/YmLO8P++dblss0A+/CTo0k9tN0BCIiIunlynGY9zJcPAQmV8tlucd66645J3OkNkjxRdTY2FguXryYZKLLYsWKpXSXIiIiWdfhX2BxL4iJhNx+0G4mlKjn7FTiIIcLp6NHj9KtWze2bdtm024YBiaTiYSEhFQLJyIikmX8s81SNBWrA8/Pgjz+zk4kKeBw4dSlSxdy5MjBihUrCAgIwKTuRRERkQdrOhzyFoOa3cA1p7PTSAo5XDjt27eP3bt3U758+bTIIyIikjX8uxu2T4a2X1sKJdecUPtVZ6eSh+Rw4RQcHMzly5fTIouIiEjmZxjw5wxYNQgSYsE3GBq+7exUkkocngBz3LhxvPPOO2zcuJErV64QGRlp8xIREcm2YqNhSW/4ZYClaKrQWr1MWYzD0xG4uFhqrbvHNmWWweGajkBERNLE1RMwrzNc2A8mF8uYprqva6qBTCBNpyPYsGFDioOJiIhkSSd+h/md4HYEeBWCdjMgqIGzU0kacLhw0uzgIiIid/EuDGYzFK0F7WdrJvAszOExTgCbN2/m5Zdfpm7dupw9exaA77//ni1btji8ry+//JISJUrg4eFB7dq12bVr133XnzhxIuXKlcPT05PAwEDefPNNbt++nZKPISIiknIJcf99XbAMdFkBXX5R0ZTFOVw4LVy4kJCQEDw9PdmzZw8xMTEAREREMGbMGIf2NW/ePAYMGMCwYcPYs2cPVatWJSQkhIsXLya7/o8//sigQYMYNmwYYWFhfPvtt8ybN4/333/f0Y8hIiKScmf3wOSalkt0iQpXgxxuTosk6cPhwmnUqFFMnTqV6dOnkzPnfxN41atXjz179ji0r08//ZSePXvStWtXgoODmTp1Krly5WLGjBnJrr9t2zbq1avHiy++SIkSJWjevDkdO3Z8YC+ViIhIqtk9G2aEwLVTsGG0ZfoByTYcLpyOHDlCgwZJB7z5+Phw/fp1u/cTGxvL7t27adq06X9hXFxo2rQp27dvT3abunXrsnv3bmuhdOLECVauXEmrVq3ueZyYmBhNmSAiIg8v7jYs7QvLX7dMNVDuSXhxvu6ay2YcHhzu7+/PsWPHKFGihE37li1bKFmypN37uXz5MgkJCfj5+dm0+/n5cfjw4WS3efHFF7l8+TKPP/44hmEQHx9Pr1697nupbuzYsYwYMcLuXCIiIklc+8dy11z4X5apBp74AOq9CS4pGiosmZjD3/GePXvyxhtvsHPnTkwmE+fOnWPOnDkMHDiQ1157LS0yWm3cuJExY8bw1VdfsWfPHhYtWsQvv/zChx9+eM9t3nvvPSIiIqyvM2fOpGlGERHJYq6fga8bWoqmXAXg5UVQ/y0VTdmUwz1OgwYNwmw206RJE6Kjo2nQoAHu7u4MHDiQfv362b2fggUL4urqyoULF2zaL1y4gL9/8k+MHjJkCJ06daJHjx4AVK5cmZs3b/LKK68wePBg6+Scd3J3d8fd3d2BTygiInIHn6JQuhlcOQbtv4O8gc5OJE7kcLlsMpkYPHgwV69e5cCBA+zYsYNLly7dt9cnOW5ubtSoUYP169db28xmM+vXr6dOnTrJbhMdHZ2kOHJ1dQUsM5eLiIikiuircOu65WuTCVp/Dt1WqWgSx3ucErm5uREcHPxQBx8wYAChoaHUrFmTWrVqMXHiRG7evEnXrl0B6Ny5M0WKFGHs2LEAtG7dmk8//ZTq1atTu3Ztjh07xpAhQ2jdurW1gBIREXko4X/BvE7gVxE6zLFcknPL5exUkkE4XDg1btw4yXPq7vTbb7/Zva8OHTpw6dIlhg4dyvnz56lWrRqrVq2yDhg/ffq0TQ/TBx98gMlk4oMPPuDs2bMUKlSI1q1bM3r0aEc/hoiISFJ751ge0Bv//xMrR10A7wDnZpIMxeGH/L755ps2y3Fxcezbt48DBw4QGhrK559/nqoBU5se8isiIknEx8Cv78DuWZblMs2h7dfgmc+psSR9pOlDfj/77LNk24cPH05UVJSjuxMREXGu62dgfmc4twcwQeP3of5A3TUnyUq1n4qXX375njN+i4iIZEiGAfNeshRNnvngpQXQ8B0VTXJPqfaTsX37djw8PFJrdyIiImnPZIInP4OiteCV36FM0wdvI9maw5fq2rZta7NsGAbh4eH8+eefDBkyJNWCiYiIpIlb1+HcXijV2LJctAZ0X6NHp4hdHC6cfHx8bJZdXFwoV64cI0eOpHnz5qkWTEREJNWdPwDzXoYb4dB9LQRUsbSraBI7OVw4zZw5My1yiIiIpK2/5sHyNyD+FvgUA8Ps7ESSCaV4AkwREZFMIT4WVr8Pf0y3LJdqAs99A7nyOzeXZEoOF0758uW77wSYd7p69arDgURERFJNxFn4ORT+/cOy3OAdaDQIXPS0CUkZhwunIUOGMGrUKEJCQqzPlNu+fTurV69myJAh5M+vCl5ERDKI/82zFE0ePvDs11CuhbMTSSbn8Mzhzz33HI0bN6Zv37427ZMnT2bdunUsWbIkNfOlOs0cLiKSjZgTYM0HUKsn5C/p7DSSQTlSGzhcOOXOnZt9+/ZRunRpm/Zjx45RrVq1DD97uAonEZEs7HYkbJ4Ajd6DnJpbUOzjSG3g8ASYBQoUYOnSpUnaly5dSoECBRzdnYiISOq4GAbTG8PWibBqkLPTSBbl8BinESNG0KNHDzZu3Ejt2rUB2LlzJ6tWrWL69OmpHlBEROSB9i+AZf0gLhq8i0D1l52dSLIohwunLl26UKFCBSZNmsSiRYsAqFChAlu2bLEWUiIiIukiIQ7WDIGdUyzLQQ2h3QzwKujcXJJlOTzGKbPTGCcRkSwiMhx+7gJndliWHx8AT3ygqQbEYWk6xgng+PHjfPDBB7z44otcvHgRgF9//ZWDBw+mZHciIiKOS4iFy0fA3Rte+BGaDlPRJGnO4cLp999/p3LlyuzcuZOFCxda76L766+/GDZsWKoHFBERSVa+4tDhB3hlI5R/0tlpJJtwuHAaNGgQo0aNYu3atbi5uVnbn3jiCXbs2JGq4URERKxibsDPXeHv1f+1lXgcCpRyXibJdhwunPbv38+zzz6bpN3X15fLly+nSigREREbl47A9CZwcBEs7QOx0c5OJNmUw4VT3rx5CQ8PT9K+d+9eihQpkiqhRERErA4ugelPWMYz5QmADnPALZezU0k25XDh9MILL/Duu+9y/vx5TCYTZrOZrVu3MnDgQDp37pwWGUVEJDtKiIfVgy0P6Y2NghL14dVNUExT34jzOFw4jRkzhvLlyxMYGEhUVBTBwcE0aNCAunXr8sEHH6RFRhERyW7ibsN3z8D2yZbluq9DpyWQ29epsUQcmsfJMAzOnDlDoUKFuHz5Mvv37ycqKorq1atTpkyZtMyZajSPk4hIJrG8P+z/Gdp8BcHPODuNZGFp9pBfs9mMh4cHBw8ezDSF0t1UOImIZFCGAXG3/hu/FB8DEf/qrjlJc2k2AaaLiwtlypThypUrDxVQRETERuxNWNgDfnoBzAmWthzuKpokw3F4jNNHH33E22+/zYEDB9Iij4iIZDeXj8E3TeHAAvhnK/z7h7MTidyTw8+qy5cvH9HR0cTHx+Pm5oanp6fN+1evXk3VgKlNl+pERDKQsOWwpDfEREJuP3h+FhSv6+xUks04UhvkcHTnEydOTGkuERERi4R4+O1D2DrRslysjqVoyuPvzFQiD2RX4TRgwAA+/PBDvLy8CAoKom7duuTI4XDNJSIiYvHLm7DnO8vXj/WBZiPANadzM4nYwa4xTl988YX1Yb6NGzfO8JfjREQkg6v1KngVgnYzocUYFU2SadjVbVSiRAkmTZpE8+bNMQyD7du3ky9fvmTXbdCgQaoGFBGRLMAw4NJh8K1gWfavBG/8T49OkUzHrsHhS5YsoVevXly8eBGTycS9NjGZTCQkJKR6yNSkweEiIuksNhp+GWCZzLLLSj0yRTKcVB8c3qZNG9q0aUNUVBTe3t4cOXIEX19Ney8iIg9w9QTM6wQXDoDJFS4eUuEkmZpDI7xz587Nhg0bCAoK0uBwERG5vyOrYNErEBPx33imoPrOTiXyUByufho2bJgWOUREJKswJ8DGsbDpY8ty0VrQfjZ4F3ZuLpFUoG4jERFJXYeW/Fc01XoVmo+CHG5OjSSSWlQ4iYhI6qrYFv5eA6WbQJX2zk4jkqpUOImIyMPbvwDKtgD33GAyQdtpzk4kkiYcfsjvzJkziY6OTossIiKS2cTdgqV9YGF3WNbPMl+TSBbmcOE0aNAg/P396d69O9u2bUuLTCIikhlcOwXfNoe9P4DJBfwrOzuRSJpzuHA6e/Yss2fP5vLlyzRq1Ijy5cszbtw4zp8/nxb5REQkIzq6FqY1hPP/g1wFoNNiqD/AcplOJAtzuHDKkSMHzz77LEuXLuXMmTP07NmTOXPmUKxYMZ5++mmWLl2K2WxOi6wiIuJsZjNs/AjmPA+3r0ORGvDqJijZyNnJRNKFw4XTnfz8/Hj88cepU6cOLi4u7N+/n9DQUEqVKsXGjRtTKaKIiGQYt67CH98CBtTsDl1/BZ+izk4lkm5SVDhduHCBTz75hIoVK9KoUSMiIyNZsWIFJ0+e5OzZs7Rv357Q0NDUzioiIs7mVRCenwVtpsBTn0IOd2cnEklXdj3k906tW7dm9erVlC1blh49etC5c2fy589vs87Fixfx9/fPkJfs9JBfEREH7f0B3HJDxTbOTiKSJlL9Ib938vX15ffff6dOnTr3XKdQoUKcPHnS0V2LiEhGEncbVr0Lu2dBTi8oXA3ylXByKBHncrhw+vbbbx+4jslkonjx4ikKJCIiGcD10zC/M5zbC5jg8TfBp5izU4k4ncNjnF5//XUmTZqUpH3y5Mn0798/NTKJiIgzHf/NMtXAub3gmQ9eXgAN3waXh7qfSCRLcPj/goULF1KvXr0k7XXr1mXBggWpEkpERJxk0yfwfVvL3XOFq1umGijd1NmpRDIMhy/VXblyBR8fnyTt3t7eXL58OVVCiYiIk9y+DhjwSCi0HA85PZydSCRDcbjHqXTp0qxatSpJ+6+//krJkiVTJZSIiKSjO2+ubjIcXvgJnp6kokkkGQ73OA0YMIC+ffty6dIlnnjiCQDWr1/PhAkTmDhxYmrnExGRtPTXPPjrR3jxZ8jhBq45oHwrZ6cSybAcLpy6detGTEwMo0eP5sMPPwSgRIkSTJkyhc6dO6d6QBERSQPxsbD6PfjjG8vy3u/g0R7OzSSSCTg8AeadLl26hKenJ7lz507NTGlKE2CKSLYXcRZ+DoV//7AsNxwEDd8BF1fn5hJxkjSdAPNOhQoVepjNRUQkvZ34HRZ0g+jL4OEDbb+Bss2dnUok03B4cPiFCxfo1KkThQsXJkeOHLi6utq8REQkg9o7B75vYyma/CvDK7+raBJxkMM9Tl26dOH06dMMGTKEgIAATCZTWuQSEZHUFljb8sy5Cq3hyQmQ09PZiUQyHYcLpy1btrB582aqVauWBnFERCRV3bpmmf0boGBp6LUF8hYD/aNXJEUcvlQXGBjIQ4wnFxGR9LJ/AXxWGU5s/K8tX3EVTSIPweHCaeLEiQwaNIhTp06lQRwREXlo8bHw67uwsDvE3oA93zk7kUiW4fClug4dOhAdHU2pUqXIlSsXOXPmtHn/6tWrqRZOREQcFBlumWrgzE7Lcv23oPFg52YSyUIcLpw0O7iISAZ1agv83BVuXgR3H3h2qmYBF0llDhdOoaGhqRrgyy+/5OOPP+b8+fNUrVqVL774glq1at1z/evXrzN48GAWLVrE1atXKV68OBMnTqRVK/1yEJFs7Px+mP00GAngWxE6fA8FSjk7lUiWk6IJMI8fP87MmTM5fvw4n3/+Ob6+vvz6668UK1aMihUr2r2fefPmMWDAAKZOnUrt2rWZOHEiISEhHDlyBF9f3yTrx8bG0qxZM3x9fVmwYAFFihThn3/+IW/evCn5GCIiWYdfJaj8PGDAUxPBLZezE4lkSQ4/cuX333+nZcuW1KtXj02bNhEWFkbJkiX56KOP+PPPP1mwYIHd+6pduzaPPvookydPBsBsNhMYGEi/fv0YNGhQkvWnTp3Kxx9/zOHDh5OMrbKXHrkiIlnGpSOQ2w8881qWE+LAJYfumhNxkCO1gcN31Q0aNIhRo0axdu1a3NzcrO1PPPEEO3bssHs/sbGx7N69m6ZNm/4XxsWFpk2bsn379mS3WbZsGXXq1KFPnz74+flRqVIlxowZQ0JCgqMfQ0QkczuwCL5uDIt7gdlsaXPNqaJJJI05fKlu//79/Pjjj0nafX19uXz5st37uXz5MgkJCfj5+dm0+/n5cfjw4WS3OXHiBL/99hsvvfQSK1eu5NixY/Tu3Zu4uDiGDRuW7DYxMTHExMRYlyMjI+3OKCKS4STEwbrhsN3SU09slOXloR50kfTgcI9T3rx5CQ8PT9K+d+9eihQpkiqh7sVsNuPr68vXX39NjRo16NChA4MHD2bq1Kn33Gbs2LH4+PhYX4GBgWmaUUQkzdy4AN8981/RVK8/dFqiokkkHTlcOL3wwgu8++67nD9/HpPJhNlsZuvWrQwcOJDOnTvbvZ+CBQvi6urKhQsXbNovXLiAv79/stsEBARQtmxZm4cJV6hQgfPnzxMbG5vsNu+99x4RERHW15kzZ+zOKCKSYZzeAdMawD9bwS0PtP8emo0A1xTd4yMiKeRw4TRmzBjKly9PYGAgUVFRBAcH06BBA+rWrcsHH3xg937c3NyoUaMG69evt7aZzWbWr19PnTp1kt2mXr16HDt2DHPi9Xzg77//JiAgwGa81Z3c3d3x9va2eYmIZCoJcZaxTFHnoVAFeGUDBD/t7FQi2ZLDd9UlOn36NAcOHCAqKorq1atTpkwZh/cxb948QkNDmTZtGrVq1WLixInMnz+fw4cP4+fnR+fOnSlSpAhjx44F4MyZM1SsWJHQ0FD69evH0aNH6datG6+//jqDB9s3M67uqhORTOnsbtg1HVp9Au65nZ1GJEtxpDZIcR9vsWLFKFasWEo3ByyPb7l06RJDhw7l/PnzVKtWjVWrVlkHjJ8+fRoXl/86xQIDA1m9ejVvvvkmVapUoUiRIrzxxhu8++67D5VDRCTDuXwMLh76r2epSA14toZzM4mI4z1O3bp1u+/7M2bMeKhAaU09TiKS4YUth8WvQUIsdF8Dhas5O5FIlpamPU7Xrl2zWY6Li+PAgQNcv36dJ554wtHdiYhIooR4+O1D2DrRslysLuRJ/mYZEXEOhwunxYsXJ2kzm8289tprlCql5yKJiKRI1CVY0BVObbYs1+kLTYdbJrUUkQwjxYPD73bkyBEaNWqU7BxPGYku1YlIhnPmD5jfGW6cg5xe8MxkqNTW2alEso10GRx+t+PHjxMfH59auxMRyT6Or7cUTQXLQocfoFA5ZycSkXtwuHAaMGCAzbJhGISHh/PLL78QGhqaasFERLKNBu9ADnd4tAe453F2GhG5D4cLp71799osu7i4UKhQISZMmPDAO+5ERAS4chx+HwetP4ecnuDiAo+/6exUImIHhwunDRs2pEUOEZHs4fBKyyzgMRGQqwC0GOvsRCLiAD3kSEQkPZgTYMMY2PyJZTmwNtTt59xMIuIwhwun6tWrYzKZ7Fp3z549DgcSEclybl6Bhd3hxP/32NfuBc0+hBzJP2NTRDIuhwunFi1a8NVXXxEcHGx9GO+OHTs4ePAgr732Gp6enqkeUkQk0wr/H/zUESL/hZy54OkvoHI7Z6cSkRRyuHC6dOkSr7/+Oh9++KFN+7Bhwzhz5kyGf+SKiEi68swLcdGQv5RlqgG/YGcnEpGH4PAEmD4+Pvz555+UKVPGpv3o0aPUrFmTiIiIVA2Y2jQBpoikOXMCuLj+t3x2DxQoBR4+zsskIvfkSG3g4ujOPT092bp1a5L2rVu34uHh4ejuRESylqsnYXpjOPLrf21FHlHRJJJFOHyprn///rz22mvs2bOHWrVqAbBz505mzJjBkCFDUj2giEim8fcaWNQDbkfA6vehdDNw1c3LIlmJw/9HDxo0iJIlS/L555/zww8/AFChQgVmzpxJ+/btUz2giEiGZ06wTGj5+zjLcpGa0H62iiaRLCjVHvKbWWiMk4ikquirsKgnHFtnWX60B4SMsTxCRUQyhXR5yG9sbCwXL17EbDbbtBcrViyluxQRyVxuXYNpDSHiNOTwhNYToeoLzk4lImnI4cLp6NGjdOvWjW3bttm0G4aByWQiISEh1cKJiGRonvmgTDM4/ht0+B78Kzs7kYikMYcLpy5dupAjRw5WrFhBQECA3bOIi4hkCXG3LfMy5cpvWW4xFuJuWeZrEpEsz+HCad++fezevZvy5cunRR4RkYzr+mmY1wk8vOHlxZbB3zncNZ5JJBtxuHAKDg7m8uXLaZFFRCTjOrYOFvawjGvyzAdXT0Chss5OJSLpzOEJMMeNG8c777zDxo0buXLlCpGRkTYvEZEsxWyG38fDD+0sRVPh6vDqJhVNItmUw9MRuLhYaq27xzZllsHhmo5AROx26xosehWOrrYs1+gCLcZBTj0lQSQrSdPpCDZs2JDiYCIimcrCHpZLdK7u8OQEeKSTsxOJiJM5XDg1bNgwLXKIiGQ8zUZCxFl4dioUrubsNCKSAdhVOP3vf/+jUqVKuLi48L///e++61apUiVVgomIpLv4GDizC4LqW5b9KsJr28DF4eGgIpJF2VU4VatWjfPnz+Pr60u1atUwmUwkNzQqM4xxEhFJVsS/ML8zhP8FXVZCsdqWdhVNInIHuwqnkydPUqhQIevXIiJZyomNsKAbRF8Bj7wQG+XsRCKSQdlVOBUvXjzZr0VEMjXDgC2fwW8fgmEG/yqWR6fkK+HsZCKSQdlVOC1btszuHT799NMpDiMikm5uR8CS3nB4hWW52kuWO+dyejo3l4hkaHYVTm3atLFrZxrjJCKZxoFFlqLJ1Q1ajrfM0aRnb4rIA9hVOJnN5rTOISKSvmp0gUuHoUp7KFLD2WlEJJN4qNtFbt++nVo5RETSVnwsbPoYYm5Ylk0maDlORZOIOMThwikhIYEPP/yQIkWKkDt3bk6cOAHAkCFD+Pbbb1M9oIjIQ4s8B7Ofgt9GwbJ+zk4jIpmYw4XT6NGjmTVrFuPHj8fNzc3aXqlSJb755ptUDSci8tBOboZpDeDMTnD3gSodnJ1IRDIxhwun7777jq+//pqXXnoJV1dXa3vVqlU5fPhwqoYTEUkxw4Ctk+C7Z+DmJfCrBK9sgHItnZ1MRDIxh59Vd/bsWUqXLp2k3Ww2ExcXlyqhREQeyu1IWNoHwv5/KpUqL8BTn4FbLufmEpFMz+Eep+DgYDZv3pykfcGCBVSvXj1VQomIPJS4aMulOZeclrmZnp2qoklEUoXDPU5Dhw4lNDSUs2fPYjabWbRoEUeOHOG7775jxYoVaZFRRMQxefyh/fdgcoHAR52dRkSyEId7nJ555hmWL1/OunXr8PLyYujQoYSFhbF8+XKaNWuWFhlFRO4vIQ5WvQ8HFv7XVqy2iiYRSXUO9zgB1K9fn7Vr16Z2FhERx904Dz93hdPbwC03BDUEr4LOTiUiWVSKCicRkQzhn+3wcyhEXQB3b2gzRUWTiKQpuwqnfPnyYbLzGU5Xr159qEAiIg9kGLBjCqwdAuZ4KFQBOvwABZPe8SsikprsKpwmTpxo/frKlSuMGjWKkJAQ6tSpA8D27dtZvXo1Q4YMSZOQIiJWZjMs6vHfeKZK7eDpSeDm5dxcIpItmAzDMBzZ4LnnnqNx48b07dvXpn3y5MmsW7eOJUuWpGa+VBcZGYmPjw8RERF4e3s7O46IpMSaIbDjK2g+Gmq/annunIhICjlSGzhcOOXOnZt9+/YlmQTz2LFjVKtWjaioKMcTpyMVTiKZVHws5Pj/xzwlxMPFgxBQ1bmZRCRLcKQ2cHg6ggIFCrB06dIk7UuXLqVAgQKO7k5E5P4S4i09TLOfshRPAK45VDSJiFM4fFfdiBEj6NGjBxs3bqR27doA7Ny5k1WrVjF9+vRUDygi2VjURVjQDU79/9MKjq6BCk85N5OIZGsOF05dunShQoUKTJo0iUWLFgFQoUIFtmzZYi2kREQe2pldMD8UbpyzzM/0zJcqmkTE6Rwe45TZaYyTSAZnGLBrOqx+H8xxULCsZaqBQuWcnUxEsqg0HeN0pyeffJLw8PCH2YWIiK2NY+HXty1FU/Az0PM3FU0ikmE8VOG0adMmbt26lVpZRESgSgfwzG+ZauD52eCex9mJRESs9MgVEXG+qycgf0nL1wVKwRv7wMPHqZFERJLzUD1OxYsXJ2fOnKmVRUSyG3MCrB8JX9SE4xv+a1fRJCIZ1EP1OB04cCC1cohIdnPzCizsBic2WpZP74BSjZ0aSUTkQVLU47R582Zefvll6taty9mzZwH4/vvv2bJlS6qGE5Es6t/dMK2BpWjKmQue+xYav+fsVCIiD+Rw4bRw4UJCQkLw9PRkz549xMTEABAREcGYMWNSPaCIZCGGAX/OgJktIPJfKFDactdc5XbOTiYiYheHC6dRo0YxdepUpk+fbjO+qV69euzZsydVw4lIFnNqM6x4ExJiofxT0HMD+FZwdioREbs5PMbpyJEjNGjQIEm7j48P169fT41MIpJVlagPj3S29DTVfR1MJmcnEhFxiMM9Tv7+/hw7dixJ+5YtWyhZsmSqhBKRLOTYerh1zfK1yQStJ0G9N1Q0iUim5HDh1LNnT9544w127tyJyWTi3LlzzJkzh4EDB/Laa6+lRUYRyYzMCbBhDPzQFha9AmazpV0Fk4hkYg5fqhs0aBBms5kmTZoQHR1NgwYNcHd3Z+DAgfTr1y8tMopIZhN9FRb2gOPrLct5i4GRwENOHSci4nQOPeQ3ISGBrVu3UqVKFXLlysWxY8eIiooiODiY3Llzp2XOVKOH/IqksXN7YV5niDgNOTyh9edQtYOzU4mI3FOaPeTX1dWV5s2bc+3aNdzc3AgODqZWrVoPXTR9+eWXlChRAg8PD2rXrs2uXbvs2m7u3LmYTCbatGnzUMcXkVSy5zv4NsRSNOULgh7rVDSJSJbicL95pUqVOHHiRKoFmDdvHgMGDGDYsGHs2bOHqlWrEhISwsWLF++73alTpxg4cCD169dPtSwi8hBiouD3jyEhBsq1glc2gn8lZ6cSEUlVKZrHaeDAgaxYsYLw8HAiIyNtXo769NNP6dmzJ127diU4OJipU6eSK1cuZsyYcc9tEhISeOmllxgxYoTu5BPJKNxzQ4fvoMkw6DAHPPM6O5GISKpzeHB4q1atAHj66acx3XF3jGEYmEwmEhIS7N5XbGwsu3fv5r33/nvUgouLC02bNmX79u333G7kyJH4+vrSvXt3Nm/efN9jxMTEWGc3B1JU3InIPRxdB9FX/rscV7i65SUikkU5XDht2LDhwSvZ6fLlyyQkJODn52fT7ufnx+HDh5PdZsuWLXz77bfs27fPrmOMHTuWESNGPGxUEbmT2QybP7FMN+DqBn4VdVlORLIFhwunhg0bpkUOu9y4cYNOnToxffp0ChYsaNc27733HgMGDLAuR0ZGEhgYmFYRRbK+W9dg0atwdLVludqLULCMczOJiKQThwsngM2bNzNt2jROnDjBzz//TJEiRfj+++8JCgri8ccft3s/BQsWxNXVlQsXLti0X7hwAX9//yTrHz9+nFOnTtG6dWtrm/n/J9XLkSMHR44coVSpUjbbuLu74+7u7sjHE5F7Cf8fzO8E105BDg948lOo/pKzU4mIpBuHB4cvXLiQkJAQPD092bNnj3X8UEREBGPGjHFoX25ubtSoUYP169db28xmM+vXr6dOnTpJ1i9fvjz79+9n37591tfTTz9N48aN2bdvn3qSRNLSvp/g22aWoilvcei+RkWTiGQ7Dvc4jRo1iqlTp9K5c2fmzp1rba9Xrx6jRo1yOMCAAQMIDQ2lZs2a1KpVi4kTJ3Lz5k26du0KQOfOnSlSpAhjx47Fw8ODSpVsx1HkzZsXIEm7iKSy66ch/jaUaQ5tvwbPfM5OJCKS7hwunI4cOUKDBg2StPv4+HD9+nWHA3To0IFLly4xdOhQzp8/T7Vq1Vi1apV1wPjp06dxcdFjGkScrsHbkD8IKrUD/T8pItmUw4WTv78/x44do0SJEjbtW7ZsSfGcSn379qVv377Jvrdx48b7bjtr1qwUHVNEHuD4Btj6OXT8CXJ6WoqlKu2dnUpExKkc/mdjz549eeONN9i5cycmk4lz584xZ84cBg4cyGuvvZYWGUUkPZnNsHkC/NAWTvx/8SQiIkAKepwGDRqE2WymSZMmREdH06BBA9zd3Rk4cCD9+vVLi4wikl5uXYclr8GRlZbl6i9Dvf7OTCQikqGYDMMwUrJhbGwsx44dIyoqiuDg4Id+0G96ceQJyCLZyoWDMO9luHoCXN2h1cdQI9TZqURE0pwjtYHDPU4//PADbdu2JVeuXAQHB6c4pIhkIEfXwrxOEH8LfAKh/XdQ5BFnpxIRyXAcHuP05ptv4uvry4svvsjKlSsdejadiGRQhcpbBoCXegJe+V1Fk4jIPThcOIWHhzN37lxMJhPt27cnICCAPn36sG3btrTIJyJpJSbqv6/zBkL3tfDSAvAq4LxMIiIZnMOFU44cOXjqqaeYM2cOFy9e5LPPPuPUqVM0btw4yeNORCSDOrkJJlWHw7/811awNLi4Oi+TiEgmkKJn1SXKlSsXISEhXLt2jX/++YewsLDUyiUiacEwYNskWDccDDNs/xLKtQKTydnJREQyhRQVTtHR0SxevJg5c+awfv16AgMD6dixIwsWLEjtfCKSWm5HwtLeELbcsly1o+UhvSqaRETs5nDh9MILL7BixQpy5cpF+/btGTJkSLIP5BWRDOTiYctUA1eOgktOaDkOanZT0SQi4iCHCydXV1fmz59PSEgIrq4aDyGS4UX8C9OfgLib4F3EMtVA0ZrOTiUikik5XDjNmTPH+vXt27fx8PBI1UAiksp8ikK1jnD5KLSbAV4FnZ1IRCTTcviuOrPZzIcffkiRIkXInTs3J06cAGDIkCF8++23qR5QRFLgxnmIuvTfcshY6LRYRZOIyENyuHAaNWoUs2bNYvz48bi5uVnbK1WqxDfffJOq4UQkBf7ZBtMawIKukBBvacvhpqkGRERSgcOF03fffcfXX3/NSy+9ZDPGqWrVqhw+fDhVw4mIAwzDMr3ArKcg6gJEX7G8REQk1Tg8xuns2bOULl06SbvZbCYuLi5VQomIg2JuwLJ+cHCxZbny89D6c3Dzcm4uEZEsxuHCKTg4mM2bN1O8eHGb9gULFlC9evVUCyYidrr0t2WqgctHwCWHZTxTrZ6aakBEJA04XDgNHTqU0NBQzp49i9lsZtGiRRw5coTvvvuOFStWpEVGEbkXw4DFr1iKpjwB8PxsKFbb2alERLIsh8c4PfPMMyxfvpx169bh5eXF0KFDCQsLY/ny5TRr1iwtMorIvZhM0GYKlAmBVzepaBIRSWMmwzAMZ4dIT5GRkfj4+BAREYG3t7ez44g4LuoinN4BwU87O4mISJbgSG3gcI/TmTNn+Pfff63Lu3bton///nz99deOJxURx5ze+d9UA6d3ODuNiEi243Dh9OKLL7JhwwYAzp8/T9OmTdm1axeDBw9m5MiRqR5QRLCMZdo5DWa1ghvhkL8UeOZ3dioRkWzH4cLpwIED1KpVC4D58+dTuXJltm3bxpw5c5g1a1Zq5xOR2JuwqCf8+g6Y46His9DzNyhU1tnJRESyHYfvqouLi8Pd3R2AdevW8fTTlnEW5cuXJzw8PHXTiWR3l4/B/E5w8RCYXKH5h/BYb001ICLiJA73OFWsWJGpU6eyefNm1q5dS4sWLQA4d+4cBQoUSPWAItnasbWWoim3H3RZAXX6qGgSEXEih3ucxo0bx7PPPsvHH39MaGgoVatWBWDZsmXWS3gikkpq97LMCv5IZ8jj7+w0IiLZXoqmI0hISCAyMpJ8+fJZ206dOkWuXLnw9fVN1YCpTdMRSIZ28zKsHwkho8E9j7PTiIhkC47UBg73OAG4urraFE0AJUqUSMmuRCTRv3/C/M4QeRYSYuHZqc5OJCIid0lR4SQiqcgw4M8Z8Ou7YI6DAqWh3hvOTiUiIslQ4STiTLHR8MsA+Osny3L5pyyPUPHQZWQRkYxIhZOIs1z7B+a+BBf2g8kFmg6Huq/rrjkRkQxMhZOIs+TwgJuXIFdBeH4mBDVwdiIREXkAuwqnSZMm2b3D119/PcVhRLI8w/ivRymPH7w4F7x8waeIc3OJiIhd7JqOICgoyGb50qVLREdHkzdvXgCuX79unYrgxIkTaRI0tWg6AnGa6KuwsAdUexEqt3N2GhER+X+O1AZ2zRx+8uRJ62v06NFUq1aNsLAwrl69ytWrVwkLC+ORRx7hww8/TJUPIJLlnN0D0xrC8fWWZ87F3nR2IhERSQGHJ8AsVaoUCxYsoHr16jbtu3fvpl27dpw8eTJVA6Y29ThJuts9G1YOtMzNlL8ktP8e/Cs5O5WIiPy/NJ0AMzw8nPj4+CTtCQkJXLhwwdHdiWRdcbcsBdPeHyzL5VpZphrwzOvUWCIiknIOP+S3SZMmvPrqq+zZs8fatnv3bl577TWaNm2aquFEMq242zCjhaVoMrlAk6HQYY6KJhGRTM7hwmnGjBn4+/tTs2ZN3N3dcXd3p1atWvj5+fHNN9+kRUaRzCenB5RsCLkKwMuLoP5b4OLw/24iIpLBpOghvwB///03hw8fBqB8+fKULVs2VYOlFY1xkjRjNkNMBHj+/3McE+It8zR5Bzg3l4iI3FeaP+QXLA/1NQyDUqVKkSOH5tGUbC76Kix+1fLfrishhzu45lDRJCKSxTh87SA6Opru3buTK1cuKlasyOnTpwHo168fH330UaoHFMnwwv+CrxvB0TVw4YBlWUREsiSHC6f33nuPv/76i40bN+Lh4WFtb9q0KfPmzUvVcCIZ3t4f4NvmcP0fyFscuq+FwFrOTiUiImnE4WtsS5YsYd68eTz22GOY7ngYacWKFTl+/HiqhhPJsOJjLBNZ7p5lWS4TAm2n/Te+SUREsiSHC6dLly7h6+ubpP3mzZs2hZRIlrbiTdg3BzBB48G6a05EJJtw+Dd9zZo1+eWXX6zLicXSN998Q506dVIvmUhGVv8tyFcCXl4ADd9W0SQikk043OM0ZswYWrZsyaFDh4iPj+fzzz/n0KFDbNu2jd9//z0tMoo4n9kM//4BxWpblguUgr67LXfOiYhItuHwP5Mff/xx9u3bR3x8PJUrV2bNmjX4+vqyfft2atSokRYZRZzr1nWY+yLMCIFj6/9rV9EkIpLtpOg3f6lSpZg+fXpqZxHJeM4fgHkvw7WT4OoONy87O5GIiDiRwz1OTZs2ZdasWURGRqZFHpGM46958E1TS9HkUwy6r4aqHZydSkREnMjhwqlixYq89957+Pv78/zzz7N06VLi4uLSIpuIc8THwi8DYfErEH8LSjWBV3+HwtWdnUxERJzM4cLp888/5+zZsyxZsgQvLy86d+6Mn58fr7zyigaHS9bw96/wx/9fim74Lrz0M+TK79xMIiKSIaT4Ib+Jbt++zfLlyxk9ejT79+8nISEhtbKlCT3kVx7IMGD1+xDUEMq1cHYaERFJY+nykF+A8+fPM3fuXH744Qf+97//UauWHjUhmZBhwB/fQKXnLD1LJhO0GOvsVCIikgE5fKkuMjKSmTNn0qxZMwIDA5kyZQpPP/00R48eZceOHWmRUSTt3I6E+Z1g5UBY9IplviYREZF7cLjHyc/Pj3z58tGhQwfGjh1LzZo10yKXSNq7GGaZauDKMXB1g/KtLL1NIiIi9+BQ4WQYBpMmTeKll14iV65caZVJJO3tXwDL+kFcNHgXhfbfQVFN4CoiIvfn0KU6wzDo06cPZ8+eTas8ImkrIQ5+HQQLu1uKppKNLFMNqGgSERE7OFQ4ubi4UKZMGa5cuZJWeUTSVmwUHP7/h1TXfwteXgReBZ2bSUREMg2HB4d/9NFHvP322xw4cCAt8oikLc980OE7eOFHaDIUXFydnUhERDIRh+dxypcvH9HR0cTHx+Pm5oanp6fN+1evXk3VgKlN8zhlM4YBO74C9zzwSGdnpxERkQwoTedxmjhxYkpziaSvmBuWAeAHF1vumivxOOQv6exUIiKSiTlcOIWGhqZ6iC+//JKPP/6Y8+fPU7VqVb744ot7TqY5ffp0vvvuO+ulwho1ajBmzBhNvim2Lh2BeZ3g8hFwyQkhYyBfkLNTZQkJZoNdJ69y8cZtfPN4UCsoP64umsZBRLKHFM0cfvz4cWbOnMnx48f5/PPP8fX15ddff6VYsWJUrFjRoX3NmzePAQMGMHXqVGrXrs3EiRMJCQnhyJEj+Pr6Jll/48aNdOzYkbp16+Lh4cG4ceNo3rw5Bw8epEiRIin5OJLVHFwCS/tYBoLnKQztZ0OgCuvUsOpAOCOWHyI84ra1LcDHg2Gtg2lRKcCJyURE0ofDY5x+//13WrZsSb169di0aRNhYWGULFmSjz76iD///JMFCxY4FKB27do8+uijTJ48GQCz2UxgYCD9+vVj0KBBD9w+ISGBfPnyMXnyZDp3fvAYFo1xyuLWDoWtn1u+LlEf2s2E3IWcmymLWHUgnNd+2MPdvzAS+5qmvPyIiicRyZQcqQ0cvqtu0KBBjBo1irVr1+Lm5mZtf+KJJxx+5EpsbCy7d++madOm/wVycaFp06Zs377drn1ER0cTFxdH/vx6er0Abnks/633BnRaoqIplSSYDUYsP5SkaAKsbSOWHyLB/FDPDBcRyfAcvlS3f/9+fvzxxyTtvr6+XL582aF9Xb58mYSEBPz8/Gza/fz8OHz4sF37ePfddylcuLBN8XWnmJgYYmJirMuRkZEOZZRMwJzw37QC9d+CEvWgeF3nZspidp28anN57m4GEB5xm10nr1KnVIH0CyYiks4c7nHKmzcv4eHhSdr37t2b7mOMPvroI+bOncvixYvx8PBIdp2xY8fi4+NjfQUGBqZrRklDhgE7psI3TSA22tLm4qKiKQ2EX79l13oXb9y7uBIRyQocLpxeeOEF3n33Xc6fP4/JZMJsNrN161YGDhxo1xijOxUsWBBXV1cuXLhg037hwgX8/f3vu+0nn3zCRx99xJo1a6hSpco913vvvfeIiIiwvs6cOeNQRsmgYm/Cwh6w6l04txf++snZibIks9lgyd6zfLTKvh5g3zzJ/wNGRCSrcLhwGjNmDOXLlycwMJCoqCiCg4Np0KABdevW5YMPPnBoX25ubtSoUYP169db28xmM+vXr6dOnTr33G78+PF8+OGHrFq1ipo1a973GO7u7nh7e9u8JJO7fAy+aQoHFoBLDmjxEdTs5uxUWYphGKw7dIFWkzbTf94+Lt6I4X4zDpiw3F1XK0hjDUUka3N4jJObmxvTp09n6NCh7N+/n6ioKKpXr06ZMmVSFGDAgAGEhoZSs2ZNatWqxcSJE7l58yZdu3YFoHPnzhQpUoSxY8cCMG7cOIYOHcqPP/5IiRIlOH/+PAC5c+cmd+7cKcogmUjYcljSG2IiIbcfPD8bit+7yBbH7TxxhfGrj7D7n2sA5PHIQa+GpSiaz5P+c/cB2AwST6ynhrUO1nxOIpLlpWgeJ4DAwEACAwNJSEhg//79XLt2jXz58jm8nw4dOnDp0iWGDh3K+fPnqVatGqtWrbIOGD99+jQuLv91jE2ZMoXY2FjatWtns59hw4YxfPjwlH4cyQz+nAEr3rR8XawuPD8T8tz/kq7Y78DZCD5efYTf/74EgHsOF7rWC6JXw5LkzeVmbbt7Hid/zeMkItmIw/M49e/fn8qVK9O9e3cSEhJo2LAh27ZtI1euXKxYsYJGjRqlUdTUoXmcMrGIf2FaQ6jSAZqNANeczk6UJZy8fJMJa46w4n+Wmz5yuJjo8Gggrzcpg5930jFLmjlcRLKaNH1W3YIFC3j55ZcBWL58OSdOnODw4cN8//33DB48mK1bt6YstUhyIs6Cz//frelTFPrsAi/d7p4awiNuMWn9Ueb/+a91/qVnqhXmzaZlKVHQ657bubqYNOWAiGRbDhdOly9ftt7xtnLlStq3b0/ZsmXp1q0bn3/+eaoHlGzKMOCPb2D1+5bZvys8ZWlX0fTQrt2MZcrvx5m17RSx8WYAnijvy8Dm5QgurF5YEZH7cbhw8vPz49ChQwQEBLBq1SqmTJkCWGbwdnV1TfWAkg3FRlvGMv1vrmX5yK//FU6SYjdj4vl2y0mmbzrBjZh4AB4tkY93WpTn0RK6G05ExB4OF05du3alffv2BAQEYDKZrDN279y5k/Lly6d6QMlmrp6AeZ3gwgEwuULT4VC3n7NTZWox8Qn8uPM0X244xuWoWAAqBHjzTkg5GpUrhMmk8UkiIvZyuHAaPnw4lSpV4syZMzz//PO4u7sD4OrqatdDeUXu6civsOhViIkAr0KWS3RB9Z2dKtNKMBss3nuWz9b+zdn/n/m7eIFcDGhWltZVCuOiAd0iIg5z+K66zE531WVQFw7ClP9/VEpgbXh+FngXdmqkzMowDFYfvMCENUc4ejEKAD9vd15vUob2NQPJ6erwvLciIllamt5VB7B+/Xo+++wzwsLCAKhQoQL9+/e/54N2RR7IryLUetXydfNRkMPNuXkyqW3HLjNu9RH+OnMdAB/PnLzWqBShdUrg6aYxiCIiD8vhwumrr77ijTfeoF27drzxxhsA7Nixg1atWvHZZ5/Rp0+fVA8pWdTZPeBdBPJYJjulxUeWh/SKw/46c52PVx9hy7HLAHjmdKX740H0bFASH0/NdyUiklocvlRXtGhRBg0aRN++fW3av/zyS8aMGcPZs2dTNWBq06W6DMAwYM9sWPk2FK0FnZeCa4onsc/Wjl28wSer/2bVQcujh3K6mnixVjH6PFFaD9wVEbFTml6qu379Oi1atEjS3rx5c959911HdyfZTdwt+GUg7PvBsuzhA/G3wVXPGXTE2eu3mLj2bxbu+RezASYTPFu9CG82LUtg/lzOjicikmU5XDg9/fTTLF68mLffftumfenSpTz1lObakfu4dsoy1cD5/4HJBZ4YAvX66/KcA65ExfDlhuP8sOMfYhMsk1c2C/ZjYPNylPPP4+R0IiJZn12F06RJk6xfBwcHM3r0aDZu3EidOpan0u/YsYOtW7fy1ltvpU1KyfyOroWFPeD2dchVANrNgJKNnJ0q07hxO47pm0/y7eYT3IxNAOCxkvl5p0V5Hinm+MO1RUQkZewa4xQUFGTfzkwmTpw48dCh0pLGODlBQrxlqoHLR6BITWg/2/LcOXmg23EJ/LDjH77ccIxr0XEAVC7iw9sh5ahfpqAmrxQRSQWpPsbp5MmTqRJMsinXHND+O9g9C5qNgBzuzk6U4cUnmFmw+18+X3+U8IjbAJQs5MXA5uVoWclfBZOIiJOk+Famy5cttz0XLFgw1cJIFnJun+WxKdVftiz7loeWHzk1UmZgNhv8euA8E9Yc4cTlmwAE+HjQv2kZnnukKDk0eaWIiFM5VDhdv36dwYMHM2/ePK5duwZAvnz5eOGFFxg1ahR58+ZNi4yS2ez9AVYMAHM8FCgNxR5zdqIMzzAMNh29zMerD3PgbCQA+XLlpE/j0rz8WHE8cmryShGRjMDuwunq1avUqVOHs2fP8tJLL1GhQgUADh06xKxZs1i/fj3btm0jXz4NVM224m7Dr+9Y5mgCKNsCCpVzbqZMYM/pa4xfdZgdJ64C4OXmSo/6JelRP4g8Hpq8UkQkI7G7cBo5ciRubm4cP34cPz+/JO81b96ckSNH8tlnn6V6SMkErp+G+Z3h3F7ABI0HQ/23NNXAfRw5f4OPVx9hXdgFANxcXXj5seL0aVyKArk1DkxEJCOye+bwEiVKMG3aNEJCQpJ9f9WqVfTq1YtTp06lZr5Up7vq0sDx32BBd7h1FTzzwXPfQOnM8dzCBLPBrpNXuXjjNr55PKgVlB9Xl4cfeH2//Z65Gs1na/9m8b6zGAa4mKBdjaK80bQsRfJ6PvSxRUTEMWkyc3h4eDgVK1a85/uVKlXi/Pnz9qeUrONimKVoKlzdcvdc3mLOTmSXVQfCGbH8kPWuNbAMxB7WOpgWlQJSfb/9m5bh4LlIftp1mrgEy79XWlby563mZSntq8krRUQyA7sLp4IFC3Lq1CmKFk1+/p2TJ0+SP3/+VAsmmchjvcHNC6q8ADkzx/PRVh0I57Uf9nB3d+v5iNu89sMeprz8SIqKp3vtNzziNu8u3G9dfrx0Qd4OKUfVwLwOH0NERJzH7gEoISEhDB48mNjY2CTvxcTEMGTIkGSfYSdZ0Pn9MOd5uG25+wuTCWp0yTRFU4LZYMTyQ0mKG8DaNmL5IRLMDj3/+r77TZTT1cQP3WrxQ4/aKppERDIhhwaH16xZkzJlytCnTx/Kly+PYRiEhYXx1VdfERMTw/fff5+WWSUj2PcTrHgT4m/B+pHw5CfOTuSwXSev2lxGu5uBpYeo0ccb8HK3f8aOmzHx990vQFyCgavmYhIRybTs/qtQtGhRtm/fTu/evXnvvfdIHFNuMplo1qwZkydPJjAwMM2CipPFx8Cq9+DPby3LpZtC4/edmymFLt64f3GT6My1W049voiIZDwOTYAZFBTEr7/+yrVr1zh69CgApUuX1timrC7iX5gfCmf/BEzQ8F1o+A64ZM5JGd1z2Nfj836rCgQH2H/n5aHwSMasDHvger55MsclTRERSSpFj1zJly8ftWrVSu0skhH9uxt+fB6ir4CHD7T9Bso2d3aqFDEMg2V/nWPo0gP3Xc8E+Pt40P3xIIemJqhTqgAzt57kfMTtZMc5Je63VpD+oSEikllpsIXcX95AcHUD/8rwyu+Ztmi6eOM2r36/mzfm7iPiVjyB+SzzJd1dFiUuD2sd7PB8Tq4uJoa1Dk71/YqISMahwkmSirtjDE5uX+i8DLqvhfxBzsuUQoZhsHTfWZp/tok1hy6Q09XEgGZl+W1gI6a+/Aj+PraXzfx9PFI8FQFAi0oBTEmD/YqISMZg98zhWYVmDn+AC4dg3svQaBBUae/sNA/lYuRtBi85wNpDlkeaVCrizcftqlLhjnFLzpg5XEREMpY0mTlcsoH9C2BZP4iLhk0fQ8W24Jr5fkQMw2DJvrMMX3aIiFtx5HQ18UaTMrzasBQ575oKwNXFRJ1SBVI9Q1rtV0REnCvz/VWU1BcfC2s+gF3TLMslG8FzMzJl0XQx8jbvLz5gfXBupSLefPJ8Vcr7q3dRREQeXub7yyipKzIcfg6FMzsty/XfgsaDM91UA4ZhsHjvWYYvO0jk7Xhyupro37QsrzQomaSXSUREJKVUOGVnt67D1w0h6gK4e8Oz06B8K2enctiFyNu8v2g/6w9fBKBKUR8+bleVcv56cK6IiKQuFU7ZmWdeqPYS/L0aOnwPBUo91O7SckB0cvt2McGiPWcZsdzSy+Tm6sIbTcvwaoOS5FAvk4iIpAHdVZfdxNyA2JuQx9+ybE6wPE7FLddD7XbVgXBGLD9k86y2AB8PhrUOfuhb8JPbt28ed3y93Tlw1vKg4apFffj4+aqU9VMvk4iIOMaR2kCFU3Zy6YhlqgH3PND1V8jhniq7XXUgnNd+2JNktuzEvqaHmb/oXvtOlMPFxFvNy9GzfpB6mUREJEU0HYEkdXAxLO0LsVGQpzBcPwMFSz/0bhPMBiOWH0q2sElsG7r0IJWK+Dh82S7BbDB06cF7Fk0A+XK58UqDkpojSURE0oUKp6wuIQ7WDoMdX1qWS9SHdjMhd6FU2f2uk1dtLqEl5+KNGB4ftyFVjne3S1Ex7Dp5VXMmiYhIulDhlJXduAA/d4HT2yzL9d6AJ4am6vxMF2/cv2hK5GoypajHKcGOK8n2ZhAREXlYKpyysqW9LUWTWx5o8xUEP53qh/DN4/HglYAfetR2uFdo+/ErdJy+I9UyiIiIPCyNps3KWn0MgY/BKxvSpGgCqBWUnwAfD+7Vl2TCcnddraD8GWrfIiIiKaHCKSuJiYKwFf8t5y8J3VZBwTJpdkhXFxPDWgcDJClwEpeHtQ5O0eDttNy3iIhISqhwyiouH4VvmlimGzi2/r92U9oXFS0qBTDl5Ufw97G9ZObv4/FQUxGk9b5FREQcpTFOWcGhZbCkN8TegNz+4OaV7hFaVAqgWbB/mswcnpb7FhERcYQKp8wsIR5+GwlbP7csF69nmWogj59T4ri6mNJsWoC03LeIiIi9VDhlVlGXYEFXOLXZslynLzQdDq45nRpLREQkK1PhlFkdW2cpmnJ6wTOToVJbZycSERHJ8lQ4ZVbVOsL1f6Dis1ConLPTiIiIZAu6qy6ziI2G1YMh+up/bY0GqWgSERFJR+pxygyuHId5neDiQbhyDF6c5+xEIiIi2ZIKp4zu8EpY3AtiIsDLF+q+7uxEIiIi2ZYKp4zKnAAbxsDmTyzLgbXh+dngrQkfRUREnEWFU0YUfRUWdIMTGyzLtXtBsw8hh5tzc4mIiGRzKpwyqqsnIGcuePoLqNzO2WlEREQEFU4Zh2FY/msyQa788MIcMLmCX7Bzc4mIiIiVpiPICOJuwdI+sHvWf23+lVU0iYiIZDDqcXK2qydhfic4vx8OLILyT0HuQs5OJSIiIslQ4eRMf6+BRT3gdgTkKgjtZqhoEhERycBUODmDOQF+H2d5ARSpCe2/A58izs0lIiIi96XCKb2ZzfBTRzi62rL8aA8IGQM53J2bS0RERB5Ig8PTm4sLBD4KOTzh2Wnw5AQVTSIiIpmEepzSS8wNcM9j+frxt6BiWyhQyrmZRERExCHqcUprcbdhWT+Y0QJioy1tLi4qmkRERDKhDFE4ffnll5QoUQIPDw9q167Nrl277rv+zz//TPny5fHw8KBy5cqsXLkynZI66PppmBECe76DCwfh5O/OTiQiIiIPwemF07x58xgwYADDhg1jz549VK1alZCQEC5evJjs+tu2baNjx450796dvXv30qZNG9q0acOBAwfSOfkDHFsH0xpA+D7wzA8vL4RyLZ2dSkRERB6CyTASn/XhHLVr1+bRRx9l8uTJAJjNZgIDA+nXrx+DBg1Ksn6HDh24efMmK1assLY99thjVKtWjalTpz7weJGRkfj4+BAREYG3t3fqfZBEZjNs/gQ2jAEMKFzdMtVA3mKpfywRERF5aI7UBk7tcYqNjWX37t00bdrU2ubi4kLTpk3Zvn17stts377dZn2AkJCQe64fExNDZGSkzStN/TYSNowGDKjRBbquUtEkIiKSRTi1cLp8+TIJCQn4+fnZtPv5+XH+/Plktzl//rxD648dOxYfHx/rKzAwMHXC38ujPcEnEJ75Elp/Djk90vZ4IiIikm6cPsYprb333ntERERYX2fOnEnbA/oUgb5/QvWX0/Y4IiIiku6cOo9TwYIFcXV15cKFCzbtFy5cwN/fP9lt/P39HVrf3d0dd/d0nmBSvUwiIiJZklN7nNzc3KhRowbr16+3tpnNZtavX0+dOnWS3aZOnTo26wOsXbv2nuuLiIiIpBanzxw+YMAAQkNDqVmzJrVq1WLixIncvHmTrl27AtC5c2eKFCnC2LFjAXjjjTdo2LAhEyZM4Mknn2Tu3Ln8+eeffP311878GCIiIpINOL1w6tChA5cuXWLo0KGcP3+eatWqsWrVKusA8NOnT+Pi8l/HWN26dfnxxx/54IMPeP/99ylTpgxLliyhUqVKzvoIIiIikk04fR6n9Jbm8ziJiIhIppJp5nESERERyUxUOImIiIjYSYWTiIiIiJ1UOImIiIjYSYWTiIiIiJ1UOImIiIjYSYWTiIiIiJ1UOImIiIjYSYWTiIiIiJ1UOImIiIjYSYWTiIiIiJ2c/pDf9Jb4aL7IyEgnJxEREZGMILEmsOfxvdmucLpx4wYAgYGBTk4iIiIiGcmNGzfw8fG57zomw57yKgsxm82cO3eOPHnyYDKZUn3/kZGRBAYGcubMmQc+YVlSj867c+i8O4/OvXPovDtHWp93wzC4ceMGhQsXxsXl/qOYsl2Pk4uLC0WLFk3z43h7e+t/KifQeXcOnXfn0bl3Dp1350jL8/6gnqZEGhwuIiIiYicVTiIiIiJ2UuGUytzd3Rk2bBju7u7OjpKt6Lw7h8678+jcO4fOu3NkpPOe7QaHi4iIiKSUepxERERE7KTCSURERMROKpxERERE7KTCKQW+/PJLSpQogYeHB7Vr12bXrl33Xf/nn3+mfPnyeHh4ULlyZVauXJlOSbMWR8779OnTqV+/Pvny5SNfvnw0bdr0gd8nSZ6jP++J5s6di8lkok2bNmkbMAtz9Nxfv36dPn36EBAQgLu7O2XLltXvmxRw9LxPnDiRcuXK4enpSWBgIG+++Sa3b99Op7RZw6ZNm2jdujWFCxfGZDKxZMmSB26zceNGHnnkEdzd3SldujSzZs1K85wAGOKQuXPnGm5ubsaMGTOMgwcPGj179jTy5s1rXLhwIdn1t27dari6uhrjx483Dh06ZHzwwQdGzpw5jf3796dz8szN0fP+4osvGl9++aWxd+9eIywszOjSpYvh4+Nj/Pvvv+mcPHNz9LwnOnnypFGkSBGjfv36xjPPPJM+YbMYR899TEyMUbNmTaNVq1bGli1bjJMnTxobN2409u3bl87JMzdHz/ucOXMMd3d3Y86cOcbJkyeN1atXGwEBAcabb76Zzskzt5UrVxqDBw82Fi1aZADG4sWL77v+iRMnjFy5chkDBgwwDh06ZHzxxReGq6ursWrVqjTPqsLJQbVq1TL69OljXU5ISDAKFy5sjB07Ntn127dvbzz55JM2bbVr1zZeffXVNM2Z1Th63u8WHx9v5MmTx5g9e3ZaRcySUnLe4+Pjjbp16xrffPONERoaqsIphRw991OmTDFKlixpxMbGplfELMnR896nTx/jiSeesGkbMGCAUa9evTTNmZXZUzi98847RsWKFW3aOnToYISEhKRhMgtdqnNAbGwsu3fvpmnTptY2FxcXmjZtyvbt25PdZvv27TbrA4SEhNxzfUkqJef9btHR0cTFxZE/f/60ipnlpPS8jxw5El9fX7p3754eMbOklJz7ZcuWUadOHfr06YOfnx+VKlVizJgxJCQkpFfsTC8l571u3brs3r3bejnvxIkTrFy5klatWqVL5uzKmX9bs92z6h7G5cuXSUhIwM/Pz6bdz8+Pw4cPJ7vN+fPnk13//PnzaZYzq0nJeb/bu+++S+HChZP8jyb3lpLzvmXLFr799lv27duXDgmzrpSc+xMnTvDbb7/x0ksvsXLlSo4dO0bv3r2Ji4tj2LBh6RE700vJeX/xxRe5fPkyjz/+OIZhEB8fT69evXj//ffTI3K2da+/rZGRkdy6dQtPT880O7Z6nCTL++ijj5g7dy6LFy/Gw8PD2XGyrBs3btCpUyemT59OwYIFnR0n2zGbzfj6+vL1119To0YNOnTowODBg5k6daqzo2VpGzduZMyYMXz11Vfs2bOHRYsW8csvv/Dhhx86O5qkEfU4OaBgwYK4urpy4cIFm/YLFy7g7++f7Db+/v4OrS9JpeS8J/rkk0/46KOPWLduHVWqVEnLmFmOo+f9+PHjnDp1itatW1vbzGYzADly5ODIkSOUKlUqbUNnESn5mQ8ICCBnzpy4urpa2ypUqMD58+eJjY3Fzc0tTTNnBSk570OGDKFTp0706NEDgMqVK3Pz5k1eeeUVBg8ejIuL+ifSwr3+tnp7e6dpbxOox8khbm5u1KhRg/Xr11vbzGYz69evp06dOsluU6dOHZv1AdauXXvP9SWplJx3gPHjx/Phhx+yatUqatasmR5RsxRHz3v58uXZv38/+/bts76efvppGjduzL59+wgMDEzP+JlaSn7m69Wrx7Fjx6zFKsDff/9NQECAiiY7peS8R0dHJymOEotXQ080SzNO/dua5sPPs5i5c+ca7u7uxqxZs4xDhw4Zr7zyipE3b17j/PnzhmEYRqdOnYxBgwZZ19+6dauRI0cO45NPPjHCwsKMYcOGaTqCFHD0vH/00UeGm5ubsWDBAiM8PNz6unHjhrM+Qqbk6Hm/m+6qSzlHz/3p06eNPHnyGH379jWOHDlirFixwvD19TVGjRrlrI+QKTl63ocNG2bkyZPH+Omnn4wTJ04Ya9asMUqVKmW0b9/eWR8hU7px44axd+9eY+/evQZgfPrpp8bevXuNf/75xzAMwxg0aJDRqVMn6/qJ0xG8/fbbRlhYmPHll19qOoKM7IsvvjCKFStmuLm5GbVq1TJ27Nhhfa9hw4ZGaGiozfrz5883ypYta7i5uRkVK1Y0fvnll3ROnDU4ct6LFy9uAElew4YNS//gmZyjP+93UuH0cBw999u2bTNq165tuLu7GyVLljRGjx5txMfHp3PqzM+R8x4XF2cMHz7cKFWqlOHh4WEEBgYavXv3Nq5du5b+wTOxDRs2JPs7O/Fch4aGGg0bNkyyTbVq1Qw3NzejZMmSxsyZM9Mlq8kw1JcoIiIiYg+NcRIRERGxkwonERERETupcBIRERGxkwonERERETupcBIRERGxkwonERERETupcBIRERGxkwonERERETupcBKRDK9Lly60adPGutyoUSP69++f7jk2btyIyWTi+vXr6X7sU6dOYTKZ2Ldv30Pt5+5zmZy7z2+JEiWYOHGiddlkMrFkyZKHyiGSWalwEslAunTpgslkolevXkne69OnDyaTiS5duqR/sAxm0aJFfPjhh3at68xiJ7N60PkNDw+nZcuWQOoVdCKZhQonkQwmMDCQuXPncuvWLWvb7du3+fHHHylWrJgTkz2c2NjYVNtX/vz5yZMnT6rtz9ni4uKcHcHGg86vv78/7u7u6ZhIJONQ4SSSwTzyyCMEBgayaNEia9uiRYsoVqwY1atXt1nXbDYzduxYgoKC8PT0pGrVqixYsMD6fkJCAt27d7e+X65cOT7//HObfSReuvnkk08ICAigQIEC9OnT575/zIcPH061atWYNm0agYGB5MqVi/bt2xMREZFkv6NHj6Zw4cKUK1cOgDNnztC+fXvy5s1L/vz5eeaZZzh16pRN5gEDBpA3b14KFCjAO++8w92P1Lz7UlJMTAzvvvsugYGBuLu7U7p0ab799ltOnTpF48aNAciXL59Nj92Dzh3AypUrKVu2LJ6enjRu3Ngm572YTCamTJlCy5Yt8fT0pGTJkjb7TeyhmTdvHg0bNsTDw4M5c+ZgNpsZOXIkRYsWxd3dnWrVqrFq1aok+z98+DB169bFw8ODSpUq8fvvv9ucuwd9vxONGDGCQoUK4e3tTa9evWwK2wddCr3zUl1QUBAA1atXx2Qy0ahRIzZt2kTOnDk5f/68zXb9+/enfv36DzyHIhmZCieRDKhbt27MnDnTujxjxgy6du2aZL2xY8fy3XffMXXqVA4ePMibb77Jyy+/bP1jajabKVq0KD///DOHDh1i6NChvP/++8yfP99mPxs2bOD48eNs2LCB2bNnM2vWLGbNmnXfjMeOHWP+/PksX76cVatWsXfvXnr37m2zzvr16zly5Ahr165lxYoVxMXFERISQp48edi8eTNbt24ld+7ctGjRwvqHe8KECcyaNYsZM2awZcsWrl69yuLFi++bpXPnzvz0009MmjSJsLAwpk2bRu7cuQkMDGThwoUAHDlyhPDwcGsh8aBzd+bMGdq2bUvr1q3Zt28fPXr0YNCgQffNkWjIkCE899xz/PXXX7z00ku88MILhIWF2awzaNAg3njjDcLCwggJCeHzzz9nwoQJfPLJJ/zvf/8jJCSEp59+mqNHj9ps9/bbb/PWW2+xd+9e6tSpQ+vWrbly5Qpg//d7/fr1hIWFsXHjRn766ScWLVrEiBEj7Ppsd9u1axcA/9fevYVE1bVxAP/PjOUhnYvwkKM4naTGUMMsM0EvOhhJDVkpEjXiFJmKUioZJCqdxiKKwqwb00RTwhwkUynIMmO0k4OgjimGiVISYox5nvVefMym7Ti26/N9X/2+53flXmvttdda+2Ie9nr29tmzZxgYGMCjR48QGhqK1atXo7i4mGs3OTmJkpISxMXF/dF1CFkwGCFkwVCpVEypVLKvX78yW1tb9unTJ/bp0ydmZ2fHBgcHmVKpZCqVijHG2NjYGHNwcGCvX7/m9aFWq1lMTIzVayQmJrIDBw7wrimXy9nU1BRXdujQIRYdHW21j6ysLCaRSFhfXx9XVlNTw8RiMRsYGOD6dXNzY+Pj41yb4uJitm7dOmYymbiy8fFxZm9vz+rq6hhjjLm7u7MrV65w9ZOTk8zT05MplUquLCwsjKWkpDDGGDMYDAwAe/r06axjff78OQPAhoaGuDIha3f27Fnm4+PDqz9z5oxFXzMBYPHx8byyoKAgdvLkScYYYz09PQwAu3HjBq+NTCZjFy9e5JVt3ryZJSQk8M7TaDRcvXltcnNzrY5ntvu9fPlyNjIywpXl5+czR0dHNj09zRjjry9jjMnlcnb9+nXeHCsrK3nj+vDhA++6ubm5TKFQcMcVFRXM0dGRGY1Gq2MlZDGw+fdCNkKINS4uLoiIiEBhYSEYY4iIiICzszOvTVdXF378+IGdO3fyyicmJnhbenl5eSgoKEBvby9GR0cxMTGBjRs38s7ZsGEDJBIJd+zu7o7W1tY5x+jl5QUPDw/uODg4GCaTCQaDAStWrAAA+Pr6YunSpVwbvV6Prq4ui/yZsbExdHd3Y3h4GAMDAwgKCuLqbGxsEBgYaLFdZ9bS0gKJRIKwsLA5x/szIWvX3t7OG4d5jkLMbBccHGyRPB0YGMj9/f37d/T39yMkJITXJiQkBHq93mrf5rX5+WmWkPvt7+8PBwcHXp9GoxGfP3+GXC4XNMdfiY2Nxblz56DT6bB161YUFhYiKioKy5Ytm5f+Cfm3UOBEyAIVFxeHpKQkAP/5MZzJaDQCAKqrq3kBDAAucbesrAxpaWm4du0agoOD4eTkhKtXr6KpqYnXfsmSJbxjkUgEk8n0X89h5o+k0WjEpk2bUFJSYtHWxcXlj65hb2//2+cIWbu/298RQAi93/8EV1dX7N27F/fu3cOqVatQU1OD+vr6f3wchMw3CpwIWaDMeT8ikQjh4eEW9T4+PrC1tUVvb6/Vpy2NjY3Ytm0bL/eou7t7XsbX29uL/v5+yGQyAIBOp4NYLOaSwGcTEBCA8vJyuLq6QiqVztrG3d0dTU1NCA0NBQBMTU3h3bt3CAgImLW9r68vTCYTXrx4gR07dljUm594TU9Pc2VC1k6hUKCqqopXptPprM5tZrujR4/yjmcm9v9MKpVCJpOhsbGRN57GxkZs2bLFou+Za2MOsIXeb71ej9HRUS7o1Ol0XE7Y75ptfc2OHTuGmJgYeHp6Ys2aNRZP1AhZjCg5nJAFSiKRoL29HW1tbbxtNDMnJyekpaXh1KlTKCoqQnd3N96/f49bt26hqKgIAODt7Y23b9+irq4OnZ2dyMzMxJs3b+ZlfHZ2dlCpVNDr9WhoaEBycjKioqK4bbrZHD58GM7OzlAqlWhoaEBPTw/q6+uRnJyMvr4+AEBKSgo0Gg20Wi06OjqQkJAw5zeYVq5cCZVKhbi4OGi1Wq5Pc0K0XC6HSCTC48ePMTg4CKPRKGjt4uPj8fHjR6Snp8NgMKC0tPSXCfNmDx8+REFBATo7O5GVlYXm5mYuuLEmPT0dubm5KC8vh8FgQEZGBlpaWpCSksJrl5eXh8rKSnR0dCAxMRFDQ0NcwrXQ+z0xMQG1Wo22tjY8efIEWVlZSEpKglj8+z8Jrq6usLe3R21tLb58+cJ7szI8PBxSqRQXLlyY9eUGQhYjCpwIWcCkUqnVJzMAcP78eWRmZuLy5ctQKBTYvXs3qquruVfET5w4gcjISERHRyMoKAjfvn2zePPtT61duxaRkZHYs2cPdu3aBT8/P9y+fXvOcxwcHPDy5Ut4eXkhMjISCoUCarUaY2Nj3DxTU1Nx5MgRqFQqbrtp//79c/abn5+PgwcPIiEhAevXr8fx48cxMjICAPDw8EBOTg4yMjLg5ubGBTC/WjsvLy9UVFRAq9XC398fd+7cwaVLlwStTU5ODsrKyuDn54f79+/jwYMH8PHxmfOc5ORknD59GqmpqfD19UVtbS2qqqrg7e3Na6fRaKDRaODv749Xr16hqqqKy38Ter+3b98Ob29vhIaGIjo6Gvv27UN2draguc1kY2ODmzdv4u7du5DJZFAqlVydWCxGbGwspqeneU/gCFnMRMxaxiUhhFiRnZ0NrVZLX4uehUgkQmVl5S//rcn/C7VajcHBQYttT0IWK8pxIoQQMu+Gh4fR2tqK0tJSCprI/xQKnAghhMw7pVKJ5uZmxMfHW3z2gZDFjLbqCCGEEEIEouRwQgghhBCBKHAihBBCCBGIAidCCCGEEIEocCKEEEIIEYgCJ0IIIYQQgShwIoQQQggRiAInQgghhBCBKHAihBBCCBGIAidCCCGEEIH+AnCWG+X9tjy0AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Reliability diagram for the severe-delinquency probability.\n",
        "for model_name in [\n",
        "    \"Logistic Regression\",\n",
        "    \"Random Forest\",\n",
        "]:\n",
        "    probabilities = model_evaluations[model_name][\"probabilities\"]\n",
        "    focus_probability = probabilities[PRIMARY_FOCUS_CLASS]\n",
        "    focus_true = y_test.eq(PRIMARY_FOCUS_CLASS).astype(int)\n",
        "\n",
        "    observed_frequency, mean_probability = calibration_curve(\n",
        "        focus_true,\n",
        "        focus_probability,\n",
        "        n_bins=5,\n",
        "        strategy=\"quantile\",\n",
        "    )\n",
        "\n",
        "    plt.figure(figsize=(6, 5))\n",
        "    plt.plot(\n",
        "        mean_probability,\n",
        "        observed_frequency,\n",
        "        marker=\"o\",\n",
        "        label=model_name,\n",
        "    )\n",
        "    plt.plot(\n",
        "        [0, 1],\n",
        "        [0, 1],\n",
        "        linestyle=\"--\",\n",
        "        label=\"Perfect calibration\",\n",
        "    )\n",
        "    plt.xlabel(\"Mean predicted probability\")\n",
        "    plt.ylabel(\"Observed severe-delinquency frequency\")\n",
        "    plt.title(f\"{model_name}: severe-class calibration\")\n",
        "    plt.legend()\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "131a0100",
      "metadata": {
        "id": "131a0100"
      },
      "source": [
        "ROC-AUC measures ranking rather than final classification decisions and can appear acceptable under class imbalance. Precision–recall curves and average precision are therefore more informative for the severe class. Calibration is also operationally important: a predicted probability should correspond to an observed event frequency if the score is to support pricing, limits or human-review prioritisation."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "881c19f5",
      "metadata": {
        "id": "881c19f5"
      },
      "source": [
        "## 14. Bootstrap uncertainty intervals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "260ebb8b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "260ebb8b",
        "outputId": "17297a94-55f2-44b1-a68b-d99ae497a9ab"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 model                     metric  estimate  lower  upper  \\\n",
              "0  Logistic Regression                   Macro F1    0.3667 0.2896 0.4486   \n",
              "1  Logistic Regression  Severe Delinquency recall    0.0667 0.0000 0.2361   \n",
              "2        Random Forest                   Macro F1    0.4319 0.3227 0.5284   \n",
              "3        Random Forest  Severe Delinquency recall    0.3333 0.0909 0.6000   \n",
              "\n",
              "   successful_iterations  \n",
              "0                    750  \n",
              "1                    750  \n",
              "2                    750  \n",
              "3                    750  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>metric</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>Macro F1</td>\n",
              "      <td>0.3667</td>\n",
              "      <td>0.2896</td>\n",
              "      <td>0.4486</td>\n",
              "      <td>750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>Severe Delinquency recall</td>\n",
              "      <td>0.0667</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2361</td>\n",
              "      <td>750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>Macro F1</td>\n",
              "      <td>0.4319</td>\n",
              "      <td>0.3227</td>\n",
              "      <td>0.5284</td>\n",
              "      <td>750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>Severe Delinquency recall</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.0909</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>750</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"confidence_table\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Random Forest\",\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Severe Delinquency recall\",\n          \"Macro F1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estimate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1606220804305762,\n        \"min\": 0.06666666666666667,\n        \"max\": 0.4318996415770609,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.06666666666666667,\n          0.3333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15560958843530742,\n        \"min\": 0.0,\n        \"max\": 0.3226921889038635,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.09090909090909091\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15746359227503268,\n        \"min\": 0.23605769230769189,\n        \"max\": 0.6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.23605769230769189,\n          0.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"successful_iterations\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 750,\n        \"max\": 750,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          750\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
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      ],
      "source": [
        "def bootstrap_confidence_interval(\n",
        "    y_true,\n",
        "    y_pred,\n",
        "    metric_function,\n",
        "    iterations=1000,\n",
        "    confidence=0.95,\n",
        "    random_state=42,\n",
        "):\n",
        "    rng = np.random.default_rng(random_state)\n",
        "    y_true_array = np.asarray(y_true)\n",
        "    y_pred_array = np.asarray(y_pred)\n",
        "    sample_size = len(y_true_array)\n",
        "    metric_values = []\n",
        "\n",
        "    for _ in range(iterations):\n",
        "        sample_indices = rng.integers(\n",
        "            0,\n",
        "            sample_size,\n",
        "            sample_size,\n",
        "        )\n",
        "        sampled_true = y_true_array[sample_indices]\n",
        "        sampled_pred = y_pred_array[sample_indices]\n",
        "\n",
        "        if len(np.unique(sampled_true)) < len(PRIMARY_CLASS_ORDER):\n",
        "            continue\n",
        "\n",
        "        metric_values.append(\n",
        "            metric_function(\n",
        "                sampled_true,\n",
        "                sampled_pred,\n",
        "            )\n",
        "        )\n",
        "\n",
        "    alpha = 1 - confidence\n",
        "    lower = np.quantile(metric_values, alpha / 2)\n",
        "    upper = np.quantile(metric_values, 1 - alpha / 2)\n",
        "\n",
        "    return {\n",
        "        \"estimate\": metric_function(\n",
        "            y_true_array,\n",
        "            y_pred_array,\n",
        "        ),\n",
        "        \"lower\": lower,\n",
        "        \"upper\": upper,\n",
        "        \"successful_iterations\": len(metric_values),\n",
        "    }\n",
        "\n",
        "\n",
        "def macro_f1_metric(y_true_sample, y_pred_sample):\n",
        "    return f1_score(\n",
        "        y_true_sample,\n",
        "        y_pred_sample,\n",
        "        labels=PRIMARY_CLASS_ORDER,\n",
        "        average=\"macro\",\n",
        "        zero_division=0,\n",
        "    )\n",
        "\n",
        "\n",
        "def severe_recall_metric(y_true_sample, y_pred_sample):\n",
        "    return recall_score(\n",
        "        np.asarray(y_true_sample) == PRIMARY_FOCUS_CLASS,\n",
        "        np.asarray(y_pred_sample) == PRIMARY_FOCUS_CLASS,\n",
        "        zero_division=0,\n",
        "    )\n",
        "\n",
        "\n",
        "confidence_rows = []\n",
        "\n",
        "for model_name in [\n",
        "    \"Logistic Regression\",\n",
        "    \"Random Forest\",\n",
        "]:\n",
        "    predictions = model_evaluations[model_name][\"predictions\"]\n",
        "\n",
        "    confidence_rows.extend([\n",
        "        {\n",
        "            \"model\": model_name,\n",
        "            \"metric\": \"Macro F1\",\n",
        "            **bootstrap_confidence_interval(\n",
        "                y_test,\n",
        "                predictions,\n",
        "                macro_f1_metric,\n",
        "                iterations=BOOTSTRAP_ITERATIONS,\n",
        "                random_state=RANDOM_STATE,\n",
        "            ),\n",
        "        },\n",
        "        {\n",
        "            \"model\": model_name,\n",
        "            \"metric\": f\"{PRIMARY_FOCUS_CLASS} recall\",\n",
        "            **bootstrap_confidence_interval(\n",
        "                y_test,\n",
        "                predictions,\n",
        "                severe_recall_metric,\n",
        "                iterations=BOOTSTRAP_ITERATIONS,\n",
        "                random_state=RANDOM_STATE,\n",
        "            ),\n",
        "        },\n",
        "    ])\n",
        "\n",
        "confidence_table = pd.DataFrame(confidence_rows)\n",
        "display(confidence_table)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e7af4475",
      "metadata": {
        "id": "e7af4475"
      },
      "source": [
        "The interval width is evidence about statistical uncertainty. The primary cohort contains substantially more usable outcome information than the full snapshot, but the severe class remains small. Point estimates should therefore not be presented without their uncertainty or class support."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2a4ded3a",
      "metadata": {
        "id": "2a4ded3a"
      },
      "source": [
        "## 15. Cost-sensitive severe-delinquency threshold analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "fa6cca45",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fa6cca45",
        "outputId": "013823e1-4168-4768-f1b2-4665e6a63e59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training-only selected severe-class threshold: 0.17658768328347701\n",
            "Out-of-fold precision: 0.14035087719298245\n",
            "Out-of-fold recall: 0.9655172413793104\n",
            "Out-of-fold F2: 0.44374009508687257\n"
          ]
        }
      ],
      "source": [
        "# Select the threshold using training-only out-of-fold probabilities.\n",
        "candidate_oof_probability = cross_val_predict(\n",
        "    champion,\n",
        "    X_train,\n",
        "    y_train,\n",
        "    cv=threshold_cv,\n",
        "    method=\"predict_proba\",\n",
        "    n_jobs=N_JOBS,\n",
        ")\n",
        "\n",
        "candidate_classes = extract_estimator_classes(champion)\n",
        "focus_index = int(\n",
        "    np.where(candidate_classes == PRIMARY_FOCUS_CLASS)[0][0]\n",
        ")\n",
        "\n",
        "oof_focus_true = (\n",
        "    y_train.eq(PRIMARY_FOCUS_CLASS)\n",
        "    .astype(int)\n",
        "    .to_numpy()\n",
        ")\n",
        "oof_focus_probability = candidate_oof_probability[:, focus_index]\n",
        "\n",
        "threshold_precision, threshold_recall, thresholds = (\n",
        "    precision_recall_curve(\n",
        "        oof_focus_true,\n",
        "        oof_focus_probability,\n",
        "    )\n",
        ")\n",
        "\n",
        "beta = 2\n",
        "f_beta = (\n",
        "    (1 + beta**2)\n",
        "    * threshold_precision[:-1]\n",
        "    * threshold_recall[:-1]\n",
        "    / (\n",
        "        beta**2 * threshold_precision[:-1]\n",
        "        + threshold_recall[:-1]\n",
        "        + 1e-12\n",
        "    )\n",
        ")\n",
        "\n",
        "best_threshold_position = int(np.nanargmax(f_beta))\n",
        "selected_focus_threshold = float(\n",
        "    thresholds[best_threshold_position]\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Training-only selected severe-class threshold:\",\n",
        "    selected_focus_threshold,\n",
        ")\n",
        "print(\n",
        "    \"Out-of-fold precision:\",\n",
        "    threshold_precision[best_threshold_position],\n",
        ")\n",
        "print(\n",
        "    \"Out-of-fold recall:\",\n",
        "    threshold_recall[best_threshold_position],\n",
        ")\n",
        "print(\n",
        "    \"Out-of-fold F2:\",\n",
        "    f_beta[best_threshold_position],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "c2235acf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "c2235acf",
        "outputId": "f9dc0052-ab05-4132-eaee-6437d51e5709"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(\n",
        "    thresholds,\n",
        "    threshold_precision[:-1],\n",
        "    label=\"Precision\",\n",
        ")\n",
        "plt.plot(\n",
        "    thresholds,\n",
        "    threshold_recall[:-1],\n",
        "    label=\"Recall\",\n",
        ")\n",
        "plt.axvline(\n",
        "    selected_focus_threshold,\n",
        "    linestyle=\"--\",\n",
        "    label=\"Selected F2 threshold\",\n",
        ")\n",
        "plt.xlabel(f\"{PRIMARY_FOCUS_CLASS} probability threshold\")\n",
        "plt.ylabel(\"Metric value\")\n",
        "plt.title(f\"{champion_name}: training-only threshold trade-off\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "e782631f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e782631f",
        "outputId": "2357a766-d083-44c8-e7e9-02eef4006594"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                 accuracy  balanced_accuracy  macro_precision  \\\n",
              "model                                                                           \n",
              "Random Forest with F2 threshold    0.3280             0.3938           0.3310   \n",
              "\n",
              "                                 macro_recall  macro_f1  weighted_f1  \\\n",
              "model                                                                  \n",
              "Random Forest with F2 threshold        0.3938    0.2355       0.3565   \n",
              "\n",
              "                                 focus_precision  focus_recall  focus_f1  \n",
              "model                                                                     \n",
              "Random Forest with F2 threshold           0.1444        0.8667    0.2476  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>balanced_accuracy</th>\n",
              "      <th>macro_precision</th>\n",
              "      <th>macro_recall</th>\n",
              "      <th>macro_f1</th>\n",
              "      <th>weighted_f1</th>\n",
              "      <th>focus_precision</th>\n",
              "      <th>focus_recall</th>\n",
              "      <th>focus_f1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Random Forest with F2 threshold</th>\n",
              "      <td>0.3280</td>\n",
              "      <td>0.3938</td>\n",
              "      <td>0.3310</td>\n",
              "      <td>0.3938</td>\n",
              "      <td>0.2355</td>\n",
              "      <td>0.3565</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.8667</td>\n",
              "      <td>0.2476</td>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d173f6b3-d041-44cc-be93-23aea1f8e1d2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-d173f6b3-d041-44cc-be93-23aea1f8e1d2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d173f6b3-d041-44cc-be93-23aea1f8e1d2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "                    precision  recall  f1-score  support\n",
              "Repaid                 0.8485  0.3146    0.4590  89.0000\n",
              "Early Delinquency      0.0000  0.0000    0.0000  21.0000\n",
              "Severe Delinquency     0.1444  0.8667    0.2476  15.0000\n",
              "accuracy               0.3280  0.3280    0.3280   0.3280\n",
              "macro avg              0.3310  0.3938    0.2355 125.0000\n",
              "weighted avg           0.6215  0.3280    0.3565 125.0000"
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              "      <th>Repaid</th>\n",
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            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "candidate_test_probability = (\n",
        "    model_evaluations[champion_name][\"probabilities\"]\n",
        ")\n",
        "\n",
        "threshold_predictions = (\n",
        "    candidate_test_probability\n",
        "    .idxmax(axis=1)\n",
        "    .astype(object)\n",
        ")\n",
        "\n",
        "threshold_predictions.loc[\n",
        "    candidate_test_probability[PRIMARY_FOCUS_CLASS]\n",
        "    >= selected_focus_threshold\n",
        "] = PRIMARY_FOCUS_CLASS\n",
        "\n",
        "threshold_metrics = evaluate_predictions(\n",
        "    f\"{champion_name} with F2 threshold\",\n",
        "    y_test,\n",
        "    threshold_predictions,\n",
        "    PRIMARY_CLASS_ORDER,\n",
        "    PRIMARY_FOCUS_CLASS,\n",
        ")\n",
        "\n",
        "display(\n",
        "    pd.DataFrame([threshold_metrics])\n",
        "    .set_index(\"model\")\n",
        ")\n",
        "\n",
        "display(\n",
        "    pd.DataFrame(\n",
        "        classification_report(\n",
        "            y_test,\n",
        "            threshold_predictions,\n",
        "            labels=PRIMARY_CLASS_ORDER,\n",
        "            output_dict=True,\n",
        "            zero_division=0,\n",
        "        )\n",
        "    ).T\n",
        ")\n",
        "\n",
        "plot_confusion_matrices(\n",
        "    y_test,\n",
        "    threshold_predictions,\n",
        "    PRIMARY_CLASS_ORDER,\n",
        "    f\"{champion_name} with training-derived threshold\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "2ed579f0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "2ed579f0",
        "outputId": "5ee76d96-886f-49bd-ac80-85fa7b1ae3ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Threshold interpretation\n\nThe training-derived threshold changed hold-out macro F1 by -0.196 and Severe Delinquency recall by +0.533. Severe-class precision after thresholding was 0.144. A recall increase accompanied by very low precision represents a larger human-review burden and more false alerts. The threshold is therefore a policy sensitivity analysis rather than an automatically superior production rule."
          },
          "metadata": {}
        }
      ],
      "source": [
        "default_metrics = test_summary.loc[champion_name]\n",
        "threshold_macro_gain = (\n",
        "    threshold_metrics[\"macro_f1\"]\n",
        "    - default_metrics[\"test_macro_f1\"]\n",
        ")\n",
        "threshold_recall_gain = (\n",
        "    threshold_metrics[\"focus_recall\"]\n",
        "    - default_metrics[\"focus_recall\"]\n",
        ")\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Threshold interpretation\\n\\n\"\n",
        "        f\"The training-derived threshold changed hold-out macro F1 by \"\n",
        "        f\"{threshold_macro_gain:+.3f} and {PRIMARY_FOCUS_CLASS} recall by \"\n",
        "        f\"{threshold_recall_gain:+.3f}. Severe-class precision after thresholding \"\n",
        "        f\"was {threshold_metrics['focus_precision']:.3f}. A recall increase accompanied \"\n",
        "        \"by very low precision represents a larger human-review burden and more false \"\n",
        "        \"alerts. The threshold is therefore a policy sensitivity analysis rather than \"\n",
        "        \"an automatically superior production rule.\"\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e008c22",
      "metadata": {
        "id": "8e008c22"
      },
      "source": [
        "## 16. Multinomial Logistic Regression interpretation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "2cbc1134",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2cbc1134",
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features associated with higher modelled Severe Delinquency log-odds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                            transformed_feature  coefficient\n",
              "73                        categorical__state_NY       0.3269\n",
              "41                     numeric__log_loan_amount       0.3036\n",
              "44         numeric__missingindicator_emp_length       0.2314\n",
              "12               numeric__total_credit_utilized       0.2264\n",
              "72                        categorical__state_NV       0.2146\n",
              "25                   numeric__total_debit_limit       0.2082\n",
              "85              categorical__homeownership_RENT       0.1808\n",
              "4                 numeric__debt_to_income_joint       0.1577\n",
              "98     categorical__loan_purpose_major_purchase       0.1558\n",
              "96   categorical__loan_purpose_home_improvement       0.1528\n",
              "66                        categorical__state_MA       0.1441\n",
              "30        numeric__account_never_delinq_percent       0.1401\n",
              "28             numeric__num_cc_carrying_balance       0.1276\n",
              "67                        categorical__state_MD       0.1231\n",
              "111       categorical__disbursement_method_Cash       0.1164"
            ],
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              "      <th>73</th>\n",
              "      <td>categorical__state_NY</td>\n",
              "      <td>0.3269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>numeric__log_loan_amount</td>\n",
              "      <td>0.3036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>numeric__missingindicator_emp_length</td>\n",
              "      <td>0.2314</td>\n",
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              "      <th>12</th>\n",
              "      <td>numeric__total_credit_utilized</td>\n",
              "      <td>0.2264</td>\n",
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              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>categorical__state_NV</td>\n",
              "      <td>0.2146</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>numeric__total_debit_limit</td>\n",
              "      <td>0.2082</td>\n",
              "    </tr>\n",
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              "      <th>85</th>\n",
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              "      <td>0.1808</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>numeric__debt_to_income_joint</td>\n",
              "      <td>0.1577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>categorical__loan_purpose_major_purchase</td>\n",
              "      <td>0.1558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>categorical__loan_purpose_home_improvement</td>\n",
              "      <td>0.1528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>categorical__state_MA</td>\n",
              "      <td>0.1441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>numeric__account_never_delinq_percent</td>\n",
              "      <td>0.1401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>numeric__num_cc_carrying_balance</td>\n",
              "      <td>0.1276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>categorical__state_MD</td>\n",
              "      <td>0.1231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>categorical__disbursement_method_Cash</td>\n",
              "      <td>0.1164</td>\n",
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          "text": [
            "Features associated with lower modelled Severe Delinquency log-odds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                              transformed_feature  coefficient\n",
              "76                          categorical__state_PA      -0.3018\n",
              "24             numeric__num_active_debit_accounts      -0.2890\n",
              "29                     numeric__num_mort_accounts      -0.2328\n",
              "5                              numeric__delinq_2y      -0.2318\n",
              "82          categorical__state_infrequent_sklearn      -0.2129\n",
              "26                 numeric__num_total_cc_accounts      -0.1986\n",
              "87   categorical__verified_income_Source Verified      -0.1879\n",
              "57                          categorical__state_AZ      -0.1854\n",
              "1                          numeric__annual_income      -0.1709\n",
              "20      numeric__months_since_last_credit_inquiry      -0.1673\n",
              "102      categorical__loan_purpose_small_business      -0.1428\n",
              "112    categorical__disbursement_method_DirectPay      -0.1374\n",
              "11                    numeric__total_credit_limit      -0.1370\n",
              "37                        numeric__loan_to_income      -0.1316\n",
              "38              numeric__credit_utilisation_ratio      -0.1280"
            ],
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              "  <tbody>\n",
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              "      <th>76</th>\n",
              "      <td>categorical__state_PA</td>\n",
              "      <td>-0.3018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>numeric__num_active_debit_accounts</td>\n",
              "      <td>-0.2890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>numeric__num_mort_accounts</td>\n",
              "      <td>-0.2328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>numeric__delinq_2y</td>\n",
              "      <td>-0.2318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>categorical__state_infrequent_sklearn</td>\n",
              "      <td>-0.2129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>numeric__num_total_cc_accounts</td>\n",
              "      <td>-0.1986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>categorical__verified_income_Source Verified</td>\n",
              "      <td>-0.1879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>categorical__state_AZ</td>\n",
              "      <td>-0.1854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>numeric__annual_income</td>\n",
              "      <td>-0.1709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>numeric__months_since_last_credit_inquiry</td>\n",
              "      <td>-0.1673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>categorical__loan_purpose_small_business</td>\n",
              "      <td>-0.1428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>categorical__disbursement_method_DirectPay</td>\n",
              "      <td>-0.1374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>numeric__total_credit_limit</td>\n",
              "      <td>-0.1370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>numeric__loan_to_income</td>\n",
              "      <td>-0.1316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>numeric__credit_utilisation_ratio</td>\n",
              "      <td>-0.1280</td>\n",
              "    </tr>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "summary": "{\n  \"name\": \"display(top_negative[[\\\"transformed_feature\\\", \\\"coefficient\\\"]])\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"transformed_feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"numeric__months_since_last_credit_inquiry\",\n          \"categorical__disbursement_method_DirectPay\",\n          \"categorical__state_PA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.055057429428066636,\n        \"min\": -0.30178218935510914,\n        \"max\": -0.12795793001153635,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          -0.16732927057296149,\n          -0.137395758228168,\n          -0.30178218935510914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {}
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      ],
      "source": [
        "logistic_feature_names = (\n",
        "    best_logistic.named_steps[\"preprocessor\"]\n",
        "    .get_feature_names_out()\n",
        ")\n",
        "logistic_classes = best_logistic.named_steps[\"model\"].classes_\n",
        "logistic_coefficients = best_logistic.named_steps[\"model\"].coef_\n",
        "\n",
        "focus_class_index = int(\n",
        "    np.where(logistic_classes == PRIMARY_FOCUS_CLASS)[0][0]\n",
        ")\n",
        "\n",
        "focus_coefficients = pd.DataFrame({\n",
        "    \"transformed_feature\": logistic_feature_names,\n",
        "    \"coefficient\": logistic_coefficients[focus_class_index],\n",
        "})\n",
        "focus_coefficients[\"absolute_coefficient\"] = (\n",
        "    focus_coefficients[\"coefficient\"].abs()\n",
        ")\n",
        "\n",
        "top_positive = (\n",
        "    focus_coefficients\n",
        "    .sort_values(\"coefficient\", ascending=False)\n",
        "    .head(15)\n",
        ")\n",
        "top_negative = (\n",
        "    focus_coefficients\n",
        "    .sort_values(\"coefficient\", ascending=True)\n",
        "    .head(15)\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Features associated with higher modelled \"\n",
        "    f\"{PRIMARY_FOCUS_CLASS} log-odds\"\n",
        ")\n",
        "display(top_positive[[\"transformed_feature\", \"coefficient\"]])\n",
        "\n",
        "print(\n",
        "    f\"Features associated with lower modelled \"\n",
        "    f\"{PRIMARY_FOCUS_CLASS} log-odds\"\n",
        ")\n",
        "display(top_negative[[\"transformed_feature\", \"coefficient\"]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "0d12faca",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        },
        "id": "0d12faca",
        "outputId": "ccde90af-1ba7-40d2-ed54-3c1beae0a269"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "coefficient_plot = pd.concat([\n",
        "    top_negative,\n",
        "    top_positive,\n",
        "]).sort_values(\"coefficient\")\n",
        "\n",
        "plt.figure(figsize=(10, 9))\n",
        "plt.barh(\n",
        "    coefficient_plot[\"transformed_feature\"],\n",
        "    coefficient_plot[\"coefficient\"],\n",
        ")\n",
        "plt.axvline(0, linewidth=1)\n",
        "plt.xlabel(\n",
        "    f\"Multinomial coefficient for {PRIMARY_FOCUS_CLASS}\"\n",
        ")\n",
        "plt.ylabel(\"Transformed feature\")\n",
        "plt.title(\n",
        "    \"Logistic Regression: strongest severe-class associations\"\n",
        ")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "003ba9ff",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "003ba9ff",
        "outputId": "f7bac70d-e9f0-46f7-d2cd-7a65355abd93"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Coefficient interpretation safeguard\n\nThe coefficients are interpretable model associations, subject to regularisation, collinearity and sampling uncertainty. Positive and negative coefficients describe conditional score associations; they do not establish causal effects on delinquency."
          },
          "metadata": {}
        }
      ],
      "source": [
        "logistic_validity_note = (\n",
        "    \"The coefficient evidence is treated as exploratory because the hold-out \"\n",
        "    \"macro F1 does not materially exceed the baseline.\"\n",
        "    if test_summary.loc[\"Logistic Regression\", \"test_macro_f1\"]\n",
        "    <= test_summary.loc[\"Majority baseline\", \"test_macro_f1\"] + 0.03\n",
        "    else\n",
        "    \"The coefficients are interpretable model associations, subject to \"\n",
        "    \"regularisation, collinearity and sampling uncertainty.\"\n",
        ")\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Coefficient interpretation safeguard\\n\\n\"\n",
        "        + logistic_validity_note\n",
        "        + \" Positive and negative coefficients describe conditional score \"\n",
        "          \"associations; they do not establish causal effects on delinquency.\"\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6a6aa58a",
      "metadata": {
        "id": "6a6aa58a"
      },
      "source": [
        "## 17. Random Forest interpretation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "f70c3da6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "f70c3da6",
        "outputId": "92789f2f-c711-4e89-c999-4dc622f0fc5f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                             feature  importance_mean  importance_std\n",
              "46                    loan_to_income           0.0583          0.0278\n",
              "30             num_total_cc_accounts           0.0438          0.0323\n",
              "2                      homeownership           0.0413          0.0144\n",
              "13                total_credit_lines           0.0411          0.0187\n",
              "48           income_per_open_account           0.0366          0.0254\n",
              "28         num_active_debit_accounts           0.0317          0.0149\n",
              "24  months_since_last_credit_inquiry           0.0300          0.0127\n",
              "32           num_cc_carrying_balance           0.0295          0.0256\n",
              "51            log_total_credit_limit           0.0247          0.0213\n",
              "33                 num_mort_accounts           0.0247          0.0123\n",
              "3                      annual_income           0.0203          0.0176\n",
              "0                         emp_length           0.0193          0.0021\n",
              "5                     debt_to_income           0.0182          0.0142\n",
              "15                total_credit_limit           0.0173          0.0163\n",
              "31              num_open_cc_accounts           0.0166          0.0035\n",
              "14                 open_credit_lines           0.0151          0.0133\n",
              "49                 log_annual_income           0.0150          0.0138\n",
              "45              credit_history_years           0.0144          0.0159\n",
              "52         log_total_credit_utilized           0.0132          0.0167\n",
              "29                 total_debit_limit           0.0124          0.0155"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>importance_mean</th>\n",
              "      <th>importance_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>loan_to_income</td>\n",
              "      <td>0.0583</td>\n",
              "      <td>0.0278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>num_total_cc_accounts</td>\n",
              "      <td>0.0438</td>\n",
              "      <td>0.0323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>homeownership</td>\n",
              "      <td>0.0413</td>\n",
              "      <td>0.0144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>total_credit_lines</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>0.0187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>income_per_open_account</td>\n",
              "      <td>0.0366</td>\n",
              "      <td>0.0254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>num_active_debit_accounts</td>\n",
              "      <td>0.0317</td>\n",
              "      <td>0.0149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>months_since_last_credit_inquiry</td>\n",
              "      <td>0.0300</td>\n",
              "      <td>0.0127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>num_cc_carrying_balance</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.0256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>log_total_credit_limit</td>\n",
              "      <td>0.0247</td>\n",
              "      <td>0.0213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>num_mort_accounts</td>\n",
              "      <td>0.0247</td>\n",
              "      <td>0.0123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>annual_income</td>\n",
              "      <td>0.0203</td>\n",
              "      <td>0.0176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>emp_length</td>\n",
              "      <td>0.0193</td>\n",
              "      <td>0.0021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>debt_to_income</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>0.0142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>total_credit_limit</td>\n",
              "      <td>0.0173</td>\n",
              "      <td>0.0163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>num_open_cc_accounts</td>\n",
              "      <td>0.0166</td>\n",
              "      <td>0.0035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>open_credit_lines</td>\n",
              "      <td>0.0151</td>\n",
              "      <td>0.0133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>log_annual_income</td>\n",
              "      <td>0.0150</td>\n",
              "      <td>0.0138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>credit_history_years</td>\n",
              "      <td>0.0144</td>\n",
              "      <td>0.0159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>log_total_credit_utilized</td>\n",
              "      <td>0.0132</td>\n",
              "      <td>0.0167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>total_debit_limit</td>\n",
              "      <td>0.0124</td>\n",
              "      <td>0.0155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                    [key], {});\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(forest_importance\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"loan_to_income\",\n          \"credit_history_years\",\n          \"open_credit_lines\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012622205490556239,\n        \"min\": 0.012412524907895489,\n        \"max\": 0.05834533053744181,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.05834533053744181,\n          0.014361924351068069,\n          0.015129807757205994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007283426638123492,\n        \"min\": 0.0020670960426866073,\n        \"max\": 0.03227720770719075,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.027804991948582894,\n          0.01592940120464354,\n          0.013310898587924462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "forest_permutation = permutation_importance(\n",
        "    best_forest,\n",
        "    X_test,\n",
        "    y_test,\n",
        "    scoring=\"f1_macro\",\n",
        "    n_repeats=PERMUTATION_REPEATS,\n",
        "    random_state=RANDOM_STATE,\n",
        "    n_jobs=N_JOBS,\n",
        ")\n",
        "\n",
        "forest_importance = pd.DataFrame({\n",
        "    \"feature\": X_test.columns,\n",
        "    \"importance_mean\": forest_permutation.importances_mean,\n",
        "    \"importance_std\": forest_permutation.importances_std,\n",
        "}).sort_values(\"importance_mean\", ascending=False)\n",
        "\n",
        "display(forest_importance.head(20))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "0416fb06",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "0416fb06",
        "outputId": "dc5e6dd2-35e3-47ee-d174-de8a19498476"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "top_forest_importance = (\n",
        "    forest_importance\n",
        "    .head(20)\n",
        "    .sort_values(\"importance_mean\")\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(9, 7))\n",
        "plt.barh(\n",
        "    top_forest_importance[\"feature\"],\n",
        "    top_forest_importance[\"importance_mean\"],\n",
        "    xerr=top_forest_importance[\"importance_std\"],\n",
        ")\n",
        "plt.axvline(0, linewidth=1)\n",
        "plt.xlabel(\"Decrease in hold-out macro F1 after permutation\")\n",
        "plt.ylabel(\"Original input feature\")\n",
        "plt.title(\"Random Forest: permutation importance\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "cf54756a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cf54756a",
        "outputId": "37a6aa40-c12f-4a83-8277-02b78f1c2858"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "candidate_pdp_features = [\n",
        "    feature\n",
        "    for feature in [\n",
        "        \"debt_to_income\",\n",
        "        \"loan_to_income\",\n",
        "        \"inquiries_last_12m\",\n",
        "    ]\n",
        "    if feature in X_test.columns\n",
        "]\n",
        "\n",
        "for feature in candidate_pdp_features:\n",
        "    PartialDependenceDisplay.from_estimator(\n",
        "        best_forest,\n",
        "        X_test,\n",
        "        features=[feature],\n",
        "        target=PRIMARY_FOCUS_CLASS,\n",
        "        kind=\"average\",\n",
        "    )\n",
        "    plt.suptitle(\n",
        "        f\"Random Forest partial dependence: {feature}\"\n",
        "    )\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a987ac0c",
      "metadata": {
        "id": "a987ac0c"
      },
      "source": [
        "### Optional SHAP analysis for the severe class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "a03bc278",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "a03bc278",
        "outputId": "774ab5bc-b8c3-407b-9cf9-a1baee30d02c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "try:\n",
        "    import shap\n",
        "\n",
        "    shap_sample = X_test.sample(\n",
        "        n=min(SHAP_SAMPLE_SIZE, len(X_test)),\n",
        "        random_state=RANDOM_STATE,\n",
        "    )\n",
        "\n",
        "    transformed_sample = (\n",
        "        best_forest.named_steps[\"preprocessor\"]\n",
        "        .transform(shap_sample)\n",
        "    )\n",
        "\n",
        "    if hasattr(transformed_sample, \"toarray\"):\n",
        "        transformed_sample = transformed_sample.toarray()\n",
        "\n",
        "    transformed_feature_names = (\n",
        "        best_forest.named_steps[\"preprocessor\"]\n",
        "        .get_feature_names_out()\n",
        "    )\n",
        "\n",
        "    forest_model = best_forest.named_steps[\"model\"]\n",
        "    explainer = shap.TreeExplainer(forest_model)\n",
        "    shap_values = explainer.shap_values(transformed_sample)\n",
        "\n",
        "    class_index = int(\n",
        "        np.where(\n",
        "            forest_model.classes_ == PRIMARY_FOCUS_CLASS\n",
        "        )[0][0]\n",
        "    )\n",
        "\n",
        "    if isinstance(shap_values, list):\n",
        "        focus_shap_values = shap_values[class_index]\n",
        "    else:\n",
        "        shap_array = np.asarray(shap_values)\n",
        "\n",
        "        if (\n",
        "            shap_array.ndim == 3\n",
        "            and shap_array.shape[-1]\n",
        "            == len(forest_model.classes_)\n",
        "        ):\n",
        "            focus_shap_values = shap_array[:, :, class_index]\n",
        "        elif (\n",
        "            shap_array.ndim == 3\n",
        "            and shap_array.shape[0]\n",
        "            == len(forest_model.classes_)\n",
        "        ):\n",
        "            focus_shap_values = shap_array[class_index]\n",
        "        elif shap_array.ndim == 2:\n",
        "            focus_shap_values = shap_array\n",
        "        else:\n",
        "            raise ValueError(\n",
        "                f\"Unexpected SHAP output shape: {shap_array.shape}\"\n",
        "            )\n",
        "\n",
        "    shap.summary_plot(\n",
        "        focus_shap_values,\n",
        "        transformed_sample,\n",
        "        feature_names=transformed_feature_names,\n",
        "        plot_type=\"bar\",\n",
        "        show=False,\n",
        "        max_display=20,\n",
        "    )\n",
        "    plt.title(\n",
        "        f\"Random Forest SHAP importance: {PRIMARY_FOCUS_CLASS}\"\n",
        "    )\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "except Exception as shap_error:\n",
        "    print(\n",
        "        \"SHAP could not be completed in this environment. \"\n",
        "        \"Permutation importance and partial dependence remain available.\"\n",
        "    )\n",
        "    print(\"Technical detail:\", shap_error)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "fc891a2b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "fc891a2b",
        "outputId": "cb1e69ec-9a63-44fe-f753-27428e1a3564"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Explainability validity safeguard\n\nThe Random Forest attribution results identify predictive associations within the fitted model, but correlated variables can share importance. SHAP, permutation importance and partial dependence do not prove causality, fairness or model adequacy."
          },
          "metadata": {}
        }
      ],
      "source": [
        "forest_validity_note = (\n",
        "    \"Because the Random Forest did not materially exceed the baseline, \"\n",
        "    \"its feature-attribution results explain an unstable fitted model and \"\n",
        "    \"must not be presented as validated risk drivers.\"\n",
        "    if test_summary.loc[\"Random Forest\", \"test_macro_f1\"]\n",
        "    <= test_summary.loc[\"Majority baseline\", \"test_macro_f1\"] + 0.03\n",
        "    else\n",
        "    \"The Random Forest attribution results identify predictive associations \"\n",
        "    \"within the fitted model, but correlated variables can share importance.\"\n",
        ")\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Explainability validity safeguard\\n\\n\"\n",
        "        + forest_validity_note\n",
        "        + \" SHAP, permutation importance and partial dependence do not prove \"\n",
        "          \"causality, fairness or model adequacy.\"\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "09fb2587",
      "metadata": {
        "id": "09fb2587"
      },
      "source": [
        "## 18. Fairness feasibility and subgroup error audit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "e466ede2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e466ede2",
        "outputId": "25645346-030d-4534-fe9a-6dbcab295543"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Directly available protected attributes: []\n",
            "No direct age, gender, race, ethnicity or disability variables are present. Formal demographic fairness cannot be established from this dataset.\n"
          ]
        }
      ],
      "source": [
        "expected_protected_attributes = [\n",
        "    \"age\",\n",
        "    \"gender\",\n",
        "    \"sex\",\n",
        "    \"race\",\n",
        "    \"ethnicity\",\n",
        "    \"disability\",\n",
        "]\n",
        "\n",
        "present_protected_attributes = [\n",
        "    column\n",
        "    for column in expected_protected_attributes\n",
        "    if column in data.columns\n",
        "]\n",
        "\n",
        "print(\n",
        "    \"Directly available protected attributes:\",\n",
        "    present_protected_attributes,\n",
        ")\n",
        "\n",
        "if not present_protected_attributes:\n",
        "    print(\n",
        "        \"No direct age, gender, race, ethnicity or disability \"\n",
        "        \"variables are present. Formal demographic fairness \"\n",
        "        \"cannot be established from this dataset.\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa92247b",
      "metadata": {
        "id": "fa92247b"
      },
      "source": [
        "The following results are a **subgroup error audit**, not a legal fairness determination. `homeownership`, `application_type` and `state` may capture socioeconomic or geographic structure, but they are not substitutes for protected-attribute data. Every metric is accompanied by group support because disparities based on only a few severe cases are statistically unstable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "37eef8f0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "37eef8f0",
        "outputId": "c94cb79e-2dba-4ad7-9046-b05b7f4515c0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       group_column       group    n  actual_focus_n  predicted_focus_n  \\\n",
              "0     homeownership    MORTGAGE   65               5                  0   \n",
              "1     homeownership        RENT   39               4                 12   \n",
              "2     homeownership         OWN   21               6                  5   \n",
              "3  application_type  individual  108              13                 13   \n",
              "4  application_type       joint   17               2                  4   \n",
              "5             state          CA   21               1                  3   \n",
              "\n",
              "   focus_precision  focus_recall  focus_false_positive_rate  group_macro_f1  \n",
              "0              NaN        0.0000                     0.0000          0.2798  \n",
              "1           0.1667        0.5000                     0.2857          0.4402  \n",
              "2           0.6000        0.5000                     0.1333          0.4437  \n",
              "3           0.3077        0.3077                     0.0947          0.4478  \n",
              "4           0.2500        0.5000                     0.2000          0.3535  \n",
              "5           0.0000        0.0000                     0.1500          0.3499  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-afaa2450-edb2-4c0e-a360-00d05fb24d77\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>group_column</th>\n",
              "      <th>group</th>\n",
              "      <th>n</th>\n",
              "      <th>actual_focus_n</th>\n",
              "      <th>predicted_focus_n</th>\n",
              "      <th>focus_precision</th>\n",
              "      <th>focus_recall</th>\n",
              "      <th>focus_false_positive_rate</th>\n",
              "      <th>group_macro_f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>homeownership</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>homeownership</td>\n",
              "      <td>RENT</td>\n",
              "      <td>39</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>0.1667</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.2857</td>\n",
              "      <td>0.4402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>homeownership</td>\n",
              "      <td>OWN</td>\n",
              "      <td>21</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.1333</td>\n",
              "      <td>0.4437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>application_type</td>\n",
              "      <td>individual</td>\n",
              "      <td>108</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>0.3077</td>\n",
              "      <td>0.3077</td>\n",
              "      <td>0.0947</td>\n",
              "      <td>0.4478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>application_type</td>\n",
              "      <td>joint</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>0.3535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>state</td>\n",
              "      <td>CA</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.3499</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-afaa2450-edb2-4c0e-a360-00d05fb24d77 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-afaa2450-edb2-4c0e-a360-00d05fb24d77');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "  <div id=\"id_92fb0dc6-cabc-483c-8f82-53e84177a8a9\">\n",
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              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('subgroup_audit')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('subgroup_audit');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "subgroup_audit",
              "summary": "{\n  \"name\": \"subgroup_audit\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"group_column\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"homeownership\",\n          \"application_type\",\n          \"state\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"MORTGAGE\",\n          \"RENT\",\n          \"CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35,\n        \"min\": 17,\n        \"max\": 108,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          39,\n          17,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actual_focus_n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 13,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_focus_n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 13,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          12,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"focus_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22032192094623984,\n        \"min\": 0.0,\n        \"max\": 0.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6,\n          0.0,\n          0.3076923076923077\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"focus_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24496910403750682,\n        \"min\": 0.0,\n        \"max\": 0.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.5,\n          0.3076923076923077\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"focus_false_positive_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09646923606644485,\n        \"min\": 0.0,\n        \"max\": 0.2857142857142857,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0,\n          0.2857142857142857,\n          0.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06888994206406689,\n        \"min\": 0.27976190476190477,\n        \"max\": 0.4477798624140088,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.27976190476190477,\n          0.44021739130434784,\n          0.3499361430395913\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def subgroup_error_metrics(\n",
        "    X_slice,\n",
        "    y_true,\n",
        "    y_pred,\n",
        "    group_column,\n",
        "    focus_class,\n",
        "    class_order,\n",
        "    minimum_group_size=15,\n",
        "):\n",
        "    frame = pd.DataFrame({\n",
        "        \"group\": X_slice[group_column],\n",
        "        \"y_true\": y_true,\n",
        "        \"y_pred\": pd.Series(\n",
        "            y_pred,\n",
        "            index=y_true.index,\n",
        "        ),\n",
        "    }).dropna(subset=[\"group\"])\n",
        "\n",
        "    rows = []\n",
        "\n",
        "    for group_name, group_frame in frame.groupby(\n",
        "        \"group\",\n",
        "        dropna=False,\n",
        "    ):\n",
        "        if len(group_frame) < minimum_group_size:\n",
        "            continue\n",
        "\n",
        "        true_binary = (\n",
        "            group_frame[\"y_true\"]\n",
        "            .eq(focus_class)\n",
        "            .astype(int)\n",
        "        )\n",
        "        pred_binary = (\n",
        "            group_frame[\"y_pred\"]\n",
        "            .eq(focus_class)\n",
        "            .astype(int)\n",
        "        )\n",
        "\n",
        "        tn, fp, fn, tp = confusion_matrix(\n",
        "            true_binary,\n",
        "            pred_binary,\n",
        "            labels=[0, 1],\n",
        "        ).ravel()\n",
        "\n",
        "        rows.append({\n",
        "            \"group_column\": group_column,\n",
        "            \"group\": group_name,\n",
        "            \"n\": len(group_frame),\n",
        "            \"actual_focus_n\": int(true_binary.sum()),\n",
        "            \"predicted_focus_n\": int(pred_binary.sum()),\n",
        "            \"focus_precision\": safe_divide(tp, tp + fp),\n",
        "            \"focus_recall\": safe_divide(tp, tp + fn),\n",
        "            \"focus_false_positive_rate\": safe_divide(\n",
        "                fp,\n",
        "                fp + tn,\n",
        "            ),\n",
        "            \"group_macro_f1\": f1_score(\n",
        "                group_frame[\"y_true\"],\n",
        "                group_frame[\"y_pred\"],\n",
        "                labels=class_order,\n",
        "                average=\"macro\",\n",
        "                zero_division=0,\n",
        "            ),\n",
        "        })\n",
        "\n",
        "    return (\n",
        "        pd.DataFrame(rows)\n",
        "        .sort_values(\n",
        "            [\"group_column\", \"n\"],\n",
        "            ascending=[True, False],\n",
        "        )\n",
        "    )\n",
        "\n",
        "\n",
        "candidate_predictions = (\n",
        "    model_evaluations[champion_name][\"predictions\"]\n",
        ")\n",
        "\n",
        "subgroup_tables = []\n",
        "\n",
        "for group_column, minimum_n in [\n",
        "    (\"homeownership\", 15),\n",
        "    (\"application_type\", 15),\n",
        "    (\"state\", 20),\n",
        "]:\n",
        "    if group_column in X_test.columns:\n",
        "        subgroup_tables.append(\n",
        "            subgroup_error_metrics(\n",
        "                X_test,\n",
        "                y_test,\n",
        "                candidate_predictions,\n",
        "                group_column,\n",
        "                PRIMARY_FOCUS_CLASS,\n",
        "                PRIMARY_CLASS_ORDER,\n",
        "                minimum_group_size=minimum_n,\n",
        "            )\n",
        "        )\n",
        "\n",
        "subgroup_audit = pd.concat(\n",
        "    subgroup_tables,\n",
        "    ignore_index=True,\n",
        ")\n",
        "\n",
        "display(subgroup_audit)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "b7469c79",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 997
        },
        "id": "b7469c79",
        "outputId": "b429622c-193f-4f48-8883-4f192be88e3d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for group_column in [\n",
        "    \"homeownership\",\n",
        "    \"application_type\",\n",
        "]:\n",
        "    plot_data = (\n",
        "        subgroup_audit\n",
        "        .query(\"group_column == @group_column\")\n",
        "        .dropna(subset=[\"focus_recall\"])\n",
        "        .sort_values(\"focus_recall\")\n",
        "    )\n",
        "\n",
        "    if not plot_data.empty:\n",
        "        plt.figure(figsize=(8, 5))\n",
        "        plt.barh(\n",
        "            plot_data[\"group\"].astype(str),\n",
        "            plot_data[\"focus_recall\"],\n",
        "        )\n",
        "        plt.xlabel(f\"{PRIMARY_FOCUS_CLASS} recall\")\n",
        "        plt.ylabel(\n",
        "            group_column.replace(\"_\", \" \").title()\n",
        "        )\n",
        "        plt.title(\n",
        "            f\"{champion_name}: severe-class recall by \"\n",
        "            f\"{group_column}\"\n",
        "        )\n",
        "        plt.xlim(0, 1)\n",
        "        plt.tight_layout()\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "d49e3c89",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "d49e3c89",
        "outputId": "4f0a7b83-1386-4ec7-a3ff-0b54eb555eb2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Fairness conclusion\n\n1 subgroup rows contain at least ten severe cases, but direct protected characteristics are still unavailable. The correct conclusion is that demographic fairness remains unidentified, not that the absence of a detected disparity proves fairness."
          },
          "metadata": {}
        }
      ],
      "source": [
        "groups_with_adequate_focus_support = (\n",
        "    subgroup_audit[\"actual_focus_n\"].ge(10).sum()\n",
        "    if not subgroup_audit.empty\n",
        "    else 0\n",
        ")\n",
        "\n",
        "if groups_with_adequate_focus_support == 0:\n",
        "    fairness_conclusion = (\n",
        "        \"No reported subgroup contains at least ten severe cases in the \"\n",
        "        \"hold-out set. Comparative recall estimates are therefore too \"\n",
        "        \"unstable for a defensible fairness conclusion.\"\n",
        "    )\n",
        "else:\n",
        "    fairness_conclusion = (\n",
        "        f\"{groups_with_adequate_focus_support} subgroup rows contain at \"\n",
        "        \"least ten severe cases, but direct protected characteristics are \"\n",
        "        \"still unavailable.\"\n",
        "    )\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Fairness conclusion\\n\\n\"\n",
        "        + fairness_conclusion\n",
        "        + \" The correct conclusion is that demographic fairness remains \"\n",
        "          \"unidentified, not that the absence of a detected disparity proves fairness.\"\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a596f50",
      "metadata": {
        "id": "3a596f50"
      },
      "source": [
        "## 19. Class-imbalance sensitivity: weighting versus random oversampling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "a99ebf3a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "a99ebf3a",
        "outputId": "68bc3b35-e404-4d07-d7d2-1c720f118404"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                cv_macro_f1_mean  cv_macro_f1_std  \\\n",
              "strategy                                                            \n",
              "Best tuned Logistic Regression            0.3575           0.0468   \n",
              "Random oversampling inside CV             0.3451           0.0460   \n",
              "\n",
              "                                cv_balanced_accuracy_mean  \n",
              "strategy                                                   \n",
              "Best tuned Logistic Regression                     0.3591  \n",
              "Random oversampling inside CV                      0.3631  "
            ],
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              "      <th></th>\n",
              "      <th>cv_macro_f1_mean</th>\n",
              "      <th>cv_macro_f1_std</th>\n",
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              "      <th>Best tuned Logistic Regression</th>\n",
              "      <td>0.3575</td>\n",
              "      <td>0.0468</td>\n",
              "      <td>0.3591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random oversampling inside CV</th>\n",
              "      <td>0.3451</td>\n",
              "      <td>0.0460</td>\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e5c9545f-1486-45e3-bb78-e77e1ff8c7b8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "imbalance_sensitivity",
              "summary": "{\n  \"name\": \"imbalance_sensitivity\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"strategy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Random oversampling inside CV\",\n          \"Best tuned Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_macro_f1_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00879006569507373,\n        \"min\": 0.34506341121264356,\n        \"max\": 0.3574944413327673,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.34506341121264356,\n          0.3574944413327673\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_macro_f1_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005836236952778696,\n        \"min\": 0.04601962959908732,\n        \"max\": 0.04684499814427159,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.04601962959908732,\n          0.04684499814427159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cv_balanced_accuracy_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002796613345639515,\n        \"min\": 0.3591223614214501,\n        \"max\": 0.3630773699435671,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.3630773699435671,\n          0.3591223614214501\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Compare the best class-weighted Logistic Regression with training-fold\n",
        "# random oversampling. Oversampling is performed inside every CV fold.\n",
        "\n",
        "oversampled_logistic = ImbalancedPipeline([\n",
        "    (\"preprocessor\", clone(logistic_preprocessor)),\n",
        "    (\n",
        "        \"sampler\",\n",
        "        RandomOverSampler(\n",
        "            sampling_strategy=\"not majority\",\n",
        "            random_state=RANDOM_STATE,\n",
        "        ),\n",
        "    ),\n",
        "    (\n",
        "        \"model\",\n",
        "        LogisticRegression(\n",
        "            C=logistic_search.best_params_[\"model__C\"],\n",
        "            class_weight=None,\n",
        "            solver=\"lbfgs\",\n",
        "            penalty=\"l2\",\n",
        "            tol=1e-3,\n",
        "            max_iter=2000,\n",
        "            random_state=RANDOM_STATE,\n",
        "        ),\n",
        "    ),\n",
        "])\n",
        "\n",
        "oversampling_cv = cross_validate(\n",
        "    oversampled_logistic,\n",
        "    X_train,\n",
        "    y_train,\n",
        "    cv=repeated_cv,\n",
        "    scoring=MULTI_SCORING,\n",
        "    n_jobs=N_JOBS,\n",
        "    return_train_score=True,\n",
        ")\n",
        "\n",
        "imbalance_sensitivity = pd.DataFrame([\n",
        "    {\n",
        "        \"strategy\": \"Best tuned Logistic Regression\",\n",
        "        \"cv_macro_f1_mean\": validation_summary.loc[\n",
        "            \"Logistic Regression\",\n",
        "            \"cv_macro_f1_mean\",\n",
        "        ],\n",
        "        \"cv_macro_f1_std\": validation_summary.loc[\n",
        "            \"Logistic Regression\",\n",
        "            \"cv_macro_f1_std\",\n",
        "        ],\n",
        "        \"cv_balanced_accuracy_mean\": validation_summary.loc[\n",
        "            \"Logistic Regression\",\n",
        "            \"cv_balanced_accuracy\",\n",
        "        ],\n",
        "    },\n",
        "    {\n",
        "        \"strategy\": \"Random oversampling inside CV\",\n",
        "        \"cv_macro_f1_mean\": oversampling_cv[\n",
        "            \"test_macro_f1\"\n",
        "        ].mean(),\n",
        "        \"cv_macro_f1_std\": oversampling_cv[\n",
        "            \"test_macro_f1\"\n",
        "        ].std(),\n",
        "        \"cv_balanced_accuracy_mean\": oversampling_cv[\n",
        "            \"test_balanced_accuracy\"\n",
        "        ].mean(),\n",
        "    },\n",
        "]).set_index(\"strategy\")\n",
        "\n",
        "display(imbalance_sensitivity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "e88de18e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "e88de18e",
        "outputId": "f651cc8f-af5f-43e9-ee41-14bcb391000c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Imbalance-treatment interpretation\n\nRandom oversampling changed mean repeated-CV macro F1 by -0.012. Oversampling duplicates minority observations and may increase overfitting, particularly in a small cohort. It is retained only when the validation gain is material and stable across folds; otherwise, class weighting is the more parsimonious treatment."
          },
          "metadata": {}
        }
      ],
      "source": [
        "oversampling_gain = (\n",
        "    imbalance_sensitivity.loc[\n",
        "        \"Random oversampling inside CV\",\n",
        "        \"cv_macro_f1_mean\",\n",
        "    ]\n",
        "    - imbalance_sensitivity.loc[\n",
        "        \"Best tuned Logistic Regression\",\n",
        "        \"cv_macro_f1_mean\",\n",
        "    ]\n",
        ")\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Imbalance-treatment interpretation\\n\\n\"\n",
        "        f\"Random oversampling changed mean repeated-CV macro F1 by \"\n",
        "        f\"{oversampling_gain:+.3f}. Oversampling duplicates minority observations \"\n",
        "        \"and may increase overfitting, particularly in a small cohort. It is retained \"\n",
        "        \"only when the validation gain is material and stable across folds; otherwise, \"\n",
        "        \"class weighting is the more parsimonious treatment.\"\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "62e17c21",
      "metadata": {
        "id": "62e17c21"
      },
      "source": [
        "## 20. Sensitivity analysis: lender-derived grade and pricing variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "e4c25d35",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "e4c25d35",
        "outputId": "73eccbee-9064-48d5-88fb-fb9dede4b72b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                     cv_macro_f1_mean  cv_macro_f1_std  \\\n",
              "information_set                                                          \n",
              "Independent primary feature set                0.3830           0.0365   \n",
              "Expanded set with grade and pricing            0.3924           0.0450   \n",
              "\n",
              "                                     cv_balanced_accuracy_mean  \n",
              "information_set                                                 \n",
              "Independent primary feature set                         0.3897  \n",
              "Expanded set with grade and pricing                     0.3981  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cv_macro_f1_mean</th>\n",
              "      <th>cv_macro_f1_std</th>\n",
              "      <th>cv_balanced_accuracy_mean</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Independent primary feature set</th>\n",
              "      <td>0.3830</td>\n",
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              "      <td>0.3897</td>\n",
              "    </tr>\n",
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              "      <th>Expanded set with grade and pricing</th>\n",
              "      <td>0.3924</td>\n",
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        "EXPANDED_EXCLUSIONS = (\n",
        "    ID_COLUMNS\n",
        "    + POST_OUTCOME_COLUMNS\n",
        "    + HIGH_CARDINALITY_COLUMNS\n",
        "    + TARGET_COLUMNS\n",
        ")\n",
        "\n",
        "X_expanded = primary_cohort.drop(\n",
        "    columns=EXPANDED_EXCLUSIONS,\n",
        "    errors=\"ignore\",\n",
        ")\n",
        "X_expanded_train = X_expanded.loc[X_train.index]\n",
        "\n",
        "expanded_scale_numeric = (\n",
        "    champion_name == \"Logistic Regression\"\n",
        ")\n",
        "\n",
        "expanded_preprocessor, _, _ = make_preprocessor(\n",
        "    X_expanded_train,\n",
        "    scale_numeric=expanded_scale_numeric,\n",
        ")\n",
        "\n",
        "expanded_model = clone(\n",
        "    champion.named_steps[\"model\"]\n",
        ")\n",
        "\n",
        "expanded_pipeline = Pipeline([\n",
        "    (\"preprocessor\", expanded_preprocessor),\n",
        "    (\"model\", expanded_model),\n",
        "])\n",
        "\n",
        "expanded_validation = cross_validate(\n",
        "    expanded_pipeline,\n",
        "    X_expanded_train,\n",
        "    y_train,\n",
        "    cv=repeated_cv,\n",
        "    scoring=MULTI_SCORING,\n",
        "    n_jobs=N_JOBS,\n",
        "    return_train_score=True,\n",
        ")\n",
        "\n",
        "lender_sensitivity = pd.DataFrame([\n",
        "    {\n",
        "        \"information_set\": \"Independent primary feature set\",\n",
        "        \"cv_macro_f1_mean\": validation_summary.loc[\n",
        "            champion_name,\n",
        "            \"cv_macro_f1_mean\",\n",
        "        ],\n",
        "        \"cv_macro_f1_std\": validation_summary.loc[\n",
        "            champion_name,\n",
        "            \"cv_macro_f1_std\",\n",
        "        ],\n",
        "        \"cv_balanced_accuracy_mean\": validation_summary.loc[\n",
        "            champion_name,\n",
        "            \"cv_balanced_accuracy\",\n",
        "        ],\n",
        "    },\n",
        "    {\n",
        "        \"information_set\": \"Expanded set with grade and pricing\",\n",
        "        \"cv_macro_f1_mean\": expanded_validation[\n",
        "            \"test_macro_f1\"\n",
        "        ].mean(),\n",
        "        \"cv_macro_f1_std\": expanded_validation[\n",
        "            \"test_macro_f1\"\n",
        "        ].std(),\n",
        "        \"cv_balanced_accuracy_mean\": expanded_validation[\n",
        "            \"test_balanced_accuracy\"\n",
        "        ].mean(),\n",
        "    },\n",
        "]).set_index(\"information_set\")\n",
        "\n",
        "display(lender_sensitivity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "30209da1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "30209da1",
        "outputId": "daf1742c-f0b3-45c8-9aaa-1f3a6547ada3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Lender-derived feature interpretation\n\nAdding grade and pricing changed mean repeated-CV macro F1 by +0.009. The change is smaller than the fold-to-fold standard deviation, so it is not compelling evidence of a stable improvement. Even where performance improves, these variables partly encode Lending Club's earlier underwriting decision, creating circularity and possible inheritance of historical policy bias."
          },
          "metadata": {}
        }
      ],
      "source": [
        "lender_gain = (\n",
        "    lender_sensitivity.loc[\n",
        "        \"Expanded set with grade and pricing\",\n",
        "        \"cv_macro_f1_mean\",\n",
        "    ]\n",
        "    - lender_sensitivity.loc[\n",
        "        \"Independent primary feature set\",\n",
        "        \"cv_macro_f1_mean\",\n",
        "    ]\n",
        ")\n",
        "\n",
        "expanded_std = lender_sensitivity.loc[\n",
        "    \"Expanded set with grade and pricing\",\n",
        "    \"cv_macro_f1_std\",\n",
        "]\n",
        "\n",
        "if abs(lender_gain) < expanded_std:\n",
        "    materiality_text = (\n",
        "        \"The change is smaller than the fold-to-fold standard deviation, \"\n",
        "        \"so it is not compelling evidence of a stable improvement.\"\n",
        "    )\n",
        "else:\n",
        "    materiality_text = (\n",
        "        \"The change exceeds the expanded model's fold-to-fold standard \"\n",
        "        \"deviation and may represent a material signal.\"\n",
        "    )\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Lender-derived feature interpretation\\n\\n\"\n",
        "        f\"Adding grade and pricing changed mean repeated-CV macro F1 by \"\n",
        "        f\"{lender_gain:+.3f}. {materiality_text} Even where performance improves, \"\n",
        "        \"these variables partly encode Lending Club's earlier underwriting decision, \"\n",
        "        \"creating circularity and possible inheritance of historical policy bias.\"\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42bbed26",
      "metadata": {
        "id": "42bbed26"
      },
      "source": [
        "## 21. Full operational snapshot sensitivity experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "4f63f5ca",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "4f63f5ca",
        "outputId": "fa0b9996-61e2-42ea-eb49-f347cea515b3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     train_count  test_count\n",
              "snapshot_risk_class                         \n",
              "Performing                  7858        1964\n",
              "Early Warning                 84          21\n",
              "High Risk                     58          15"
            ],
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>train_count</th>\n",
              "      <th>test_count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>snapshot_risk_class</th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Performing</th>\n",
              "      <td>7858</td>\n",
              "      <td>1964</td>\n",
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              "    <tr>\n",
              "      <th>Early Warning</th>\n",
              "      <td>84</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High Risk</th>\n",
              "      <td>58</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "      .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('snapshot_split')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('snapshot_split');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "snapshot_split",
              "summary": "{\n  \"name\": \"snapshot_split\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"snapshot_risk_class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Performing\",\n          \"Early Warning\",\n          \"High Risk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4495,\n        \"min\": 58,\n        \"max\": 7858,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7858,\n          84,\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1123,\n        \"min\": 15,\n        \"max\": 1964,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1964,\n          21,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Rebuild the predictor matrix for all loans, including Current accounts.\n",
        "X_snapshot = data.drop(\n",
        "    columns=PRIMARY_EXCLUSIONS,\n",
        "    errors=\"ignore\",\n",
        ")\n",
        "y_snapshot = data[\"snapshot_risk_class\"].copy()\n",
        "\n",
        "X_snapshot_train, X_snapshot_test, y_snapshot_train, y_snapshot_test = (\n",
        "    train_test_split(\n",
        "        X_snapshot,\n",
        "        y_snapshot,\n",
        "        test_size=TEST_SIZE,\n",
        "        stratify=y_snapshot,\n",
        "        random_state=RANDOM_STATE,\n",
        "    )\n",
        ")\n",
        "\n",
        "snapshot_split = pd.concat(\n",
        "    [\n",
        "        y_snapshot_train.value_counts()\n",
        "        .reindex(SNAPSHOT_CLASS_ORDER)\n",
        "        .rename(\"train_count\"),\n",
        "        y_snapshot_test.value_counts()\n",
        "        .reindex(SNAPSHOT_CLASS_ORDER)\n",
        "        .rename(\"test_count\"),\n",
        "    ],\n",
        "    axis=1,\n",
        ")\n",
        "\n",
        "display(snapshot_split)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "7fe4e9ce",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "7fe4e9ce",
        "outputId": "ac22cb20-217a-4517-f467-22b11340a12c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                              train_macro_f1  test_accuracy  \\\n",
              "model                                                         \n",
              "Snapshot majority baseline            0.3303         0.9820   \n",
              "Snapshot Logistic Regression          0.2893         0.6015   \n",
              "Snapshot Random Forest                0.9774         0.9815   \n",
              "\n",
              "                              test_balanced_accuracy  test_macro_precision  \\\n",
              "model                                                                        \n",
              "Snapshot majority baseline                    0.3333                0.3273   \n",
              "Snapshot Logistic Regression                  0.3268                0.3357   \n",
              "Snapshot Random Forest                        0.3332                0.3273   \n",
              "\n",
              "                              test_macro_recall  test_macro_f1  \\\n",
              "model                                                            \n",
              "Snapshot majority baseline               0.3333         0.3303   \n",
              "Snapshot Logistic Regression             0.3268         0.2623   \n",
              "Snapshot Random Forest                   0.3332         0.3302   \n",
              "\n",
              "                              test_weighted_f1  test_macro_roc_auc_ovr  \\\n",
              "model                                                                    \n",
              "Snapshot majority baseline              0.9731                  0.5000   \n",
              "Snapshot Logistic Regression            0.7408                  0.5864   \n",
              "Snapshot Random Forest                  0.9728                  0.5940   \n",
              "\n",
              "                              focus_precision  focus_recall  focus_f1  \\\n",
              "model                                                                   \n",
              "Snapshot majority baseline             0.0000        0.0000    0.0000   \n",
              "Snapshot Logistic Regression           0.0055        0.1333    0.0106   \n",
              "Snapshot Random Forest                 0.0000        0.0000    0.0000   \n",
              "\n",
              "                              focus_average_precision  focus_brier_score  \n",
              "model                                                                     \n",
              "Snapshot majority baseline                     0.0075             0.0075  \n",
              "Snapshot Logistic Regression                   0.0262             0.0999  \n",
              "Snapshot Random Forest                         0.0169             0.0328  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f29e3e0-ac89-46ec-8c4f-ac08ab076242\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        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>train_macro_f1</th>\n",
              "      <th>test_accuracy</th>\n",
              "      <th>test_balanced_accuracy</th>\n",
              "      <th>test_macro_precision</th>\n",
              "      <th>test_macro_recall</th>\n",
              "      <th>test_macro_f1</th>\n",
              "      <th>test_weighted_f1</th>\n",
              "      <th>test_macro_roc_auc_ovr</th>\n",
              "      <th>focus_precision</th>\n",
              "      <th>focus_recall</th>\n",
              "      <th>focus_f1</th>\n",
              "      <th>focus_average_precision</th>\n",
              "      <th>focus_brier_score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</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>Snapshot majority baseline</th>\n",
              "      <td>0.3303</td>\n",
              "      <td>0.9820</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.3273</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.3303</td>\n",
              "      <td>0.9731</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0075</td>\n",
              "      <td>0.0075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Snapshot Logistic Regression</th>\n",
              "      <td>0.2893</td>\n",
              "      <td>0.6015</td>\n",
              "      <td>0.3268</td>\n",
              "      <td>0.3357</td>\n",
              "      <td>0.3268</td>\n",
              "      <td>0.2623</td>\n",
              "      <td>0.7408</td>\n",
              "      <td>0.5864</td>\n",
              "      <td>0.0055</td>\n",
              "      <td>0.1333</td>\n",
              "      <td>0.0106</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>0.0999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Snapshot Random Forest</th>\n",
              "      <td>0.9774</td>\n",
              "      <td>0.9815</td>\n",
              "      <td>0.3332</td>\n",
              "      <td>0.3273</td>\n",
              "      <td>0.3332</td>\n",
              "      <td>0.3302</td>\n",
              "      <td>0.9728</td>\n",
              "      <td>0.5940</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0169</td>\n",
              "      <td>0.0328</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "snapshot_test_summary",
              "summary": "{\n  \"name\": \"snapshot_test_summary\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Snapshot majority baseline\",\n          \"Snapshot Logistic Regression\",\n          \"Snapshot Random Forest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38594533414316395,\n        \"min\": 0.2892995384963164,\n        \"max\": 0.9773550266044211,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3303485096901669,\n          0.2892995384963164,\n          0.9773550266044211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21953758220404998,\n        \"min\": 0.6015,\n        \"max\": 0.982,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.982,\n          0.6015,\n          0.9815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_balanced_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0037259424212208905,\n        \"min\": 0.3267966249636311,\n        \"max\": 0.3333333333333333,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3333333333333333,\n          0.3267966249636311,\n          0.33316361167685\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_macro_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004854264352812503,\n        \"min\": 0.3273303318325829,\n        \"max\": 0.3357396646735867,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3273333333333333,\n          0.3357396646735867,\n          0.3273303318325829\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_macro_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0037259424212208905,\n        \"min\": 0.3267966249636311,\n        \"max\": 0.3333333333333333,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3333333333333333,\n          0.3267966249636311,\n          0.33316361167685\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03924243650500494,\n        \"min\": 0.26229379641118394,\n        \"max\": 0.3303060881264716,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3303060881264716,\n          0.26229379641118394,\n          0.33022121288586087\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_weighted_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13401444845464508,\n        \"min\": 0.7408369817252747,\n        \"max\": 0.9730817356205853,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9730817356205853,\n          0.7408369817252747,\n          0.9728316931617462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_macro_roc_auc_ovr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0522074677194871,\n        \"min\": 0.5,\n        \"max\": 0.5939702499025347,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.5864066058593363,\n          0.5939702499025347\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"focus_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003189780492760363,\n        \"min\": 0.0,\n        \"max\": 0.0055248618784530384,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0055248618784530384,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"focus_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0769800358919501,\n        \"min\": 0.0,\n        \"max\": 0.13333333333333333,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.13333333333333333,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"focus_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006125732299094172,\n        \"min\": 0.0,\n        \"max\": 0.010610079575596816,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.010610079575596816,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"focus_average_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009371685883694691,\n        \"min\": 0.0075,\n        \"max\": 0.02624325421238197,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0075,\n          0.02624325421238197\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"focus_brier_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04776159036037696,\n        \"min\": 0.0075,\n        \"max\": 0.0999205688105518,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0075,\n          0.0999205688105518\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "snapshot_logistic_preprocessor, _, _ = make_preprocessor(\n",
        "    X_snapshot_train,\n",
        "    scale_numeric=True,\n",
        ")\n",
        "snapshot_forest_preprocessor, _, _ = make_preprocessor(\n",
        "    X_snapshot_train,\n",
        "    scale_numeric=False,\n",
        ")\n",
        "\n",
        "snapshot_logistic = Pipeline([\n",
        "    (\"preprocessor\", snapshot_logistic_preprocessor),\n",
        "    (\n",
        "        \"model\",\n",
        "        LogisticRegression(\n",
        "            C=logistic_search.best_params_[\"model__C\"],\n",
        "            class_weight=\"balanced\",\n",
        "            solver=\"lbfgs\",\n",
        "            penalty=\"l2\",\n",
        "            tol=1e-3,\n",
        "            max_iter=2000,\n",
        "            random_state=RANDOM_STATE,\n",
        "        ),\n",
        "    ),\n",
        "])\n",
        "\n",
        "best_forest_structural_params = {\n",
        "    key.replace(\"model__\", \"\"): value\n",
        "    for key, value in forest_search.best_params_.items()\n",
        "    if key != \"model__class_weight\"\n",
        "}\n",
        "\n",
        "snapshot_forest = Pipeline([\n",
        "    (\"preprocessor\", snapshot_forest_preprocessor),\n",
        "    (\n",
        "        \"model\",\n",
        "        RandomForestClassifier(\n",
        "            **best_forest_structural_params,\n",
        "            class_weight=\"balanced_subsample\",\n",
        "            random_state=RANDOM_STATE,\n",
        "            n_jobs=N_JOBS,\n",
        "        ),\n",
        "    ),\n",
        "])\n",
        "\n",
        "snapshot_dummy = Pipeline([\n",
        "    (\n",
        "        \"preprocessor\",\n",
        "        clone(snapshot_forest_preprocessor),\n",
        "    ),\n",
        "    (\n",
        "        \"model\",\n",
        "        DummyClassifier(\n",
        "            strategy=\"most_frequent\",\n",
        "            random_state=RANDOM_STATE,\n",
        "        ),\n",
        "    ),\n",
        "])\n",
        "\n",
        "snapshot_dummy.fit(\n",
        "    X_snapshot_train,\n",
        "    y_snapshot_train,\n",
        ")\n",
        "snapshot_logistic.fit(\n",
        "    X_snapshot_train,\n",
        "    y_snapshot_train,\n",
        ")\n",
        "snapshot_forest.fit(\n",
        "    X_snapshot_train,\n",
        "    y_snapshot_train,\n",
        ")\n",
        "\n",
        "snapshot_evaluations = {\n",
        "    \"Snapshot majority baseline\": evaluate_fitted_model(\n",
        "        \"Snapshot majority baseline\",\n",
        "        snapshot_dummy,\n",
        "        X_snapshot_train,\n",
        "        y_snapshot_train,\n",
        "        X_snapshot_test,\n",
        "        y_snapshot_test,\n",
        "        SNAPSHOT_CLASS_ORDER,\n",
        "        SNAPSHOT_FOCUS_CLASS,\n",
        "    ),\n",
        "    \"Snapshot Logistic Regression\": evaluate_fitted_model(\n",
        "        \"Snapshot Logistic Regression\",\n",
        "        snapshot_logistic,\n",
        "        X_snapshot_train,\n",
        "        y_snapshot_train,\n",
        "        X_snapshot_test,\n",
        "        y_snapshot_test,\n",
        "        SNAPSHOT_CLASS_ORDER,\n",
        "        SNAPSHOT_FOCUS_CLASS,\n",
        "    ),\n",
        "    \"Snapshot Random Forest\": evaluate_fitted_model(\n",
        "        \"Snapshot Random Forest\",\n",
        "        snapshot_forest,\n",
        "        X_snapshot_train,\n",
        "        y_snapshot_train,\n",
        "        X_snapshot_test,\n",
        "        y_snapshot_test,\n",
        "        SNAPSHOT_CLASS_ORDER,\n",
        "        SNAPSHOT_FOCUS_CLASS,\n",
        "    ),\n",
        "}\n",
        "\n",
        "snapshot_test_summary = pd.DataFrame([\n",
        "    evaluation[\"summary\"]\n",
        "    for evaluation in snapshot_evaluations.values()\n",
        "]).set_index(\"model\")\n",
        "\n",
        "display(snapshot_test_summary)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "db674281",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "db674281",
        "outputId": "ec853005-cde1-4c7e-ef56-ce0aa9b15371"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Censoring sensitivity conclusion\n\nThe primary non-current candidate achieved hold-out macro F1 0.432. The best full-snapshot model was **Snapshot majority baseline**, with macro F1 0.330, only +0.000 above its majority baseline. The full snapshot is dominated by unresolved Current loans, so high accuracy can coexist with weak minority discrimination. This comparison supports the decision to treat the non-current cohort as the primary experiment and the full snapshot as an operational sensitivity analysis."
          },
          "metadata": {}
        }
      ],
      "source": [
        "primary_best_test_macro_f1 = test_summary.loc[\n",
        "    champion_name,\n",
        "    \"test_macro_f1\",\n",
        "]\n",
        "snapshot_best_name = snapshot_test_summary[\n",
        "    \"test_macro_f1\"\n",
        "].idxmax()\n",
        "snapshot_best_macro_f1 = snapshot_test_summary.loc[\n",
        "    snapshot_best_name,\n",
        "    \"test_macro_f1\",\n",
        "]\n",
        "\n",
        "snapshot_baseline_macro_f1 = snapshot_test_summary.loc[\n",
        "    \"Snapshot majority baseline\",\n",
        "    \"test_macro_f1\",\n",
        "]\n",
        "snapshot_best_gain = (\n",
        "    snapshot_best_macro_f1\n",
        "    - snapshot_baseline_macro_f1\n",
        ")\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Censoring sensitivity conclusion\\n\\n\"\n",
        "        f\"The primary non-current candidate achieved hold-out macro F1 \"\n",
        "        f\"{primary_best_test_macro_f1:.3f}. The best full-snapshot model was \"\n",
        "        f\"**{snapshot_best_name}**, with macro F1 {snapshot_best_macro_f1:.3f}, \"\n",
        "        f\"only {snapshot_best_gain:+.3f} above its majority baseline. \"\n",
        "        \"The full snapshot is dominated by unresolved Current loans, so high \"\n",
        "        \"accuracy can coexist with weak minority discrimination. This comparison \"\n",
        "        \"supports the decision to treat the non-current cohort as the primary \"\n",
        "        \"experiment and the full snapshot as an operational sensitivity analysis.\"\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "e128b0b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e128b0b6",
        "outputId": "ab8517cb-33ab-4826-9662-4510fab2e445"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               precision  recall  f1-score    support\n",
              "Performing        0.9901  0.6090    0.7541 1,964.0000\n",
              "Early Warning     0.0116  0.2381    0.0222    21.0000\n",
              "High Risk         0.0055  0.1333    0.0106    15.0000\n",
              "accuracy          0.6015  0.6015    0.6015     0.6015\n",
              "macro avg         0.3357  0.3268    0.2623 2,000.0000\n",
              "weighted avg      0.9724  0.6015    0.7408 2,000.0000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f6ad1bb-0d1d-45be-a006-b3cc7c4d3ec9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Performing</th>\n",
              "      <td>0.9901</td>\n",
              "      <td>0.6090</td>\n",
              "      <td>0.7541</td>\n",
              "      <td>1,964.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Early Warning</th>\n",
              "      <td>0.0116</td>\n",
              "      <td>0.2381</td>\n",
              "      <td>0.0222</td>\n",
              "      <td>21.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High Risk</th>\n",
              "      <td>0.0055</td>\n",
              "      <td>0.1333</td>\n",
              "      <td>0.0106</td>\n",
              "      <td>15.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.6015</td>\n",
              "      <td>0.6015</td>\n",
              "      <td>0.6015</td>\n",
              "      <td>0.6015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.3357</td>\n",
              "      <td>0.3268</td>\n",
              "      <td>0.2623</td>\n",
              "      <td>2,000.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.9724</td>\n",
              "      <td>0.6015</td>\n",
              "      <td>0.7408</td>\n",
              "      <td>2,000.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f6ad1bb-0d1d-45be-a006-b3cc7c4d3ec9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6f6ad1bb-0d1d-45be-a006-b3cc7c4d3ec9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6f6ad1bb-0d1d-45be-a006-b3cc7c4d3ec9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"    )\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44327859933507174,\n        \"min\": 0.0055248618784530384,\n        \"max\": 0.9900662251655629,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9900662251655629,\n          0.011627906976744186,\n          0.972408562599927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21238117275702104,\n        \"min\": 0.13333333333333333,\n        \"max\": 0.6089613034623218,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.23809523809523808,\n          0.3267966249636311,\n          0.13333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34510477510871934,\n        \"min\": 0.010610079575596816,\n        \"max\": 0.7540983606557377,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7540983606557377,\n          0.022172949002217297,\n          0.7408369817252747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1082.2900998809769,\n        \"min\": 0.6015,\n        \"max\": 2000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21.0,\n          2000.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               precision  recall  f1-score    support\n",
              "Performing        0.9820  0.9995    0.9907 1,964.0000\n",
              "Early Warning     0.0000  0.0000    0.0000    21.0000\n",
              "High Risk         0.0000  0.0000    0.0000    15.0000\n",
              "accuracy          0.9815  0.9815    0.9815     0.9815\n",
              "macro avg         0.3273  0.3332    0.3302 2,000.0000\n",
              "weighted avg      0.9643  0.9815    0.9728 2,000.0000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-da380307-2796-4662-a53e-eea94069a0a0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Performing</th>\n",
              "      <td>0.9820</td>\n",
              "      <td>0.9995</td>\n",
              "      <td>0.9907</td>\n",
              "      <td>1,964.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Early Warning</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>21.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High Risk</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>15.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.9815</td>\n",
              "      <td>0.9815</td>\n",
              "      <td>0.9815</td>\n",
              "      <td>0.9815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.3273</td>\n",
              "      <td>0.3332</td>\n",
              "      <td>0.3302</td>\n",
              "      <td>2,000.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.9643</td>\n",
              "      <td>0.9815</td>\n",
              "      <td>0.9728</td>\n",
              "      <td>2,000.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da380307-2796-4662-a53e-eea94069a0a0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da380307-2796-4662-a53e-eea94069a0a0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-da380307-2796-4662-a53e-eea94069a0a0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"    )\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48963493447290074,\n        \"min\": 0.0,\n        \"max\": 0.9819909954977488,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          0.9643151575787894,\n          0.9815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4952658645155992,\n        \"min\": 0.0,\n        \"max\": 0.9994908350305499,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.33316361167685,\n          0.9994908350305499\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1-score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49241520660913934,\n        \"min\": 0.0,\n        \"max\": 0.9906636386575827,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          0.9728316931617462,\n          0.9815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1082.2199224543233,\n        \"min\": 0.9815,\n        \"max\": 2000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21.0,\n          2000.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for model_name in [\n",
        "    \"Snapshot Logistic Regression\",\n",
        "    \"Snapshot Random Forest\",\n",
        "]:\n",
        "    display(snapshot_evaluations[model_name][\"report\"])\n",
        "    plot_confusion_matrices(\n",
        "        y_snapshot_test,\n",
        "        snapshot_evaluations[model_name][\"predictions\"],\n",
        "        SNAPSHOT_CLASS_ORDER,\n",
        "        model_name,\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dbcfff56",
      "metadata": {
        "id": "dbcfff56"
      },
      "source": [
        "## 22. Deployment gate, model card and reproducible export"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "05a70370",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "05a70370",
        "outputId": "c86299ed-05bb-4c90-e592-b22b248e3d32"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                gate  passed\n",
              "0        Macro F1 improves baseline by at least 0.05    True\n",
              "1                 Balanced accuracy is at least 0.50   False\n",
              "2         Severe Delinquency recall is at least 0.50   False\n",
              "3  Training macro F1 exceeds test macro F1 by no ...   False"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>gate</th>\n",
              "      <th>passed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Macro F1 improves baseline by at least 0.05</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Balanced accuracy is at least 0.50</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Severe Delinquency recall is at least 0.50</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Training macro F1 exceeds test macro F1 by no ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-145f2c57-3d7e-4b02-b280-6a237d338e8b')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-145f2c57-3d7e-4b02-b280-6a237d338e8b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-145f2c57-3d7e-4b02-b280-6a237d338e8b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_d6788d5a-768e-4689-8684-e2fdef1310b3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        border-radius: 50%;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('deployment_gate_table')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d6788d5a-768e-4689-8684-e2fdef1310b3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('deployment_gate_table');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "deployment_gate_table",
              "summary": "{\n  \"name\": \"deployment_gate_table\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"gate\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Balanced accuracy is at least 0.50\",\n          \"Training macro F1 exceeds test macro F1 by no more than 0.15\",\n          \"Macro F1 improves baseline by at least 0.05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passed\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Deployment eligible: False\n"
          ]
        }
      ],
      "source": [
        "# The candidate must pass explicit technical gates before it can be described\n",
        "# as suitable for deployment. Failing a gate is a valid research finding.\n",
        "baseline_macro_f1 = test_summary.loc[\n",
        "    \"Majority baseline\",\n",
        "    \"test_macro_f1\",\n",
        "]\n",
        "candidate_metrics = test_summary.loc[champion_name]\n",
        "\n",
        "deployment_gates = {\n",
        "    \"Macro F1 improves baseline by at least 0.05\": (\n",
        "        candidate_metrics[\"test_macro_f1\"]\n",
        "        >= baseline_macro_f1 + 0.05\n",
        "    ),\n",
        "    \"Balanced accuracy is at least 0.50\": (\n",
        "        candidate_metrics[\"test_balanced_accuracy\"]\n",
        "        >= 0.50\n",
        "    ),\n",
        "    f\"{PRIMARY_FOCUS_CLASS} recall is at least 0.50\": (\n",
        "        candidate_metrics[\"focus_recall\"]\n",
        "        >= 0.50\n",
        "    ),\n",
        "    \"Training macro F1 exceeds test macro F1 by no more than 0.15\": (\n",
        "        candidate_metrics[\"train_macro_f1\"]\n",
        "        - candidate_metrics[\"test_macro_f1\"]\n",
        "        <= 0.15\n",
        "    ),\n",
        "}\n",
        "\n",
        "deployment_gate_table = pd.DataFrame({\n",
        "    \"gate\": list(deployment_gates.keys()),\n",
        "    \"passed\": list(deployment_gates.values()),\n",
        "})\n",
        "\n",
        "deployment_eligible = bool(\n",
        "    deployment_gate_table[\"passed\"].all()\n",
        ")\n",
        "\n",
        "display(deployment_gate_table)\n",
        "print(\"Deployment eligible:\", deployment_eligible)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "d34c7c1a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d34c7c1a",
        "outputId": "56245288-46a5-4a1a-bdc8-c301cd5939f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved research candidate: /content/acca7026_outputs/acca7026_research_candidate.joblib\n",
            "Saved model card: /content/acca7026_outputs/acca7026_model_card.json\n"
          ]
        }
      ],
      "source": [
        "OUTPUT_DIRECTORY = Path(\"acca7026_outputs\")\n",
        "OUTPUT_DIRECTORY.mkdir(exist_ok=True)\n",
        "\n",
        "MODEL_OUTPUT_PATH = (\n",
        "    OUTPUT_DIRECTORY\n",
        "    / \"acca7026_research_candidate.joblib\"\n",
        ")\n",
        "MODEL_CARD_PATH = (\n",
        "    OUTPUT_DIRECTORY\n",
        "    / \"acca7026_model_card.json\"\n",
        ")\n",
        "\n",
        "joblib.dump(champion, MODEL_OUTPUT_PATH)\n",
        "\n",
        "model_card = {\n",
        "    \"project\": \"ACCA7026 Credit Risk Assessment\",\n",
        "    \"model_status\": (\n",
        "        \"Deployment candidate\"\n",
        "        if deployment_eligible\n",
        "        else \"Research candidate only - deployment gates failed\"\n",
        "    ),\n",
        "    \"selected_model\": champion_name,\n",
        "    \"selection_rule\": \"Highest repeated-CV macro F1 on the primary training cohort\",\n",
        "    \"primary_target_definition\": PRIMARY_TARGET_MAP,\n",
        "    \"snapshot_target_definition\": SNAPSHOT_TARGET_MAP,\n",
        "    \"primary_class_order\": PRIMARY_CLASS_ORDER,\n",
        "    \"training_rows\": int(len(X_train)),\n",
        "    \"test_rows\": int(len(X_test)),\n",
        "    \"predictor_count\": int(X_train.shape[1]),\n",
        "    \"dataset_sha256\": dataset_hash,\n",
        "    \"random_state\": RANDOM_STATE,\n",
        "    \"cv_splits\": CV_SPLITS,\n",
        "    \"cv_repeats\": CV_REPEATS,\n",
        "    \"best_parameters\": (\n",
        "        logistic_search.best_params_\n",
        "        if champion_name == \"Logistic Regression\"\n",
        "        else forest_search.best_params_\n",
        "    ),\n",
        "    \"validation_metrics\": (\n",
        "        validation_summary.loc[champion_name]\n",
        "        .drop(\"best_parameters\")\n",
        "        .to_dict()\n",
        "    ),\n",
        "    \"test_metrics\": candidate_metrics.to_dict(),\n",
        "    \"deployment_gates\": deployment_gates,\n",
        "    \"excluded_post_outcome_columns\": POST_OUTCOME_COLUMNS,\n",
        "    \"excluded_lender_derived_columns\": LENDER_DERIVED_COLUMNS,\n",
        "    \"engineered_features\": engineered_features,\n",
        "    \"known_limitations\": [\n",
        "        \"The supplied data are a cross-sectional status snapshot rather than a fully mature default cohort.\",\n",
        "        \"Delinquent loans may subsequently recover.\",\n",
        "        \"The severe class remains small, producing uncertain estimates.\",\n",
        "        \"Direct protected characteristics are unavailable.\",\n",
        "        \"Subgroup analysis cannot establish demographic fairness.\",\n",
        "        \"Feature attribution describes fitted associations, not causal effects.\",\n",
        "        \"Temporal out-of-time validation is unavailable.\",\n",
        "    ],\n",
        "}\n",
        "\n",
        "with MODEL_CARD_PATH.open(\n",
        "    \"w\",\n",
        "    encoding=\"utf-8\",\n",
        ") as model_card_file:\n",
        "    json.dump(\n",
        "        model_card,\n",
        "        model_card_file,\n",
        "        indent=2,\n",
        "        default=str,\n",
        "    )\n",
        "\n",
        "print(\"Saved research candidate:\", MODEL_OUTPUT_PATH.resolve())\n",
        "print(\"Saved model card:\", MODEL_CARD_PATH.resolve())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "b0c649d8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0c649d8",
        "outputId": "a31239c4-c1e3-445f-c743-9362b13e95a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Report-ready outputs: /content/acca7026_outputs\n",
            "['primary_test_summary.csv', 'acca7026_research_candidate.joblib', 'bootstrap_intervals.csv', 'subgroup_audit.csv', 'permutation_importance.csv', 'lender_feature_sensitivity.csv', 'snapshot_test_summary.csv', 'validation_summary.csv', 'acca7026_model_card.json', 'deployment_gates.csv', 'imbalance_sensitivity.csv']\n"
          ]
        }
      ],
      "source": [
        "# Export report-ready evidence tables.\n",
        "validation_summary.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"validation_summary.csv\"\n",
        ")\n",
        "test_summary.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"primary_test_summary.csv\"\n",
        ")\n",
        "confidence_table.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"bootstrap_intervals.csv\",\n",
        "    index=False,\n",
        ")\n",
        "forest_importance.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"permutation_importance.csv\",\n",
        "    index=False,\n",
        ")\n",
        "subgroup_audit.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"subgroup_audit.csv\",\n",
        "    index=False,\n",
        ")\n",
        "imbalance_sensitivity.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"imbalance_sensitivity.csv\"\n",
        ")\n",
        "lender_sensitivity.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"lender_feature_sensitivity.csv\"\n",
        ")\n",
        "snapshot_test_summary.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"snapshot_test_summary.csv\"\n",
        ")\n",
        "deployment_gate_table.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"deployment_gates.csv\",\n",
        "    index=False,\n",
        ")\n",
        "\n",
        "print(\"Report-ready outputs:\", OUTPUT_DIRECTORY.resolve())\n",
        "print([path.name for path in OUTPUT_DIRECTORY.iterdir()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "e8829b1a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "e8829b1a",
        "outputId": "f7469ea0-bd04-43cb-e036-b29023153c1e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Final governance decision\n\nThe candidate is not suitable for deployment because it failed: Balanced accuracy is at least 0.50; Severe Delinquency recall is at least 0.50; Training macro F1 exceeds test macro F1 by no more than 0.15. The correct conclusion is a rigorous negative result rather than an inflated claim of predictive success."
          },
          "metadata": {}
        }
      ],
      "source": [
        "if deployment_eligible:\n",
        "    final_decision = (\n",
        "        \"The candidate passed the predefined technical gates. It still \"\n",
        "        \"requires temporal validation, demographic fairness testing, \"\n",
        "        \"independent model validation and controlled human oversight.\"\n",
        "    )\n",
        "else:\n",
        "    failed_gates = deployment_gate_table.loc[\n",
        "        ~deployment_gate_table[\"passed\"],\n",
        "        \"gate\",\n",
        "    ].tolist()\n",
        "\n",
        "    final_decision = (\n",
        "        \"The candidate is not suitable for deployment because it failed: \"\n",
        "        + \"; \".join(failed_gates)\n",
        "        + \". The correct conclusion is a rigorous negative result rather \"\n",
        "          \"than an inflated claim of predictive success.\"\n",
        "    )\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Final governance decision\\n\\n\"\n",
        "        + final_decision\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a87eef4c",
      "metadata": {
        "id": "a87eef4c"
      },
      "source": [
        "## 23. Report-ready synthesis generated from the executed results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "80744d55",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "id": "80744d55",
        "outputId": "5aba65aa-7589-4f38-eaad-a48c9a28a3d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### Results paragraph for adaptation into the report\n\nThe primary non-current cohort contained 625 loans across 3 observed performance classes. Repeated stratified cross-validation selected Random Forest using macro F1. Its mean validation macro F1 was 0.383 (SD 0.037). On the untouched hold-out set, the candidate achieved accuracy 0.656, balanced accuracy 0.436, macro F1 0.432, macro one-vs-rest ROC-AUC 0.608, and Severe Delinquency recall 0.333. The majority baseline macro F1 was 0.277. Deployment eligibility was False. These results should be interpreted alongside the small severe-class support, the cross-sectional target design and the absence of protected demographic variables."
          },
          "metadata": {}
        }
      ],
      "source": [
        "best_test_name = test_summary[\"test_macro_f1\"].idxmax()\n",
        "best_test_metrics = test_summary.loc[best_test_name]\n",
        "\n",
        "report_results_paragraph = (\n",
        "    f\"The primary non-current cohort contained {len(primary_cohort):,} loans \"\n",
        "    f\"across {len(PRIMARY_CLASS_ORDER)} observed performance classes. \"\n",
        "    f\"Repeated stratified cross-validation selected {champion_name} using \"\n",
        "    f\"macro F1. Its mean validation macro F1 was \"\n",
        "    f\"{validation_summary.loc[champion_name, 'cv_macro_f1_mean']:.3f} \"\n",
        "    f\"(SD {validation_summary.loc[champion_name, 'cv_macro_f1_std']:.3f}). \"\n",
        "    f\"On the untouched hold-out set, the candidate achieved accuracy \"\n",
        "    f\"{candidate_metrics['test_accuracy']:.3f}, balanced accuracy \"\n",
        "    f\"{candidate_metrics['test_balanced_accuracy']:.3f}, macro F1 \"\n",
        "    f\"{candidate_metrics['test_macro_f1']:.3f}, macro one-vs-rest ROC-AUC \"\n",
        "    f\"{candidate_metrics['test_macro_roc_auc_ovr']:.3f}, and \"\n",
        "    f\"{PRIMARY_FOCUS_CLASS} recall {candidate_metrics['focus_recall']:.3f}. \"\n",
        "    f\"The majority baseline macro F1 was {baseline_macro_f1:.3f}. \"\n",
        "    f\"Deployment eligibility was {deployment_eligible}. \"\n",
        "    \"These results should be interpreted alongside the small severe-class \"\n",
        "    \"support, the cross-sectional target design and the absence of protected \"\n",
        "    \"demographic variables.\"\n",
        ")\n",
        "\n",
        "display(\n",
        "    Markdown(\n",
        "        \"### Results paragraph for adaptation into the report\\n\\n\"\n",
        "        + report_results_paragraph\n",
        "    )\n",
        ")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.x"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}