{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "bb575b16",
      "metadata": {
        "id": "bb575b16"
      },
      "source": [
        "## 1. Environment and package setup\n",
        "\n",
        "The installation code runs only when a required package is unavailable. The notebook is compatible with Google Colab, JupyterLab, and standard Python environments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "5e93f970",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5e93f970",
        "outputId": "a253d67c-273f-47f1-b287-506c10484a99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Required packages are available.\n"
          ]
        }
      ],
      "source": [
        "import importlib.util\n",
        "import subprocess\n",
        "import sys\n",
        "\n",
        "REQUIRED_PACKAGES = {\n",
        "    \"imblearn\": \"imbalanced-learn\",\n",
        "    \"shap\": \"shap\",\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(\n",
        "            [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", package_name]\n",
        "        )\n",
        "\n",
        "print(\"Required packages are available.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "88d0910e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88d0910e",
        "outputId": "94f95748-1a09-4a81-b18e-e0ef2f510d27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python: 3.12.13\n",
            "pandas: 2.2.2\n",
            "scikit-learn: 1.6.1\n"
          ]
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "import platform\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 sklearn\n",
        "\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.pipeline import Pipeline as ImbPipeline\n",
        "\n",
        "from sklearn.base import clone\n",
        "from sklearn.compose import ColumnTransformer\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",
        "    PrecisionRecallDisplay,\n",
        "    RocCurveDisplay,\n",
        "    accuracy_score,\n",
        "    average_precision_score,\n",
        "    balanced_accuracy_score,\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    f1_score,\n",
        "    fbeta_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    roc_auc_score,\n",
        ")\n",
        "from sklearn.model_selection import (\n",
        "    RandomizedSearchCV,\n",
        "    StratifiedKFold,\n",
        "    train_test_split,\n",
        ")\n",
        "from sklearn.pipeline import Pipeline as SkPipeline\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "RANDOM_STATE = 42\n",
        "TEST_SIZE = 0.20\n",
        "VALIDATION_SHARE_OF_REMAINDER = 0.25  # Gives 60% train, 20% validation, 20% test\n",
        "CV_FOLDS = 5\n",
        "N_JOBS = -1\n",
        "\n",
        "np.random.seed(RANDOM_STATE)\n",
        "\n",
        "print(\"Python:\", platform.python_version())\n",
        "print(\"pandas:\", pd.__version__)\n",
        "print(\"scikit-learn:\", sklearn.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c32c0ac",
      "metadata": {
        "id": "5c32c0ac"
      },
      "source": [
        "## 2. Load and validate the dataset\n",
        "\n",
        "The loading cell searches common local and Colab locations. If the file cannot be located in Colab, an upload prompt is displayed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "e3a14b25",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "e3a14b25",
        "outputId": "c5c97ba5-1536-4821-9217-b7977d9c8eb2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded: loans_full_schema.csv\n",
            "Dataset shape: (10000, 56)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Unnamed: 0                emp_title  emp_length state homeownership  \\\n",
              "0           1  global config engineer          3.0    NJ      MORTGAGE   \n",
              "1           2   warehouse office clerk        10.0    HI          RENT   \n",
              "2           3                 assembly         3.0    WI          RENT   \n",
              "3           4         customer service         1.0    PA          RENT   \n",
              "4           5     security supervisor         10.0    CA          RENT   \n",
              "\n",
              "   annual_income  verified_income  debt_to_income  annual_income_joint  \\\n",
              "0        90000.0         Verified           18.01                  NaN   \n",
              "1        40000.0     Not Verified            5.04                  NaN   \n",
              "2        40000.0  Source Verified           21.15                  NaN   \n",
              "3        30000.0     Not Verified           10.16                  NaN   \n",
              "4        35000.0         Verified           57.96              57000.0   \n",
              "\n",
              "  verification_income_joint  ...  sub_grade  issue_month  loan_status  \\\n",
              "0                       NaN  ...         C3     Mar-2018      Current   \n",
              "1                       NaN  ...         C1     Feb-2018      Current   \n",
              "2                       NaN  ...         D1     Feb-2018      Current   \n",
              "3                       NaN  ...         A3     Jan-2018      Current   \n",
              "4                  Verified  ...         C3     Mar-2018      Current   \n",
              "\n",
              "   initial_listing_status  disbursement_method   balance  paid_total  \\\n",
              "0                   whole                 Cash  27015.86     1999.33   \n",
              "1                   whole                 Cash   4651.37      499.12   \n",
              "2              fractional                 Cash   1824.63      281.80   \n",
              "3                   whole                 Cash  18853.26     3312.89   \n",
              "4                   whole                 Cash  21430.15     2324.65   \n",
              "\n",
              "   paid_principal  paid_interest  paid_late_fees  \n",
              "0          984.14        1015.19             0.0  \n",
              "1          348.63         150.49             0.0  \n",
              "2          175.37         106.43             0.0  \n",
              "3         2746.74         566.15             0.0  \n",
              "4         1569.85         754.80             0.0  \n",
              "\n",
              "[5 rows x 56 columns]"
            ],
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              "\n",
              "  <div id=\"df-3a6c6954-9655-4cd2-9310-3b3d1d05a180\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "<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>...</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>3.0</td>\n",
              "      <td>NJ</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>18.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>C3</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>27015.86</td>\n",
              "      <td>1999.33</td>\n",
              "      <td>984.14</td>\n",
              "      <td>1015.19</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>warehouse office clerk</td>\n",
              "      <td>10.0</td>\n",
              "      <td>HI</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>5.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>C1</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>4651.37</td>\n",
              "      <td>499.12</td>\n",
              "      <td>348.63</td>\n",
              "      <td>150.49</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>assembly</td>\n",
              "      <td>3.0</td>\n",
              "      <td>WI</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>21.15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>D1</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>fractional</td>\n",
              "      <td>Cash</td>\n",
              "      <td>1824.63</td>\n",
              "      <td>281.80</td>\n",
              "      <td>175.37</td>\n",
              "      <td>106.43</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>customer service</td>\n",
              "      <td>1.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>10.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>A3</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>18853.26</td>\n",
              "      <td>3312.89</td>\n",
              "      <td>2746.74</td>\n",
              "      <td>566.15</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>security supervisor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>35000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>57.96</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>...</td>\n",
              "      <td>C3</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>21430.15</td>\n",
              "      <td>2324.65</td>\n",
              "      <td>1569.85</td>\n",
              "      <td>754.80</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 56 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a6c6954-9655-4cd2-9310-3b3d1d05a180')\"\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-3a6c6954-9655-4cd2-9310-3b3d1d05a180 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-3a6c6954-9655-4cd2-9310-3b3d1d05a180');\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"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "CANDIDATE_PATHS = [\n",
        "    Path(\"loans_full_schema.csv\"),\n",
        "    Path(\"/content/loans_full_schema.csv\"),\n",
        "    Path(\"/mnt/data/loans_full_schema.csv\"),\n",
        "]\n",
        "\n",
        "DATA_PATH = next((path for path in CANDIDATE_PATHS if path.exists()), None)\n",
        "\n",
        "if DATA_PATH is None:\n",
        "    try:\n",
        "        from google.colab import files\n",
        "\n",
        "        print(\"Upload loans_full_schema.csv\")\n",
        "        uploaded = files.upload()\n",
        "        uploaded_name = next(iter(uploaded))\n",
        "        DATA_PATH = Path(uploaded_name)\n",
        "    except Exception as exc:\n",
        "        raise FileNotFoundError(\n",
        "            \"Place loans_full_schema.csv in the notebook working directory.\"\n",
        "        ) from exc\n",
        "\n",
        "df_raw = pd.read_csv(DATA_PATH)\n",
        "\n",
        "print(f\"Loaded: {DATA_PATH}\")\n",
        "print(\"Dataset shape:\", df_raw.shape)\n",
        "display(df_raw.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "9955b664",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "9955b664",
        "outputId": "8ded6199-6eea-4182-fc65-cdfdca3c1c88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loan-status distribution:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    count\n",
              "loan_status              \n",
              "Current              9375\n",
              "Fully Paid            447\n",
              "In Grace Period        67\n",
              "Late (31-120 days)     66\n",
              "Late (16-30 days)      38\n",
              "Charged Off             7"
            ],
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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",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Current</th>\n",
              "      <td>9375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>In Grace Period</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (31-120 days)</th>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Late (16-30 days)</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charged Off</th>\n",
              "      <td>7</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-d0e5eb12-5601-4bc8-bae1-b63217fa8ffe')\"\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-d0e5eb12-5601-4bc8-bae1-b63217fa8ffe 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-d0e5eb12-5601-4bc8-bae1-b63217fa8ffe');\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\": \"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}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "REQUIRED_COLUMNS = {\n",
        "    \"loan_status\",\n",
        "    \"paid_total\",\n",
        "    \"paid_principal\",\n",
        "    \"paid_interest\",\n",
        "    \"paid_late_fees\",\n",
        "    \"balance\",\n",
        "}\n",
        "\n",
        "missing_required = REQUIRED_COLUMNS.difference(df_raw.columns)\n",
        "if missing_required:\n",
        "    raise ValueError(f\"Required columns are missing: {sorted(missing_required)}\")\n",
        "\n",
        "print(\"Loan-status distribution:\")\n",
        "display(\n",
        "    df_raw[\"loan_status\"]\n",
        "    .value_counts(dropna=False)\n",
        "    .rename_axis(\"loan_status\")\n",
        "    .to_frame(\"count\")\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "44899889",
      "metadata": {
        "id": "44899889"
      },
      "source": [
        "## 3. Define the target and remove leakage\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "cd510fef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "cd510fef",
        "outputId": "58c98c4c-8227-4f62-c1a6-ed380467e39b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Leakage audit:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  excluded_variable                                             reason\n",
              "0        paid_total  Post-origination cumulative repayment information\n",
              "1    paid_principal   Post-origination principal repayment information\n",
              "2     paid_interest    Post-origination interest repayment information\n",
              "3    paid_late_fees              Post-origination late-fee information\n",
              "4           balance            Outstanding balance after loan issuance"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85883440-71e5-424d-9939-03aabc43b719\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>excluded_variable</th>\n",
              "      <th>reason</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>paid_total</td>\n",
              "      <td>Post-origination cumulative repayment information</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>paid_principal</td>\n",
              "      <td>Post-origination principal repayment information</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>paid_interest</td>\n",
              "      <td>Post-origination interest repayment information</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>paid_late_fees</td>\n",
              "      <td>Post-origination late-fee information</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>balance</td>\n",
              "      <td>Outstanding balance after loan issuance</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\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-85883440-71e5-424d-9939-03aabc43b719 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-85883440-71e5-424d-9939-03aabc43b719');\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_6ace7d3e-6f1a-4fdd-ac63-a20aed4a5b3a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-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('leakage_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",
              "       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_6ace7d3e-6f1a-4fdd-ac63-a20aed4a5b3a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('leakage_audit');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "leakage_audit",
              "summary": "{\n  \"name\": \"leakage_audit\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"excluded_variable\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"paid_principal\",\n          \"balance\",\n          \"paid_interest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reason\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Post-origination principal repayment information\",\n          \"Outstanding balance after loan issuance\",\n          \"Post-origination interest repayment information\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Binary target distribution:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                count  percentage\n",
              "target_default                   \n",
              "Non-default      9889       98.89\n",
              "Default           111        1.11"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f1ec95ab-a9d1-42cd-9070-c5f295436856\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\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>count</th>\n",
              "      <th>percentage</th>\n",
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              "    <tr>\n",
              "      <th>target_default</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Non-default</th>\n",
              "      <td>9889</td>\n",
              "      <td>98.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Default</th>\n",
              "      <td>111</td>\n",
              "      <td>1.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "  <style>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
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              "    }\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",
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              "\n",
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              "      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",
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              "  </style>\n",
              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"print(f\\\"Feature matrix shape after exclusions: {X\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"target_default\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Default\",\n          \"Non-default\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6914,\n        \"min\": 111,\n        \"max\": 9889,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          111,\n          9889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.14090106442062,\n        \"min\": 1.11,\n        \"max\": 98.89,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.11,\n          98.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature matrix shape after exclusions: (10000, 48)\n"
          ]
        }
      ],
      "source": [
        "DEFAULT_STATUSES = {\n",
        "    \"Charged Off\",\n",
        "    \"Late (31-120 days)\",\n",
        "    \"Late (16-30 days)\",\n",
        "    \"Default\",\n",
        "}\n",
        "\n",
        "y = (\n",
        "    df_raw[\"loan_status\"]\n",
        "    .isin(DEFAULT_STATUSES)\n",
        "    .astype(int)\n",
        "    .rename(\"target_default\")\n",
        ")\n",
        "\n",
        "LEAKAGE_COLUMNS = [\n",
        "    \"paid_total\",\n",
        "    \"paid_principal\",\n",
        "    \"paid_interest\",\n",
        "    \"paid_late_fees\",\n",
        "    \"balance\",\n",
        "]\n",
        "\n",
        "NON_PREDICTIVE_COLUMNS = [\n",
        "    \"Unnamed: 0\",  # Row identifier\n",
        "    \"emp_title\",   # Very high-cardinality free-text field\n",
        "]\n",
        "\n",
        "DROP_COLUMNS = [\"loan_status\", *LEAKAGE_COLUMNS, *NON_PREDICTIVE_COLUMNS]\n",
        "X = df_raw.drop(columns=DROP_COLUMNS, errors=\"ignore\").copy()\n",
        "\n",
        "assert \"loan_status\" not in X.columns\n",
        "assert \"target_default\" not in X.columns\n",
        "assert not set(LEAKAGE_COLUMNS).intersection(X.columns)\n",
        "\n",
        "leakage_audit = pd.DataFrame(\n",
        "    {\n",
        "        \"excluded_variable\": LEAKAGE_COLUMNS,\n",
        "        \"reason\": [\n",
        "            \"Post-origination cumulative repayment information\",\n",
        "            \"Post-origination principal repayment information\",\n",
        "            \"Post-origination interest repayment information\",\n",
        "            \"Post-origination late-fee information\",\n",
        "            \"Outstanding balance after loan issuance\",\n",
        "        ],\n",
        "    }\n",
        ")\n",
        "\n",
        "print(\"Leakage audit:\")\n",
        "display(leakage_audit)\n",
        "\n",
        "class_table = (\n",
        "    y.value_counts()\n",
        "    .sort_index()\n",
        "    .rename(index={0: \"Non-default\", 1: \"Default\"})\n",
        "    .to_frame(\"count\")\n",
        ")\n",
        "class_table[\"percentage\"] = 100 * class_table[\"count\"] / len(y)\n",
        "\n",
        "print(\"Binary target distribution:\")\n",
        "display(class_table.round(3))\n",
        "\n",
        "print(f\"Feature matrix shape after exclusions: {X.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c946f1af",
      "metadata": {
        "id": "c946f1af"
      },
      "source": [
        "## 4. Initial exploration\n",
        "\n",
        "These visualisations use raw data only for descriptive analysis. No imputation, encoding, scaling, or resampling is fitted before the data split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "3ac2a835",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "3ac2a835",
        "outputId": "253266a1-2eac-43b1-bd31-e96c02381972"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Default prevalence: 1.1100%\n",
            "Naive majority-class accuracy: 98.8900%\n",
            "No-skill PR-AUC baseline: 0.011100\n"
          ]
        }
      ],
      "source": [
        "class_counts = y.value_counts().sort_index()\n",
        "\n",
        "plt.figure(figsize=(7, 4))\n",
        "plt.bar([\"Non-default\", \"Default\"], class_counts.values)\n",
        "plt.title(\"Target-Class Distribution\")\n",
        "plt.ylabel(\"Number of loans\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "prevalence = y.mean()\n",
        "majority_accuracy = max(y.mean(), 1 - y.mean())\n",
        "\n",
        "print(f\"Default prevalence: {prevalence:.4%}\")\n",
        "print(f\"Naive majority-class accuracy: {majority_accuracy:.4%}\")\n",
        "print(f\"No-skill PR-AUC baseline: {prevalence:.6f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "682e22f8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "682e22f8",
        "outputId": "07ce340a-f019-43c8-832d-5efbed3fa743"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                  missing_count  missing_percentage\n",
              "verification_income_joint                  8545               85.45\n",
              "annual_income_joint                        8505               85.05\n",
              "debt_to_income_joint                       8505               85.05\n",
              "months_since_90d_late                      7715               77.15\n",
              "months_since_last_delinq                   5658               56.58\n",
              "months_since_last_credit_inquiry           1271               12.71\n",
              "emp_length                                  817                8.17\n",
              "num_accounts_120d_past_due                  318                3.18\n",
              "debt_to_income                               24                0.24\n",
              "verified_income                               0                0.00\n",
              "delinq_2y                                     0                0.00\n",
              "earliest_credit_line                          0                0.00\n",
              "inquiries_last_12m                            0                0.00\n",
              "state                                         0                0.00\n",
              "annual_income                                 0                0.00"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>missing_count</th>\n",
              "      <th>missing_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>verification_income_joint</th>\n",
              "      <td>8545</td>\n",
              "      <td>85.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_income_joint</th>\n",
              "      <td>8505</td>\n",
              "      <td>85.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income_joint</th>\n",
              "      <td>8505</td>\n",
              "      <td>85.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_90d_late</th>\n",
              "      <td>7715</td>\n",
              "      <td>77.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_delinq</th>\n",
              "      <td>5658</td>\n",
              "      <td>56.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_credit_inquiry</th>\n",
              "      <td>1271</td>\n",
              "      <td>12.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_length</th>\n",
              "      <td>817</td>\n",
              "      <td>8.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_accounts_120d_past_due</th>\n",
              "      <td>318</td>\n",
              "      <td>3.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income</th>\n",
              "      <td>24</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verified_income</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delinq_2y</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>earliest_credit_line</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inquiries_last_12m</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_income</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
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              "        document.querySelector('#df-147f272a-0585-4d74-aae1-63a5c826e747 button.colab-df-convert');\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",
              "        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\": \"plt\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"missing_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3756,\n        \"min\": 0,\n        \"max\": 8545,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          24,\n          8505,\n          817\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"missing_percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.56647597450228,\n        \"min\": 0.0,\n        \"max\": 85.45,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.24,\n          85.05,\n          8.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "missing_summary = (\n",
        "    X.isna()\n",
        "    .sum()\n",
        "    .sort_values(ascending=False)\n",
        "    .rename(\"missing_count\")\n",
        "    .to_frame()\n",
        ")\n",
        "missing_summary[\"missing_percentage\"] = (\n",
        "    100 * missing_summary[\"missing_count\"] / len(X)\n",
        ")\n",
        "\n",
        "display(missing_summary.head(15).round(2))\n",
        "\n",
        "top_missing = missing_summary.head(15).sort_values(\"missing_percentage\")\n",
        "\n",
        "plt.figure(figsize=(9, 6))\n",
        "plt.barh(top_missing.index, top_missing[\"missing_percentage\"])\n",
        "plt.title(\"Top 15 Features by Missing-Value Percentage\")\n",
        "plt.xlabel(\"Missing values (%)\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "38c1bd3d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "38c1bd3d",
        "outputId": "6ddab522-8251-490d-ec2e-d62c1e9ff2b3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "numeric_for_correlation = [\n",
        "    column\n",
        "    for column in [\n",
        "        \"annual_income\",\n",
        "        \"debt_to_income\",\n",
        "        \"delinq_2y\",\n",
        "        \"inquiries_last_12m\",\n",
        "        \"total_credit_limit\",\n",
        "        \"total_credit_utilized\",\n",
        "        \"accounts_opened_24m\",\n",
        "        \"loan_amount\",\n",
        "        \"term\",\n",
        "        \"interest_rate\",\n",
        "        \"installment\",\n",
        "    ]\n",
        "    if column in X.columns\n",
        "]\n",
        "\n",
        "correlation_frame = X[numeric_for_correlation].copy()\n",
        "correlation_frame[\"target_default\"] = y\n",
        "correlation_matrix = correlation_frame.corr(numeric_only=True)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "image = plt.imshow(correlation_matrix, aspect=\"auto\", vmin=-1, vmax=1)\n",
        "plt.colorbar(image, label=\"Pearson correlation\")\n",
        "plt.xticks(\n",
        "    range(len(correlation_matrix.columns)),\n",
        "    correlation_matrix.columns,\n",
        "    rotation=90,\n",
        ")\n",
        "plt.yticks(\n",
        "    range(len(correlation_matrix.index)),\n",
        "    correlation_matrix.index,\n",
        ")\n",
        "plt.title(\"Selected Numerical Correlations\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "638a8a9f",
      "metadata": {
        "id": "638a8a9f"
      },
      "source": [
        "## 5. Stratified train, validation, and test split\n",
        "\n",
        "The split is completed **before** any learned preprocessing. The validation set is used for imbalance-strategy comparison and threshold selection. The test set remains untouched until final evaluation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "3ca2e795",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "3ca2e795",
        "outputId": "2dc57be0-bec0-4684-83e8-01b4054c3a32"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "            rows  default_cases  default_rate\n",
              "Train       6000             67       0.01117\n",
              "Validation  2000             22       0.01100\n",
              "Test        2000             22       0.01100"
            ],
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              "      <th>default_rate</th>\n",
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              "    <tr>\n",
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              "      <td>0.01100</td>\n",
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              "    <tr>\n",
              "      <th>Test</th>\n",
              "      <td>2000</td>\n",
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              "    [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-b6e2910b-1032-4c18-80cf-3cabdfb40498 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-b6e2910b-1032-4c18-80cf-3cabdfb40498');\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(split_summary\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"rows\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2309,\n        \"min\": 2000,\n        \"max\": 6000,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2000,\n          6000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 22,\n        \"max\": 67,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          22,\n          67\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"default_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.814954576223639e-05,\n        \"min\": 0.011,\n        \"max\": 0.01117,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.011,\n          0.01117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "X_train_temp, X_test, y_train_temp, y_test = train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=TEST_SIZE,\n",
        "    random_state=RANDOM_STATE,\n",
        "    stratify=y,\n",
        ")\n",
        "\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(\n",
        "    X_train_temp,\n",
        "    y_train_temp,\n",
        "    test_size=VALIDATION_SHARE_OF_REMAINDER,\n",
        "    random_state=RANDOM_STATE,\n",
        "    stratify=y_train_temp,\n",
        ")\n",
        "\n",
        "split_summary = pd.DataFrame(\n",
        "    {\n",
        "        \"rows\": [len(X_train), len(X_valid), len(X_test)],\n",
        "        \"default_cases\": [\n",
        "            int(y_train.sum()),\n",
        "            int(y_valid.sum()),\n",
        "            int(y_test.sum()),\n",
        "        ],\n",
        "        \"default_rate\": [\n",
        "            y_train.mean(),\n",
        "            y_valid.mean(),\n",
        "            y_test.mean(),\n",
        "        ],\n",
        "    },\n",
        "    index=[\"Train\", \"Validation\", \"Test\"],\n",
        ")\n",
        "\n",
        "display(split_summary.round(5))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ce8f4b7",
      "metadata": {
        "id": "1ce8f4b7"
      },
      "source": [
        "## 6. Leakage-safe preprocessing pipelines\n",
        "\n",
        "- Numerical features: median imputation; Logistic Regression also uses standardisation.\n",
        "- Categorical features: most-frequent imputation followed by one-hot encoding.\n",
        "- Unknown test categories are ignored safely.\n",
        "- Random oversampling, when selected, occurs **inside** the imbalanced-learn pipeline and is therefore applied only to training data or training folds.\n",
        "\n",
        "Standard SMOTE is deliberately not used after one-hot encoding because it may create fractional synthetic values across nominal dummy variables. Random oversampling preserves valid categorical combinations while still addressing minority-class scarcity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "4ee7b843",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ee7b843",
        "outputId": "e19c237a-a70d-4fd2-825c-2e8692790a4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numerical features: 37\n",
            "Categorical features: 11\n"
          ]
        }
      ],
      "source": [
        "NUMERIC_COLUMNS = X_train.select_dtypes(include=np.number).columns.tolist()\n",
        "CATEGORICAL_COLUMNS = X_train.select_dtypes(exclude=np.number).columns.tolist()\n",
        "\n",
        "try:\n",
        "    one_hot_encoder = OneHotEncoder(\n",
        "        handle_unknown=\"ignore\",\n",
        "        drop=\"first\",\n",
        "        sparse_output=True,\n",
        "    )\n",
        "except TypeError:\n",
        "    one_hot_encoder = OneHotEncoder(\n",
        "        handle_unknown=\"ignore\",\n",
        "        drop=\"first\",\n",
        "        sparse=True,\n",
        "    )\n",
        "\n",
        "def make_preprocessor(scale_numeric: bool) -> ColumnTransformer:\n",
        "    numeric_steps = [\n",
        "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
        "    ]\n",
        "\n",
        "    if scale_numeric:\n",
        "        # with_mean=False preserves sparse compatibility after column transformation\n",
        "        numeric_steps.append(\n",
        "            (\"scaler\", StandardScaler(with_mean=False))\n",
        "        )\n",
        "\n",
        "    numeric_pipeline = SkPipeline(numeric_steps)\n",
        "\n",
        "    categorical_pipeline = SkPipeline(\n",
        "        [\n",
        "            (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
        "            (\"onehot\", clone(one_hot_encoder)),\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    return ColumnTransformer(\n",
        "        transformers=[\n",
        "            (\"numeric\", numeric_pipeline, NUMERIC_COLUMNS),\n",
        "            (\"categorical\", categorical_pipeline, CATEGORICAL_COLUMNS),\n",
        "        ],\n",
        "        remainder=\"drop\",\n",
        "        sparse_threshold=1.0,\n",
        "        verbose_feature_names_out=False,\n",
        "    )\n",
        "\n",
        "print(\"Numerical features:\", len(NUMERIC_COLUMNS))\n",
        "print(\"Categorical features:\", len(CATEGORICAL_COLUMNS))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4098f45",
      "metadata": {
        "id": "b4098f45"
      },
      "source": [
        "## 7. Metric functions\n",
        "\n",
        "PR-AUC is emphasised because the default class is rare. F2 gives recall twice the weight of precision, reflecting the higher operational cost of failing to identify a genuinely risky borrower. Accuracy is retained only for completeness."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "445b1fd6",
      "metadata": {
        "id": "445b1fd6"
      },
      "outputs": [],
      "source": [
        "def metric_row(y_true, probabilities, threshold=0.50):\n",
        "    predictions = (np.asarray(probabilities) >= threshold).astype(int)\n",
        "\n",
        "    return {\n",
        "        \"Threshold\": threshold,\n",
        "        \"Accuracy\": accuracy_score(y_true, predictions),\n",
        "        \"Balanced Accuracy\": balanced_accuracy_score(y_true, predictions),\n",
        "        \"Precision (Default)\": precision_score(\n",
        "            y_true, predictions, zero_division=0\n",
        "        ),\n",
        "        \"Recall (Default)\": recall_score(\n",
        "            y_true, predictions, zero_division=0\n",
        "        ),\n",
        "        \"F1 (Default)\": f1_score(\n",
        "            y_true, predictions, zero_division=0\n",
        "        ),\n",
        "        \"F2 (Default)\": fbeta_score(\n",
        "            y_true, predictions, beta=2, zero_division=0\n",
        "        ),\n",
        "        \"ROC-AUC\": roc_auc_score(y_true, probabilities),\n",
        "        \"PR-AUC\": average_precision_score(y_true, probabilities),\n",
        "    }\n",
        "\n",
        "\n",
        "def tune_threshold(y_true, probabilities, beta=2):\n",
        "    candidate_thresholds = np.linspace(0.01, 0.99, 197)\n",
        "    rows = []\n",
        "\n",
        "    for threshold in candidate_thresholds:\n",
        "        row = metric_row(y_true, probabilities, threshold)\n",
        "        rows.append(row)\n",
        "\n",
        "    threshold_table = pd.DataFrame(rows)\n",
        "    objective_column = f\"F{beta} (Default)\"\n",
        "    best_index = threshold_table[objective_column].idxmax()\n",
        "    best_threshold = float(\n",
        "        threshold_table.loc[best_index, \"Threshold\"]\n",
        "    )\n",
        "\n",
        "    return best_threshold, threshold_table\n",
        "\n",
        "\n",
        "def plot_threshold_diagnostics(threshold_table, title):\n",
        "    plt.figure(figsize=(9, 5))\n",
        "    plt.plot(\n",
        "        threshold_table[\"Threshold\"],\n",
        "        threshold_table[\"Precision (Default)\"],\n",
        "        label=\"Precision\",\n",
        "    )\n",
        "    plt.plot(\n",
        "        threshold_table[\"Threshold\"],\n",
        "        threshold_table[\"Recall (Default)\"],\n",
        "        label=\"Recall\",\n",
        "    )\n",
        "    plt.plot(\n",
        "        threshold_table[\"Threshold\"],\n",
        "        threshold_table[\"F1 (Default)\"],\n",
        "        label=\"F1\",\n",
        "    )\n",
        "    plt.plot(\n",
        "        threshold_table[\"Threshold\"],\n",
        "        threshold_table[\"F2 (Default)\"],\n",
        "        label=\"F2\",\n",
        "    )\n",
        "    plt.xlabel(\"Probability threshold\")\n",
        "    plt.ylabel(\"Metric value\")\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "db2af64a",
      "metadata": {
        "id": "db2af64a"
      },
      "source": [
        "## 8. Compare class-imbalance strategies\n",
        "\n",
        "Three strategies are compared for each algorithm using the validation set:\n",
        "\n",
        "1. no explicit balancing;\n",
        "2. class weighting;\n",
        "3. random oversampling within the training pipeline.\n",
        "\n",
        "Strategy selection uses validation PR-AUC, while default-class recall and F1 remain visible in the comparison."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "437ce4f7",
      "metadata": {
        "id": "437ce4f7"
      },
      "outputs": [],
      "source": [
        "IMBALANCE_STRATEGIES = [\n",
        "    \"No balancing\",\n",
        "    \"Class weighting\",\n",
        "    \"Random oversampling\",\n",
        "]\n",
        "\n",
        "\n",
        "def make_model_pipeline(model_name, strategy, model_parameters=None):\n",
        "    model_parameters = model_parameters or {}\n",
        "\n",
        "    if model_name == \"Logistic Regression\":\n",
        "        class_weight = (\n",
        "            \"balanced\" if strategy == \"Class weighting\" else None\n",
        "        )\n",
        "        classifier = LogisticRegression(\n",
        "            C=1.0,\n",
        "            penalty=\"l2\",\n",
        "            solver=\"liblinear\",\n",
        "            class_weight=class_weight,\n",
        "            max_iter=3000,\n",
        "            random_state=RANDOM_STATE,\n",
        "            **model_parameters,\n",
        "        )\n",
        "        preprocessor = make_preprocessor(scale_numeric=True)\n",
        "\n",
        "    elif model_name == \"Random Forest\":\n",
        "        class_weight = (\n",
        "            \"balanced_subsample\"\n",
        "            if strategy == \"Class weighting\"\n",
        "            else None\n",
        "        )\n",
        "        classifier = RandomForestClassifier(\n",
        "            n_estimators=100,\n",
        "            max_depth=10,\n",
        "            min_samples_leaf=2,\n",
        "            max_features=\"sqrt\",\n",
        "            class_weight=class_weight,\n",
        "            random_state=RANDOM_STATE,\n",
        "            n_jobs=1,\n",
        "            **model_parameters,\n",
        "        )\n",
        "        preprocessor = make_preprocessor(scale_numeric=False)\n",
        "\n",
        "    else:\n",
        "        raise ValueError(f\"Unsupported model: {model_name}\")\n",
        "\n",
        "    sampler = (\n",
        "        RandomOverSampler(random_state=RANDOM_STATE)\n",
        "        if strategy == \"Random oversampling\"\n",
        "        else \"passthrough\"\n",
        "    )\n",
        "\n",
        "    return ImbPipeline(\n",
        "        steps=[\n",
        "            (\"preprocessor\", preprocessor),\n",
        "            (\"sampler\", sampler),\n",
        "            (\"classifier\", classifier),\n",
        "        ]\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "3218ea27",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "3218ea27",
        "outputId": "1b4ce673-4ad6-454d-9b9d-8bff57e4f529"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Model   Imbalance Strategy  Accuracy  Balanced Accuracy  \\\n",
              "0  Logistic Regression         No balancing    0.9890             0.5000   \n",
              "1  Logistic Regression      Class weighting    0.7880             0.6456   \n",
              "2  Logistic Regression  Random oversampling    0.7890             0.6461   \n",
              "3        Random Forest         No balancing    0.9890             0.5000   \n",
              "4        Random Forest      Class weighting    0.9890             0.5000   \n",
              "5        Random Forest  Random oversampling    0.9865             0.5212   \n",
              "\n",
              "   Precision (Default)  Recall (Default)  F1 (Default)  F2 (Default)  ROC-AUC  \\\n",
              "0               0.0000            0.0000        0.0000        0.0000   0.6965   \n",
              "1               0.0259            0.5000        0.0493        0.1074   0.6534   \n",
              "2               0.0261            0.5000        0.0495        0.1078   0.6445   \n",
              "3               0.0000            0.0000        0.0000        0.0000   0.6604   \n",
              "4               0.0000            0.0000        0.0000        0.0000   0.6665   \n",
              "5               0.1429            0.0455        0.0690        0.0526   0.6274   \n",
              "\n",
              "   PR-AUC  \n",
              "0  0.0316  \n",
              "1  0.0245  \n",
              "2  0.0241  \n",
              "3  0.0676  \n",
              "4  0.0563  \n",
              "5  0.0319  "
            ],
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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>Imbalance Strategy</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>Precision (Default)</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>No balancing</td>\n",
              "      <td>0.9890</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
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              "      <td>0.6965</td>\n",
              "      <td>0.0316</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>Class weighting</td>\n",
              "      <td>0.7880</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0259</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0493</td>\n",
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              "      <td>0.6534</td>\n",
              "      <td>0.0245</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>Random oversampling</td>\n",
              "      <td>0.7890</td>\n",
              "      <td>0.6461</td>\n",
              "      <td>0.0261</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.1078</td>\n",
              "      <td>0.6445</td>\n",
              "      <td>0.0241</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>No balancing</td>\n",
              "      <td>0.9890</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.6604</td>\n",
              "      <td>0.0676</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>Class weighting</td>\n",
              "      <td>0.9890</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.6665</td>\n",
              "      <td>0.0563</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>Random oversampling</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>0.5212</td>\n",
              "      <td>0.1429</td>\n",
              "      <td>0.0455</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>0.0526</td>\n",
              "      <td>0.6274</td>\n",
              "      <td>0.0319</td>\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",
              "      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-9d7db6bf-0e71-4bc0-b8b0-fb523b948f52 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-9d7db6bf-0e71-4bc0-b8b0-fb523b948f52');\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\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Random Forest\",\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Imbalance Strategy\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No balancing\",\n          \"Class weighting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10322003197054337,\n        \"min\": 0.788,\n        \"max\": 0.989,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.788,\n          0.9865\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balanced Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07304282990136676,\n        \"min\": 0.5,\n        \"max\": 0.6461,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6456,\n          0.5212\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05557234624043389,\n        \"min\": 0.0,\n        \"max\": 0.1429,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0259,\n          0.1429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2529394690434848,\n        \"min\": 0.0,\n        \"max\": 0.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.031460875173247596,\n        \"min\": 0.0,\n        \"max\": 0.069,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0493,\n          0.069\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F2 (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.052857456112327866,\n        \"min\": 0.0,\n        \"max\": 0.1078,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.1074,\n          0.0526\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.023237677738248017,\n        \"min\": 0.6274,\n        \"max\": 0.6965,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6965,\n          0.6534\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PR-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018187981379654713,\n        \"min\": 0.0241,\n        \"max\": 0.0676,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0316,\n          0.0245\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "benchmark_rows = []\n",
        "benchmark_models = {}\n",
        "\n",
        "for model_name in [\"Logistic Regression\", \"Random Forest\"]:\n",
        "    for strategy in IMBALANCE_STRATEGIES:\n",
        "        pipeline = make_model_pipeline(model_name, strategy)\n",
        "        pipeline.fit(X_train, y_train)\n",
        "\n",
        "        valid_probabilities = pipeline.predict_proba(X_valid)[:, 1]\n",
        "        row = metric_row(\n",
        "            y_valid,\n",
        "            valid_probabilities,\n",
        "            threshold=0.50,\n",
        "        )\n",
        "        row.update(\n",
        "            {\n",
        "                \"Model\": model_name,\n",
        "                \"Imbalance Strategy\": strategy,\n",
        "            }\n",
        "        )\n",
        "\n",
        "        benchmark_rows.append(row)\n",
        "        benchmark_models[(model_name, strategy)] = pipeline\n",
        "\n",
        "imbalance_benchmark = pd.DataFrame(benchmark_rows)\n",
        "benchmark_order = [\n",
        "    \"Model\",\n",
        "    \"Imbalance Strategy\",\n",
        "    \"Accuracy\",\n",
        "    \"Balanced Accuracy\",\n",
        "    \"Precision (Default)\",\n",
        "    \"Recall (Default)\",\n",
        "    \"F1 (Default)\",\n",
        "    \"F2 (Default)\",\n",
        "    \"ROC-AUC\",\n",
        "    \"PR-AUC\",\n",
        "]\n",
        "\n",
        "imbalance_benchmark = imbalance_benchmark[benchmark_order]\n",
        "display(\n",
        "    imbalance_benchmark.sort_values(\n",
        "        [\"Model\", \"PR-AUC\"],\n",
        "        ascending=[True, False],\n",
        "    ).round(4)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "abd4cd07",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abd4cd07",
        "outputId": "5bf480cb-504c-4a69-8cbc-da14a653407f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected imbalance strategies based on validation PR-AUC:\n",
            "- Logistic Regression: No balancing\n",
            "- Random Forest: No balancing\n"
          ]
        }
      ],
      "source": [
        "selected_strategies = {}\n",
        "\n",
        "for model_name in [\"Logistic Regression\", \"Random Forest\"]:\n",
        "    model_rows = imbalance_benchmark[\n",
        "        imbalance_benchmark[\"Model\"] == model_name\n",
        "    ]\n",
        "    best_row = model_rows.loc[model_rows[\"PR-AUC\"].idxmax()]\n",
        "    selected_strategies[model_name] = best_row[\n",
        "        \"Imbalance Strategy\"\n",
        "    ]\n",
        "\n",
        "print(\"Selected imbalance strategies based on validation PR-AUC:\")\n",
        "for model_name, strategy in selected_strategies.items():\n",
        "    print(f\"- {model_name}: {strategy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f56d1481",
      "metadata": {
        "id": "f56d1481"
      },
      "source": [
        "## 9. Hyperparameter optimisation with stratified five-fold cross-validation\n",
        "\n",
        "Hyperparameters are selected using cross-validated PR-AUC. The search is intentionally compact enough to run on ordinary hardware while still evaluating materially different model configurations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "8c394b6f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8c394b6f",
        "outputId": "1dd8e535-495b-4545-fbb2-78d9c4657c87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Logistic Regression parameters:\n",
            "{'classifier__penalty': 'l2', 'classifier__C': 0.1}\n",
            "Best mean CV PR-AUC: 0.05247\n"
          ]
        }
      ],
      "source": [
        "cv_strategy = StratifiedKFold(\n",
        "    n_splits=CV_FOLDS,\n",
        "    shuffle=True,\n",
        "    random_state=RANDOM_STATE,\n",
        ")\n",
        "\n",
        "multi_metric_scoring = {\n",
        "    \"average_precision\": \"average_precision\",\n",
        "    \"roc_auc\": \"roc_auc\",\n",
        "    \"f1\": \"f1\",\n",
        "    \"recall\": \"recall\",\n",
        "}\n",
        "\n",
        "logistic_pipeline = make_model_pipeline(\n",
        "    \"Logistic Regression\",\n",
        "    selected_strategies[\"Logistic Regression\"],\n",
        ")\n",
        "\n",
        "logistic_parameter_space = {\n",
        "    \"classifier__C\": [0.01, 0.1, 1.0, 10.0, 100.0],\n",
        "    \"classifier__penalty\": [\"l1\", \"l2\"],\n",
        "}\n",
        "\n",
        "logistic_search = RandomizedSearchCV(\n",
        "    estimator=logistic_pipeline,\n",
        "    param_distributions=logistic_parameter_space,\n",
        "    n_iter=10,\n",
        "    scoring=multi_metric_scoring,\n",
        "    refit=\"average_precision\",\n",
        "    cv=cv_strategy,\n",
        "    random_state=RANDOM_STATE,\n",
        "    n_jobs=N_JOBS,\n",
        "    return_train_score=True,\n",
        "    error_score=\"raise\",\n",
        ")\n",
        "\n",
        "logistic_search.fit(X_train, y_train)\n",
        "\n",
        "best_logistic = logistic_search.best_estimator_\n",
        "\n",
        "print(\"Best Logistic Regression parameters:\")\n",
        "print(logistic_search.best_params_)\n",
        "print(\n",
        "    \"Best mean CV PR-AUC:\",\n",
        "    round(logistic_search.best_score_, 6),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "acca9c1f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acca9c1f",
        "outputId": "cbd2a193-19f3-4c7a-cc07-924ad658b57a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Random Forest parameters:\n",
            "{'classifier__n_estimators': 150, 'classifier__min_samples_split': 2, 'classifier__min_samples_leaf': 10, 'classifier__max_features': 'sqrt', 'classifier__max_depth': 14}\n",
            "Best mean CV PR-AUC: 0.047342\n"
          ]
        }
      ],
      "source": [
        "random_forest_pipeline = make_model_pipeline(\n",
        "    \"Random Forest\",\n",
        "    selected_strategies[\"Random Forest\"],\n",
        ")\n",
        "\n",
        "random_forest_parameter_space = {\n",
        "    \"classifier__n_estimators\": [100, 150, 200],\n",
        "    \"classifier__max_depth\": [6, 10, 14, None],\n",
        "    \"classifier__min_samples_split\": [2, 5, 10],\n",
        "    \"classifier__min_samples_leaf\": [1, 2, 5, 10],\n",
        "    \"classifier__max_features\": [\"sqrt\", 0.5],\n",
        "}\n",
        "\n",
        "random_forest_search = RandomizedSearchCV(\n",
        "    estimator=random_forest_pipeline,\n",
        "    param_distributions=random_forest_parameter_space,\n",
        "    n_iter=10,\n",
        "    scoring=multi_metric_scoring,\n",
        "    refit=\"average_precision\",\n",
        "    cv=cv_strategy,\n",
        "    random_state=RANDOM_STATE,\n",
        "    n_jobs=N_JOBS,\n",
        "    return_train_score=True,\n",
        "    error_score=\"raise\",\n",
        ")\n",
        "\n",
        "random_forest_search.fit(X_train, y_train)\n",
        "\n",
        "best_random_forest = random_forest_search.best_estimator_\n",
        "\n",
        "print(\"Best Random Forest parameters:\")\n",
        "print(random_forest_search.best_params_)\n",
        "print(\n",
        "    \"Best mean CV PR-AUC:\",\n",
        "    round(random_forest_search.best_score_, 6),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "ceaf3d4f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "ceaf3d4f",
        "outputId": "f74d9eac-a57a-4d97-9345-8ebe55baff6b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Model  Mean train PR-AUC  Mean validation PR-AUC  \\\n",
              "0  Logistic Regression             0.1317                  0.0525   \n",
              "1        Random Forest             0.8533                  0.0473   \n",
              "\n",
              "   Mean validation ROC-AUC  Mean validation recall  Mean validation F1  \\\n",
              "0                   0.6696                     0.0                 0.0   \n",
              "1                   0.6701                     0.0                 0.0   \n",
              "\n",
              "   Train-validation PR-AUC gap  \n",
              "0                       0.0792  \n",
              "1                       0.8060  "
            ],
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              "\n",
              "  <div id=\"df-7495d9c3-4840-4f27-82c9-67666fe6781e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .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>Model</th>\n",
              "      <th>Mean train PR-AUC</th>\n",
              "      <th>Mean validation PR-AUC</th>\n",
              "      <th>Mean validation ROC-AUC</th>\n",
              "      <th>Mean validation recall</th>\n",
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              "      <th>Train-validation PR-AUC gap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.1317</td>\n",
              "      <td>0.0525</td>\n",
              "      <td>0.6696</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.8533</td>\n",
              "      <td>0.0473</td>\n",
              "      <td>0.6701</td>\n",
              "      <td>0.0</td>\n",
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              "        const element = document.querySelector('#df-7495d9c3-4840-4f27-82c9-67666fe6781e');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(cv_comparison\",\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\": \"Mean train PR-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5102482533042126,\n        \"min\": 0.1317,\n        \"max\": 0.8533,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8533,\n          0.1317\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean validation PR-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0036769552621700443,\n        \"min\": 0.0473,\n        \"max\": 0.0525,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0473,\n          0.0525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean validation ROC-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00035355339059331333,\n        \"min\": 0.6696,\n        \"max\": 0.6701,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.6701,\n          0.6696\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean validation recall\",\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\": \"Mean validation F1\",\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\": \"Train-validation PR-AUC gap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5139252085663828,\n        \"min\": 0.0792,\n        \"max\": 0.806,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def cross_validation_summary(search, model_name):\n",
        "    best_index = search.best_index_\n",
        "    results = search.cv_results_\n",
        "\n",
        "    return {\n",
        "        \"Model\": model_name,\n",
        "        \"Mean train PR-AUC\": results[\n",
        "            \"mean_train_average_precision\"\n",
        "        ][best_index],\n",
        "        \"Mean validation PR-AUC\": results[\n",
        "            \"mean_test_average_precision\"\n",
        "        ][best_index],\n",
        "        \"Mean validation ROC-AUC\": results[\n",
        "            \"mean_test_roc_auc\"\n",
        "        ][best_index],\n",
        "        \"Mean validation recall\": results[\n",
        "            \"mean_test_recall\"\n",
        "        ][best_index],\n",
        "        \"Mean validation F1\": results[\n",
        "            \"mean_test_f1\"\n",
        "        ][best_index],\n",
        "    }\n",
        "\n",
        "cv_comparison = pd.DataFrame(\n",
        "    [\n",
        "        cross_validation_summary(\n",
        "            logistic_search,\n",
        "            \"Logistic Regression\",\n",
        "        ),\n",
        "        cross_validation_summary(\n",
        "            random_forest_search,\n",
        "            \"Random Forest\",\n",
        "        ),\n",
        "    ]\n",
        ")\n",
        "\n",
        "cv_comparison[\"Train-validation PR-AUC gap\"] = (\n",
        "    cv_comparison[\"Mean train PR-AUC\"]\n",
        "    - cv_comparison[\"Mean validation PR-AUC\"]\n",
        ")\n",
        "\n",
        "display(cv_comparison.round(4))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "44fcd93a",
      "metadata": {
        "id": "44fcd93a"
      },
      "source": [
        "## 10. Threshold tuning on the validation set\n",
        "\n",
        "The conventional threshold of `0.50` is not assumed to be optimal. A threshold that maximises validation F2 is selected for each model. The final test set is not used during threshold selection."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "488d6806",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "488d6806",
        "outputId": "6219f5db-3834-4a19-c6df-0b48c03a8f42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression selected threshold: 0.015\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest selected threshold: 0.025\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Model       Threshold Type  Threshold  Precision (Default)  \\\n",
              "0  Logistic Regression    Conventional 0.50      0.500               0.0000   \n",
              "1  Logistic Regression  Validation-tuned F2      0.015               0.0307   \n",
              "2        Random Forest    Conventional 0.50      0.500               0.0000   \n",
              "3        Random Forest  Validation-tuned F2      0.025               0.0345   \n",
              "\n",
              "   Recall (Default)  F1 (Default)  F2 (Default)  PR-AUC  ROC-AUC  \n",
              "0            0.0000        0.0000        0.0000  0.0501   0.7401  \n",
              "1            0.5909        0.0583        0.1270  0.0501   0.7401  \n",
              "2            0.0000        0.0000        0.0000  0.0481   0.6572  \n",
              "3            0.3182        0.0622        0.1203  0.0481   0.6572  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12f59951-45b1-482e-8935-f64bd4724203\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\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>Model</th>\n",
              "      <th>Threshold Type</th>\n",
              "      <th>Threshold</th>\n",
              "      <th>Precision (Default)</th>\n",
              "      <th>Recall (Default)</th>\n",
              "      <th>F1 (Default)</th>\n",
              "      <th>F2 (Default)</th>\n",
              "      <th>PR-AUC</th>\n",
              "      <th>ROC-AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>Conventional 0.50</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0501</td>\n",
              "      <td>0.7401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>Validation-tuned F2</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.0307</td>\n",
              "      <td>0.5909</td>\n",
              "      <td>0.0583</td>\n",
              "      <td>0.1270</td>\n",
              "      <td>0.0501</td>\n",
              "      <td>0.7401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>Conventional 0.50</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0481</td>\n",
              "      <td>0.6572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>Validation-tuned F2</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.0345</td>\n",
              "      <td>0.3182</td>\n",
              "      <td>0.0622</td>\n",
              "      <td>0.1203</td>\n",
              "      <td>0.0481</td>\n",
              "      <td>0.6572</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-12f59951-45b1-482e-8935-f64bd4724203')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \")\",\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\": \"Threshold Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Validation-tuned F2\",\n          \"Conventional 0.50\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27715819790629803,\n        \"min\": 0.015,\n        \"max\": 0.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018885444130334876,\n        \"min\": 0.0,\n        \"max\": 0.0345,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.0307\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2850721239148671,\n        \"min\": 0.0,\n        \"max\": 0.5909,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.5909\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03482177240367488,\n        \"min\": 0.0,\n        \"max\": 0.0622,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.0583\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F2 (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07144174200003804,\n        \"min\": 0.0,\n        \"max\": 0.127,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PR-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0011547005383792527,\n        \"min\": 0.0481,\n        \"max\": 0.0501,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0481,\n          0.0501\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04786233731581996,\n        \"min\": 0.6572,\n        \"max\": 0.7401,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.6572,\n          0.7401\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "fitted_models = {\n",
        "    \"Logistic Regression\": best_logistic,\n",
        "    \"Random Forest\": best_random_forest,\n",
        "}\n",
        "\n",
        "selected_thresholds = {}\n",
        "validation_threshold_tables = {}\n",
        "validation_threshold_summary = []\n",
        "\n",
        "for model_name, model in fitted_models.items():\n",
        "    valid_probabilities = model.predict_proba(X_valid)[:, 1]\n",
        "\n",
        "    best_threshold, threshold_table = tune_threshold(\n",
        "        y_valid,\n",
        "        valid_probabilities,\n",
        "        beta=2,\n",
        "    )\n",
        "\n",
        "    selected_thresholds[model_name] = best_threshold\n",
        "    validation_threshold_tables[model_name] = threshold_table\n",
        "\n",
        "    conventional_row = metric_row(\n",
        "        y_valid,\n",
        "        valid_probabilities,\n",
        "        threshold=0.50,\n",
        "    )\n",
        "    conventional_row.update(\n",
        "        {\n",
        "            \"Model\": model_name,\n",
        "            \"Threshold Type\": \"Conventional 0.50\",\n",
        "        }\n",
        "    )\n",
        "\n",
        "    tuned_row = metric_row(\n",
        "        y_valid,\n",
        "        valid_probabilities,\n",
        "        threshold=best_threshold,\n",
        "    )\n",
        "    tuned_row.update(\n",
        "        {\n",
        "            \"Model\": model_name,\n",
        "            \"Threshold Type\": \"Validation-tuned F2\",\n",
        "        }\n",
        "    )\n",
        "\n",
        "    validation_threshold_summary.extend(\n",
        "        [conventional_row, tuned_row]\n",
        "    )\n",
        "\n",
        "    print(\n",
        "        f\"{model_name} selected threshold: \"\n",
        "        f\"{best_threshold:.3f}\"\n",
        "    )\n",
        "    plot_threshold_diagnostics(\n",
        "        threshold_table,\n",
        "        f\"{model_name}: Validation Threshold Diagnostics\",\n",
        "    )\n",
        "\n",
        "validation_threshold_results = pd.DataFrame(\n",
        "    validation_threshold_summary\n",
        ")\n",
        "\n",
        "display(\n",
        "    validation_threshold_results[\n",
        "        [\n",
        "            \"Model\",\n",
        "            \"Threshold Type\",\n",
        "            \"Threshold\",\n",
        "            \"Precision (Default)\",\n",
        "            \"Recall (Default)\",\n",
        "            \"F1 (Default)\",\n",
        "            \"F2 (Default)\",\n",
        "            \"PR-AUC\",\n",
        "            \"ROC-AUC\",\n",
        "        ]\n",
        "    ].round(4)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "761ecba5",
      "metadata": {
        "id": "761ecba5"
      },
      "source": [
        "## 11. Final evaluation on the untouched test set\n",
        "\n",
        "Results are reported at both the conventional `0.50` threshold and the validation-tuned threshold. Model conclusions must distinguish threshold-independent discrimination from threshold-dependent classification behaviour."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "831c5f52",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "831c5f52",
        "outputId": "0a29c483-f097-4067-a207-49d592215a82"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 Model       Threshold Type  Threshold  Accuracy  \\\n",
              "0  Logistic Regression    Conventional 0.50      0.500    0.9890   \n",
              "1  Logistic Regression  Validation-tuned F2      0.015    0.8055   \n",
              "2        Random Forest    Conventional 0.50      0.500    0.9890   \n",
              "3        Random Forest  Validation-tuned F2      0.025    0.9015   \n",
              "\n",
              "   Balanced Accuracy  Precision (Default)  Recall (Default)  F1 (Default)  \\\n",
              "0             0.5000               0.0000            0.0000        0.0000   \n",
              "1             0.6095               0.0234            0.4091        0.0442   \n",
              "2             0.5000               0.0000            0.0000        0.0000   \n",
              "3             0.5681               0.0270            0.2273        0.0483   \n",
              "\n",
              "   F2 (Default)  ROC-AUC  PR-AUC  \n",
              "0        0.0000   0.7489  0.0772  \n",
              "1        0.0951   0.7489  0.0772  \n",
              "2        0.0000   0.7178  0.0369  \n",
              "3        0.0916   0.7178  0.0369  "
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Threshold Type</th>\n",
              "      <th>Threshold</th>\n",
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              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>Conventional 0.50</td>\n",
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              "      <td>Logistic Regression</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>Validation-tuned F2</td>\n",
              "      <td>0.025</td>\n",
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              "      <td>0.2273</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(final_test_results[final_columns]\",\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\": \"Threshold Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Validation-tuned F2\",\n          \"Conventional 0.50\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Threshold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27715819790629803,\n        \"min\": 0.015,\n        \"max\": 0.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08749904761386454,\n        \"min\": 0.8055,\n        \"max\": 0.989,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.989,\n          0.8055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balanced Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05398277503055954,\n        \"min\": 0.5,\n        \"max\": 0.6095,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.6095\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014623269128344728,\n        \"min\": 0.0,\n        \"max\": 0.027,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.0234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19813872244801958,\n        \"min\": 0.0,\n        \"max\": 0.4091,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.4091\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026754859371710404,\n        \"min\": 0.0,\n        \"max\": 0.0483,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.0442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F2 (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.053914585225150344,\n        \"min\": 0.0,\n        \"max\": 0.0951,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.0951\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01795559337179737,\n        \"min\": 0.7178,\n        \"max\": 0.7489,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.7178,\n          0.7489\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PR-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02326721584834192,\n        \"min\": 0.0369,\n        \"max\": 0.0772,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0369,\n          0.0772\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_probability_store = {}\n",
        "test_prediction_store = {}\n",
        "test_result_rows = []\n",
        "\n",
        "for model_name, model in fitted_models.items():\n",
        "    test_probabilities = model.predict_proba(X_test)[:, 1]\n",
        "    test_probability_store[model_name] = test_probabilities\n",
        "\n",
        "    for threshold_type, threshold in [\n",
        "        (\"Conventional 0.50\", 0.50),\n",
        "        (\n",
        "            \"Validation-tuned F2\",\n",
        "            selected_thresholds[model_name],\n",
        "        ),\n",
        "    ]:\n",
        "        row = metric_row(\n",
        "            y_test,\n",
        "            test_probabilities,\n",
        "            threshold=threshold,\n",
        "        )\n",
        "        row.update(\n",
        "            {\n",
        "                \"Model\": model_name,\n",
        "                \"Threshold Type\": threshold_type,\n",
        "            }\n",
        "        )\n",
        "        test_result_rows.append(row)\n",
        "\n",
        "    tuned_predictions = (\n",
        "        test_probabilities\n",
        "        >= selected_thresholds[model_name]\n",
        "    ).astype(int)\n",
        "    test_prediction_store[model_name] = tuned_predictions\n",
        "\n",
        "final_test_results = pd.DataFrame(test_result_rows)\n",
        "\n",
        "final_columns = [\n",
        "    \"Model\",\n",
        "    \"Threshold Type\",\n",
        "    \"Threshold\",\n",
        "    \"Accuracy\",\n",
        "    \"Balanced Accuracy\",\n",
        "    \"Precision (Default)\",\n",
        "    \"Recall (Default)\",\n",
        "    \"F1 (Default)\",\n",
        "    \"F2 (Default)\",\n",
        "    \"ROC-AUC\",\n",
        "    \"PR-AUC\",\n",
        "]\n",
        "\n",
        "display(final_test_results[final_columns].round(4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "5ceead11",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5ceead11",
        "outputId": "b7553a21-a876-4277-ad81-1220bb130fce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "Logistic Regression\n",
            "Tuned threshold: 0.015\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " Non-default     0.9920    0.8099    0.8917      1978\n",
            "     Default     0.0234    0.4091    0.0442        22\n",
            "\n",
            "    accuracy                         0.8055      2000\n",
            "   macro avg     0.5077    0.6095    0.4680      2000\n",
            "weighted avg     0.9813    0.8055    0.8824      2000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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3L44dOyZ3S+/duxc+Pj6oX7++XM7Z2Rn9+vV74XGcnJxw9+5dndSJMbZs2YJ69erlGdkX5Fbiffv2wcXFBS4uLqhTpw6+//57DBw4EAsWLJDL/Prrr3B0dET79u3xzz//yEvDhg1hb2+PgwcPAgD279+PzMxMnS5mILsnKD9Dhw7V+cs4NDQUCQkJePfdd3WOZW5ujiZNmsjHsrGxgUqlwqFDh3KlOHPkDBbetWtXgf7yAwy7DnIMGzZM51y3bNkSWVlZuH37trxuwIABEEIY1cslhMCWLVvQpUsXCCF0zo+fnx8SExPltIfS11nOey5ILxeQPVhWo9Hg3XfflddZWlpi9OjRSE5OxuHDh3XKv/POOzq9qy1btgQA/PXXX8Y2XfYy5+T3339Heno6xo4dq3MDy9ChQ6FWq/G///1Pp7y9vT3ee+89+bVKpcLrr79u0PswNzdH79695Tu/Nm7cCA8PD/mcPO/ZVE1qair++ecfNG3aFADyTIPlp3Xr1qhZs2aBygYFBaFWrVoICAjAiBEj0Lp1a4wePfqF++WkPJ/vSTeUIec5ICBA5xyFh4fjxo0b6Nu3Lx4+fCh/hlJSUtCuXTscOXIEWq0WQPY1c/r0ady7d8/o9hj6mciRlZWF3377Dd26dUOFChXk9d7e3nmmmxctWoTQ0NAXLp988om8T06qOK9sjbW1tU4qOS9Pnz7Nd99n6zfU8ePHMWbMGLz11lv48MMPERYWhtq1a+PTTz/Ns85SpUq91LAaU2HQ9YwHDx7gyZMnqF69eq5t3t7e0Gq1uHPnDgDg9u3bOnfb5Mhr3fMmTpwIe3t7vP7666hatSpGjhyZaxyBIW7evGnUHDFNmjRBaGgo9u7di4ULF8LJyQmPHj2CSqWSy9y4cQOJiYlwdXWVA7ScJTk5GXFxcQAgBxnPnwdnZ+d8v2S9vLx0Xt+4cQMA8MYbb+Q61r59++RjWVlZ4YsvvsCePXvg5uaGVq1aYf78+YiNjZXrat26NXr27IkZM2agTJky6Nq1K9avX4+0tLR8z4ch10GOZ78IgX9/UPILBl/WgwcPkJCQgDVr1uQ6NwMHDgQA+fwofZ2p1WoA2WNcCuL27duoWrVqrjttc9IPzwakgGnO4cuck5x2Pn89qFQqVKpUKdf7KF++fK4/dkqVKmXw++jbty+uXr2KixcvYtOmTejTp0++f0TFx8djzJgx8lgZFxcX+XNlyBjC5z+L+qhUKqxbtw63bt3C48eP5fGsBSWEKHDZvBhynvP7jgkICMj1OVq7di3S0tLk8zZ//nz88ccf8PDwwOuvv46goKA8A7uCtMfQz0SOBw8e4OnTp6hatWqubXl9TzVs2BC+vr4vXJ4NsHOC0ry+G1NTU184BsvGxibffZ+t31gqlQqBgYFISEhAWFhYru1CiGI9bxnHdBUBb29vREZGYteuXdi7dy+2bNmClStXYtq0aZgxY4bJ21OmTBl5UKufnx9q1KiBN998E0uWLMFHH30EIHtwvKurKzZu3JhnHfmNAyqI5z+MOX9hfv/997lumQegM8hz7Nix6NKlC0JCQvDbb79h6tSpmDt3Lg4cOIDXXnsNkiRh8+bNOHXqFHbu3InffvsNgwYNwqJFi3Dq1CnFpl7IbwyLsT8sz8s5N++99548qPx5devWBaD8dVajRg0A2WNLCoMx5zC/L9msrCydek3x2VPqWmjSpAkqV66MsWPH4tatW3rHnPXu3RsnTpzAhAkTUL9+fdjb20Or1cLf31++ZgrC0B/G3377DUD2D+uNGzcKFLSVLl0aQHYw/eyNM4Yy5Dzn9x2zYMECnWzFs3K+G3r37o2WLVti27Zt2LdvHxYsWIAvvvgCW7du1Rk7ZqrvgIKIj49Henr6C8vZ2NjA0dERQHZPPpA9bZCHh4dOuZiYGHlsVn7Kli2LgwcP5gp6YmJiAGSPDVNKTvvi4+NzbXv06FGewWlxwaDrGS4uLrC1tUVkZGSubdeuXYOZmZn8j+3p6Yk///wzV7m81uXFzs4O77zzDt555x2kp6ejR48e+PzzzzF58mR5pu2Cqly5Mv74448Cl3+Rzp07o3Xr1pgzZw4++OAD2NnZoXLlyvj999/RvHlzvV/Mnp6eALLPw7NfwA8fPizwX/o5A4ddXV0LdIdT5cqV8fHHH+Pjjz/GjRs3UL9+fSxatAg//PCDXKZp06Zo2rQpPv/8c2zatAn9+vXDTz/9hCFDhuSqz5DrwNRcXFzg4OCArKysAp0bJa+zatWqoXr16ti+fTuWLFnywoDV09MTly5dglar1fnL/tq1a/J2pZQqVSrPCT9v376tkyIGXnxO8nofQPb8eM/WlZ6ejlu3bhXafGEA8O6772L27Nnw9vbONzh49OgR9u/fjxkzZmDatGny+pzenGcp2QNw6dIlzJw5EwMHDkR4eDiGDBmCy5cvyz/i+ckJ3m/duoU6deoo1h5D5HzHqNXqAv37lS1bFiNGjMCIESMQFxeHBg0a4PPPPy/wjRE5XvYz4eLiAhsbmzz/TfP6nurRo0e+qcpnBQQEIDg4GADk6+vcuXM6Ada9e/dw9+5dnXkC81K/fn2sXbsWEREROj1op0+f1qlfCTk9jc//sZ+ZmYk7d+7grbfeUuxYSmN68Rnm5ubo0KEDtm/frnO7+P3797Fp0ya0aNFCTrH4+fnh5MmTCA8Pl8vFx8fn2xP0rGdv4wayu0tr1qwJIYQ87ihnlu2CzBzds2dPXLx4Edu2bcu17WX/ypo4cSIePnyIb775BkD2X3tZWVmYNWtWrrKZmZlyO9u1awcLC4tcE+ktX768wMf28/ODWq3GnDlz8hyHlTMVw5MnT3LdEVm5cmU4ODjI3dyPHj3KdQ5yPvz5pRgNuQ4MocSUEebm5ujZsye2bNmSZ6D97DQVSl9nADBjxgw8fPgQQ4YMyXOW6n379mHXrl0AgE6dOiE2NlbnDs7MzEwsW7YM9vb2aN26dYGOWRCVK1fGqVOndP6637VrV640cEHOyfN8fX2hUqmwdOlSnWvp22+/RWJiIjp37qzY+3jekCFDMH36dCxatCjfMjk9LM9f54sXL85V1tB/7/xkZGRgwIABcHd3x5IlSxAcHIz79+9j3LhxL9y3YcOGUKlURfpkkIYNG6Jy5cpYuHBhno8hyvkcZWVl5fq8urq6wt3dXe8Qhfy87GfC3Nwcfn5+CAkJQXR0tLw+IiJC7m181suM6apVqxZq1KiBNWvW6Ez5sWrVKkiShF69esnr8vou69q1KywtLbFy5Up5nRACq1evRrly5dCsWTMDzxZyTbsDZA9vWLx4McqUKYOGDRvqbLt69SpSU1Nf6lim8p/s6Vq3bp08/8yzxowZg9mzZyM0NBQtWrTAiBEjYGFhga+//hppaWk6c6588skn+OGHH9C+fXuMGjVKnjKiQoUKiI+P1/sXZYcOHaDRaNC8eXO4ubkhIiICy5cvR+fOneVByjkX05QpU9CnTx9YWlqiS5cueT7yZMKECdi8eTPefvttDBo0CA0bNkR8fDx27NiB1atXo169egafo44dO6J27dr48ssvMXLkSLRu3RoffPAB5s6di/DwcHTo0AGWlpa4ceMGfv31VyxZsgS9evWCm5sbxowZg0WLFuGtt96Cv78/Ll68iD179qBMmTIF+ktbrVZj1apV6N+/Pxo0aIA+ffrAxcUF0dHR+N///ofmzZtj+fLluH79Otq1a4fevXujZs2asLCwwLZt23D//n159uMNGzZg5cqV6N69OypXrozHjx/jm2++gVqtRqdOnfJtQ0GvA0MoNWXEvHnzcPDgQTRp0gRDhw5FzZo1ER8fj/Pnz+P333+Xu9yVvs6A7MHuly9fxueff44LFy7g3XfflWek37t3L/bv3y/PLzVs2DB8/fXXGDBgAMLCwlCxYkVs3rwZx48fx+LFiws8IL8ghgwZgs2bN8Pf3x+9e/fGzZs38cMPP+SaUqIg5+R5Li4umDx5MmbMmAF/f3+89dZbiIyMxMqVK9G4cWOdwdNK8/T0fOHz99RqtTyeMSMjA+XKlcO+fftw69atXGUN/ffOz+zZsxEeHo79+/fDwcEBdevWxbRp0/DZZ5+hV69eej9b1tbW6NChA37//XfMnDnToOMqxczMDGvXrkXHjh1Rq1YtDBw4EOXKlcPff/+NgwcPQq1WY+fOnXj8+DHKly+PXr16oV69erC3t8fvv/+Os2fP6g2E82PMZ2LGjBnYu3cvWrZsiREjRsjBWq1atXDp0iWdss8HIwW1YMECvPXWW+jQoQP69OmDP/74A8uXL8eQIUN0poLI67usfPnyGDt2LBYsWICMjAw0btwYISEhOHr0KDZu3KiTfr19+7b85I2c4Hv27NkAsq/5/v37AwBWrFiBkJAQdOnSBRUqVEBMTAzWrVuH6OhofP/99zrjjoHsm7BsbW3Rvn37l3r/JmHiuyWLVM6t1vktd+7cEUIIcf78eeHn5yfs7e2Fra2taNu2rThx4kSu+i5cuCBatmwprKysRPny5cXcuXPF0qVLBQARGxsrl3v+tvWvv/5atGrVSpQuXVpYWVmJypUriwkTJojExESd+mfNmiXKlSsnzMzMdG7zzuv2+IcPH4rAwEBRrlw5oVKpRPny5UVAQECuaQWel9eM9DmCg4NzTXGwZs0a0bBhQ2FjYyMcHBxEnTp1xCeffCLu3bsnl8nMzBRTp04VGo1G2NjYiDfeeENERESI0qVLiw8//DDXv0d+0zkcPHhQ+Pn5CUdHR2FtbS0qV64sBgwYIM6dOyeEEOKff/4RI0eOFDVq1BB2dnbC0dFRNGnSRPzyyy9yHefPnxfvvvuuqFChgrCyshKurq7izTfflOvIgTxuTy/IdZDfe8i5Vf3gwYO5yho7ZYQQQty/f1+MHDlSeHh4CEtLS6HRaES7du3EmjVr5DLGXmf67N+/X3Tt2lW4uroKCwsL4eLiIrp06SK2b9+eq50DBw4UZcqUESqVStSpUyfX+8+ZMiKvqT+e/3fRd80sWrRIlCtXTlhZWYnmzZuLc+fOvdRn7/kpI3IsX75c1KhRQ1haWgo3NzcxfPhw8ejRI50y+c1MHhAQIDw9PXOtf56+z6O+c3D37l3RvXt34eTkJBwdHcXbb78t7t27l+d1nd+/N/TM/P1sPWFhYcLCwkKMGjVKp0xmZqZo3LixcHd3z3Venrd161YhSVKe0xMI8fIz0j9/nnM+h7/++mue9Vy4cEH06NFDvh48PT1F7969xf79+4UQ2dM+TJgwQdSrV084ODgIOzs7Ua9evVzTFRjy716Qz4QQeX8nHT58WDRs2FCoVCpRqVIlsXr1akVnpBcie5qd+vXry79rn332Wa6nT+T3XZaVlSXmzJkjPD09hUqlErVq1dKZHiNHzr9LXsuzn9d9+/aJ9u3bC41GIywtLYWTk5Po0KGD/O/zvCZNmoj33nvP6HNQmCQhimCUXwk2duxYfP3110hOTtY7QeB/TUJCAkqVKoXZs2djypQpRd0cIipCWVlZqFmzJnr37p3nkAUiQ4WHh6NBgwY4f/68ouPHlMYxXUZ4fo6Qhw8f4vvvv0eLFi3+0wFXXnOn5Iwvef6RLET032Nubo6ZM2dixYoVeY6pIjLUvHnz0KtXr2IdcAEAe7qMUL9+fbRp0wbe3t64f/8+vv32W9y7dw/79+9Hq1atirp5RSY4OBjBwcHo1KkT7O3tcezYMfz444/o0KFDnoM+iYiI/gv+kwPpldKpUyds3rwZa9asgSRJaNCgAb799tv/dMAFZM8TZWFhgfnz5yMpKUkeXJ8zUJKIiOi/iOlFI8yZMwfXr1/HkydPkJKSgqNHjxbqnD2vigYNGuD333/HP//8g/T0dNy5cweLFy9WbCJSIiKiHEeOHEGXLl3g7u4OSZIQEhKSq0xERATeeustODo6ws7ODo0bN9aZfiM1NRUjR45E6dKlYW9vj549e+L+/fs6dURHR6Nz586wtbWFq6srJkyYkOfUOfow6CIiIqJXVkpKCurVq4cVK1bkuf3mzZto0aIFatSogUOHDuHSpUuYOnWqzmTI48aNw86dO/Hrr7/i8OHDuHfvHnr06CFvz8rKQufOnZGeno4TJ05gw4YNCA4O1pmQuCA4pouIiIhKBEmSsG3bNnTr1k1elzMnXc7cYM9LTEyEi4sLNm3aJE8Ce+3aNXh7e+PkyZNo2rQp9uzZgzfffBP37t2Dm5sbAGD16tWYOHEiHjx4kGvOsPxwTFcJp9Vqce/ePTg4OBTrh4ASEZGyhBB4/Pgx3N3dcz1kuzCkpqYW6JmPBaFSqfJ8LJehtFot/ve//+GTTz6Bn58fLly4AC8vL0yePFkOzMLCwpCRkaEzPKhGjRqoUKGCHHSdPHkSderUkQMuIPvpKcOHD8eVK1fw2muvFag9DLpKuHv37hXZcwKJiKjo3blzx6iHixdEamoqvDztERuX9eLCBaDRaHDx4kWdwMvKygpWVlYG1RMXF4fk5GTMmzcPs2fPxhdffIG9e/eiR48eOHjwIFq3bo3Y2FioVCo4OTnp7Ovm5obY2FgAQGxsrE7AlbM9Z1tBMegq4XIeK3H7fEWo7TmEj/57OgUGFHUTiIpEZmYqzh6Yq+gjt/KTnp6O2Lgs3A6rCLWDcb81SY+18GwYlSvImT59+gsfi/U8rVYLIPvZkDnPBq1fvz5OnDiB1atXK/oM2IJg0FXC5aQU1fZmRn8QiF5FFpbGpyiIXmWmHFpi7yDB3sG442mRvf+dO3egVqvl9Yb2cgFAmTJlYGFhgZo1a+qs9/b2xrFjxwBk96qlp6cjISFBp7fr/v370Gg0cpkzZ87o1JFzd2NOmYLgrzAREREpIktoFVmA7Ie5P7u8TNClUqnQuHFjREZG6qy/fv06PD09AWQ/INzS0hL79++Xt0dGRiI6Oho+Pj4AAB8fH1y+fBlxcXFymdDQUKjV6lwBnT7s6SIiIqJXVnJyMv7880/59a1btxAeHg5nZ2dUqFABEyZMwDvvvINWrVqhbdu22Lt3L3bu3IlDhw4BABwdHTF48GB89NFHcHZ2hlqtxqhRo+Dj44OmTZsCADp06ICaNWuif//+mD9/PmJjY/HZZ59h5MiRBgWDDLqIiIhIEVoIaGHcTFSG7n/u3Dm0bdtWfv3RRx8BAAICAhAcHIzu3btj9erVmDt3LkaPHo3q1atjy5YtaNGihbzPV199BTMzM/Ts2RNpaWnw8/PDypUr5e3m5ubYtWsXhg8fDh8fH9jZ2SEgIAAzZ840qK2cp6uES0pKgqOjIx5dr8QxXfSf1GbI0KJuAlGRyMxIxcl905GYmKgzNqow5PzW3Issr8hAevfqd03SblPjrzARERGRCTC9SERERIrIEgJZRibQjN2/OGPQRURERIooijFdrxKmF4mIiIhMgD1dREREpAgtBLLY05UvBl1ERESkCKYX9WN6kYiIiMgE2NNFREREiuDdi/ox6CIiIiJFaP9/MbaOkorpRSIiIiITYE8XERERKSJLgbsXjd2/OGPQRURERIrIEtmLsXWUVEwvEhEREZkAe7qIiIhIERxIrx+DLiIiIlKEFhKyIBldR0nF9CIRERGRCbCni4iIiBShFdmLsXWUVAy6iIiISBFZCqQXjd2/OGN6kYiIiMgE2NNFREREimBPl34MuoiIiEgRWiFBK4y8e9HI/YszpheJiIiITIA9XURERKQIphf1Y9BFREREisiCGbKMTKJlKdSW4ojpRSIiIiITYE8XERERKUIoMJBelOCB9Ay6iIiISBEc06Uf04tEREREJsCeLiIiIlJEljBDljByID2fvUhERESknxYStEYm0bQouVEX04tEREREJsCeLiIiIlIEB9Lrx6CLiIiIFKHMmC6mF4mIiIjICOzpIiIiIkVkD6Q3Lj1o7P7FGYMuIiIiUoRWgWcv8u5FIiIiIjIKe7qIiIhIERxIrx97uoiIiEgRWpgpshjiyJEj6NKlC9zd3SFJEkJCQvIt++GHH0KSJCxevFhnfXx8PPr16we1Wg0nJycMHjwYycnJOmUuXbqEli1bwtraGh4eHpg/f75B7QQYdBEREdErLCUlBfXq1cOKFSv0ltu2bRtOnToFd3f3XNv69euHK1euIDQ0FLt27cKRI0cwbNgweXtSUhI6dOgAT09PhIWFYcGCBQgKCsKaNWsMaivTi0RERKSILCEhSxg5OaqB+3fs2BEdO3bUW+bvv//GqFGj8Ntvv6Fz58462yIiIrB3716cPXsWjRo1AgAsW7YMnTp1wsKFC+Hu7o6NGzciPT0d69atg0qlQq1atRAeHo4vv/xSJzh7EfZ0ERERkSKy/v/uRWMXILt36dklLS3tpdqk1WrRv39/TJgwAbVq1cq1/eTJk3BycpIDLgDw9fWFmZkZTp8+LZdp1aoVVCqVXMbPzw+RkZF49OhRgdvCoIuIiIiKHQ8PDzg6OsrL3LlzX6qeL774AhYWFhg9enSe22NjY+Hq6qqzzsLCAs7OzoiNjZXLuLm56ZTJeZ1TpiCYXiQiIiJFaIUZtEbevaj9/7sX79y5A7VaLa+3srIyuK6wsDAsWbIE58+fhyQV/aSr7OkiIiIiRSiZXlSr1TrLywRdR48eRVxcHCpUqAALCwtYWFjg9u3b+Pjjj1GxYkUAgEajQVxcnM5+mZmZiI+Ph0ajkcvcv39fp0zO65wyBcGgi4iIiEqk/v3749KlSwgPD5cXd3d3TJgwAb/99hsAwMfHBwkJCQgLC5P3O3DgALRaLZo0aSKXOXLkCDIyMuQyoaGhqF69OkqVKlXg9jC9SERERIrQwvC7D/OqwxDJycn4888/5de3bt1CeHg4nJ2dUaFCBZQuXVqnvKWlJTQaDapXrw4A8Pb2hr+/P4YOHYrVq1cjIyMDgYGB6NOnjzy9RN++fTFjxgwMHjwYEydOxB9//IElS5bgq6++MqitDLqIiIhIES8zuWledRji3LlzaNu2rfz6o48+AgAEBAQgODi4QHVs3LgRgYGBaNeuHczMzNCzZ08sXbpU3u7o6Ih9+/Zh5MiRaNiwIcqUKYNp06YZNF0EwKCLiIiIXmFt2rSBMODRQVFRUbnWOTs7Y9OmTXr3q1u3Lo4ePWpo83Qw6CIiIiJFKPPsxZI73JxBFxERESlCCwlaGDumq+indigsJTecJCIiIipG2NNFREREimB6UT8GXURERKSIZyc3NaaOkqrkvjMiIiKiYoQ9XURERKQIrZCgNXZyVCP3L84YdBEREZEitAqkF42dXLU4K7nvjIiIiKgYYU8XERERKUIrzKA18u5DY/cvzhh0ERERkSKyICHLyMlNjd2/OCu54SQRERFRMcKeLiIiIlIE04v6MegiIiIiRWTB+PRgljJNKZZKbjhJREREVIywp4uIiIgUwfSifgy6iIiISBF84LV+JfedERERERUj7OkiIiIiRQhI0Bo5kF6U4Hm6GHQRERGRIphe1K/kvjMiIiKiYoQ9XURERKQIrZCgFcalB43dvzhj0EVERESKyIIZsoxMohm7f3FWct8ZERERUTHCni4iIiJSBNOL+jHoIiIiIkVoYQatkUk0Y/cvzkruOyMiIiIqRtjTRURERIrIEhKyjEwPGrt/ccagi4iIiBTBMV36Mb1IREREZALs6SIiIiJFCGEGrZGP8REl+DFADLqIiIhIEVmQkGXkA6uN3b84K7nhJBEREVExwp4uIiIiUoRWGD8QXisUakwxxKCL6AUun7LDrytdceOyLeLvW2L6t7fQrGOiTpnoG1b4drY7Lp2yR1Ym4FktDVO/uQXX8hkAgPRUCWtmuOPQjlLISJPQsM1jjJp7F6VcMgEAN69Y45flbvjjjB2SHlnArXw6Or//D7oP+cfk75dIn7faXEXXNhHQlE4GAETdK4UNO1/DmT88oCn9GD998XOe+01f9QYOh1WSX/s3u463O1yGh1sSUp5a4tA5LyzZ1Nwk74EKj1aBMV3G7l+cMeh6xqFDh9C2bVs8evQITk5OBdrnyZMn6N+/P0JDQ/H48WOD9tUnKCgIISEhCA8PN7ouMk7qEzNUqvUUfu/GY+Zgr1zb70Wp8FG3qvDv8xD9x8fC1iELtyOtobL+98+11UHlcOZ3NT77Ogp26iysmFIeMwdXxFc7/gQA/HnJFk5lMjFx+W24uGfg6jk7LJngATMzoOsgBl5UfDx4ZIc1W17H3ftqSBLg1+w6Pg8MxdCZ3REd44geH/XVKf9mq2vo438ZZ/7wkNe93f4yene4jNW/vo6IW66wtsqQgziikqxIg64BAwZgw4YNmDt3LiZNmiSvDwkJQffu3SFE8e9j3LBhA44ePYoTJ06gTJkycHR0LJTjDBgwAAkJCQgJCSmU+il/jd94jMZvPM53e/C8snj9jSQMmRojr3OvmC7/f0qSGX770RmTVtxG/RbZPywffRmNoa29ERFmC++GT+D3brxOnWU90xFxzhbH9zgy6KJi5eRFT53X325rjK5trqFmpThE3SuF+CRbne0tG9zGwbNeeJpmCQCwt03D4G7n8OmyDjh/rZxc7q+7pQu/8VTotJCgNXIgvLH7F2dF3odnbW2NL774Ao8ePSrqpryUmzdvwtvbG7Vr14ZGo4EkldyLhXLTaoEz+9UoVykNn75bCb3r1MLozlVxYs+/wfeNS7bIzDDDay3//Uu+QtU0uJZLR0SYXb51pzw2h4NTVqG2n8gYZpIWbzS+CWtVBq7cdM21vZrnP6ha4SF2H6sur2tU82+YmQFlSj3Bhlm/4tf5mzD9g/1wKcWerpIgZ0Z6YxdDHDlyBF26dIG7uzskSdLpnMjIyMDEiRNRp04d2NnZwd3dHe+//z7u3bunU0d8fDz69esHtVoNJycnDB48GMnJutfkpUuX0LJlS1hbW8PDwwPz5883+PwUedDl6+sLjUaDuXPn5ltmy5YtqFWrFqysrFCxYkUsWrRIZ3vFihUxZ84cDBo0CA4ODqhQoQLWrFnzwmPv3r0b1apVg42NDdq2bYuoqKhcZY4dO4aWLVvCxsYGHh4eGD16NFJSUgAAbdq0waJFi3DkyBFIkoQ2bdoAAL7//ns0atQIDg4O0Gg06Nu3L+Li4uQ6g4ODc6UgQ0JC8g3YgoKCsGHDBmzfvh2SJEGSJBw6dOiF748KX8I/FniaYo6fl7uiUdvHmPvjX2jun4iZQyri0snsgCo+zgKWKi3sHXUDKCeXDMTH5d3ZfOWsLQ7vKIVO/R4W+nsgMpRXuXjsWR6M0NXr8VH/45i6sj1ux5TKVa5Ti0hE3XPClZtu8jp3l8eQJIH3OoVj+U8+mL7aF2q7NCz6aA8szPlHBhkuJSUF9erVw4oVK3Jte/LkCc6fP4+pU6fi/Pnz2Lp1KyIjI/HWW2/plOvXrx+uXLmC0NBQ7Nq1C0eOHMGwYcPk7UlJSejQoQM8PT0RFhaGBQsWICgoqECxxrOKPOgyNzfHnDlzsGzZMty9ezfX9rCwMPTu3Rt9+vTB5cuXERQUhKlTpyI4OFin3KJFi9CoUSNcuHABI0aMwPDhwxEZGZnvce/cuYMePXqgS5cuCA8Px5AhQ3RSnEB2L5a/vz969uyJS5cu4eeff8axY8cQGBgIANi6dSuGDh0KHx8fxMTEYOvWrQCyI+tZs2bh4sWLCAkJQVRUFAYMGPDS52j8+PHo3bs3/P39ERMTg5iYGDRr1izPsmlpaUhKStJZqPAIbfZ/ffyS0GPYA1Su/RTvjIpDE98k/O+7Mi9VZ9Q1a8wYWAnvfRSLhm3yT2sSFZU7sY4YMrM7hs/piu2HvDF50GF4ltXNVqgsM+Hb5CZ2H6ums16SBCwttFj6ow/OXimPq3+5YuaatijnloTXasSAXm05A+mNXQzRsWNHzJ49G927d8+1zdHREaGhoejduzeqV6+Opk2bYvny5QgLC0N0dDQAICIiAnv37sXatWvRpEkTtGjRAsuWLcNPP/0k94ht3LgR6enpWLduHWrVqoU+ffpg9OjR+PLLLw1qa5EHXQDQvXt31K9fH9OnT8+17csvv0S7du0wdepUVKtWDQMGDEBgYCAWLFigU65Tp04YMWIEqlSpgokTJ6JMmTI4ePBgvsdctWoVKleujEWLFqF69ero169frsBo7ty56NevH8aOHYuqVauiWbNmWLp0Kb777jukpqbC2dkZtra2UKlU0Gg0cHZ2BgAMGjQIHTt2RKVKldC0aVMsXboUe/bsydVVWVD29vawsbGBlZUVNBoNNBoNVCpVnmXnzp0LR0dHefHw8MizHClD7ZwFcwsBz2qpOus9qqYi7u/sMSzOrpnISDdDcqK5TpmEB5Zwds3UWXf7uhUm9q6Mju/9g75j7xdu44leUmaWOf6Oc8T122XwzdbGuHnHGT19r+iUad3wFqxUmfjtRFWd9Q8Ts8d8Pdszlphsg8RkK7g6M8X4qtNCkp+/+NLL/4/per4DIS0tTZE2JiYmQpIkOeN08uRJODk5oVGjRnIZX19fmJmZ4fTp03KZVq1a6fz2+vn5ITIy0qDhUcUi6AKAL774Ahs2bEBERITO+oiICDRvrnsbcfPmzXHjxg1kZf3bFV23bl35/yVJgkajkVN6HTt2hL29Pezt7VGrVi253iZNmujU6+Pjo/P64sWLCA4Olve1t7eHn58ftFotbt26le97CQsLQ5cuXVChQgU4ODigdevWACBH1YVp8uTJSExMlJc7d+4U+jH/yyxVAtXqPcHdm1Y66//+y0qeLqJq3SewsNTiwjF7efudP60Q97cK3g1T5HVRkdb4pFcVtH87HgMnxZrmDRApQJIEVBa6qcHOLSNxIrwCEpNtdNb/8Wd2qtHDLUFe52CXCkf7NNx/aA+iHB4eHjqdCPqGIRVUamoqJk6ciHfffRdqtRoAEBsbC1dX3TGJFhYWcHZ2RmxsrFzGzc1Np0zO65wyBVFspoxo1aoV/Pz8MHny5JdKxVlaWuq8liQJWm127mft2rV4+vRpnuX0SU5OxgcffIDRo0fn2lahQoU890lJSYGfnx/8/PywceNGuLi4IDo6Gn5+fkhPz76jzczMLNedmRkZGQVulz5WVlawsrJ6cUEqsKcpZrh3699zGntHhZt/2MDBKROu5TPw9og4zPnQE7WbJqNes2ScO6jGqVBHLNicPR2EnVoLv3fjsSaoHBycsmDnkD1lhHfDFHg3fAIgO6X4yduV0ajNY/T44IE81svMXMCpNMe5UPExtMdZnL5cHnHx9rCxzoBvk5uoXz0GExb7y2XKuSaibtVYTFril2v/u/cdceyCJ0a9ewoLv2uBJ08tMbTnWUTHOOJCpLsp3woVAqHA3Yvi//e/c+eOHBgBMPq3LSMjA71794YQAqtWrTKqrpdVbIIuAJg3bx7q16+P6tX/vdPF29sbx48f1yl3/PhxVKtWDebm5s9Xkady5crlWuft7Y0dO3borDt16pTO6wYNGuDq1auoUqVKQd8Crl27hocPH2LevHlyau/cuXM6ZVxcXPD48WOkpKTAzi57sPWL5uNSqVQ6PXtkOtcv2uKTXv9eA18HZV9P7XvHY/ziaDTvmIjR8+7ip+VuWDW1PMpXyp4YtXaTf3uxPgz6G2aSwKyhFZGRJqFRm8cInPvvGMaju5yQ+NAS+7c4Y/8WZ3m9W/l0fHfmqgneJVHBODk8xaeDD8PZ8QlSnqrw111nTFjsj7Cr5eUyHZtfx4NHdjj7zLpnzfm2NUa+cwrzRv8GrZBw8boGnyz2R1ZWsUm+0EvKSREaWwcAqNVqnaDLGDkB1+3bt3HgwAGdep/NjOXIzMxEfHw8NBqNXOb+fd0hHzmvc8oURLEKuurUqYN+/fph6dKl8rqPP/4YjRs3xqxZs/DOO+/g5MmTWL58OVauXGnUsT788EMsWrQIEyZMwJAhQxAWFpZrcP7EiRPRtGlTBAYGYsiQIbCzs8PVq1cRGhqK5cuX51lvhQoVoFKpsGzZMnz44Yf4448/MGvWLJ0yTZo0ga2tLT799FOMHj0ap0+fznXs51WsWBG//fYbIiMjUbp0aTg6OhrUa0cvr16zZPx2L1xvGb9343PNtfUslbVA4Ny/ETj37zy39x8fi/7jmVKk4m/BhlYvLLN2W2Os3dY43+1PUlVYsKFVgeoiMlZOwHXjxg0cPHgQpUvrzgnn4+ODhIQEhIWFoWHDhgCAAwcOQKvVysOQfHx8MGXKFGRkZMi/vaGhoahevTpKlcp9525+it2fFTNnzpTTgkB2b9Mvv/yCn376CbVr18a0adMwc+ZMo+4GBLKDoy1btiAkJAT16tXD6tWrMWfOHJ0ydevWxeHDh3H9+nW0bNkSr732GqZNmwZ39/y7wF1cXBAcHIxff/0VNWvWxLx587Bw4UKdMs7Ozvjhhx+we/du1KlTBz/++COCgoL0tnfo0KGoXr06GjVqBBcXl1y9f0REREWtKO5eTE5ORnh4uJwxunXrFsLDwxEdHY2MjAz06tUL586dw8aNG5GVlYXY2FjExsbKQ368vb3h7++PoUOH4syZMzh+/DgCAwPRp08f+fe+b9++UKlUGDx4MK5cuYKff/4ZS5YswUcffWRQWyXxKkz7Ti8tKSkJjo6OeHS9EtQOxS7GJip0bYYMLeomEBWJzIxUnNw3HYmJiYql6fKT81vTdd8gWNrlfXd9QWWkpGN7h3UFbnfOI/yeFxAQgKCgIHh55X58GwAcPHhQnl8zPj4egYGB2LlzJ8zMzNCzZ08sXboU9vb/3txx6dIljBw5EmfPnkWZMmUwatQoTJw40aD3VqzSi0RERESGaNOmjd7HBhakb8nZ2RmbNm3SW6Zu3bo4evSowe17FoMuIiIiUgSfvagfgy4iIiJShJJ3L5ZEHORDREREZALs6SIiIiJFsKdLPwZdREREpAgGXfoxvUhERERkAuzpIiIiIkWwp0s/Bl1ERESkCAHjp3woyTO2M71IREREZALs6SIiIiJFML2oH4MuIiIiUgSDLv2YXiQiIiIyAfZ0ERERkSLY06Ufgy4iIiJSBIMu/ZheJCIiIjIB9nQRERGRIoSQIIzsqTJ2/+KMQRcREREpQgvJ6MlRjd2/OGN6kYiIiMgE2NNFREREiuBAev0YdBEREZEiOKZLP6YXiYiIiEyAPV1ERESkCKYX9WPQRURERIpgelE/pheJiIiITIA9XURERKQIoUB6sST3dDHoIiIiIkUIAEIYX0dJxfQiERERkQmwp4uIiIgUoYUEiY8ByheDLiIiIlIE717Uj+lFIiIiIhNgTxcREREpQiskSJwcNV8MuoiIiEgRQihw92IJvn2R6UUiIiIiE2BPFxERESmCA+n1Y9BFREREimDQpR/Ti0REREQmwJ4uIiIiUgTvXtSPPV1ERESkiJy7F41dDHHkyBF06dIF7u7ukCQJISEhz7VJYNq0aShbtixsbGzg6+uLGzdu6JSJj49Hv379oFar4eTkhMGDByM5OVmnzKVLl9CyZUtYW1vDw8MD8+fPN/j8MOgiIiKiV1ZKSgrq1auHFStW5Ll9/vz5WLp0KVavXo3Tp0/Dzs4Ofn5+SE1Nlcv069cPV65cQWhoKHbt2oUjR45g2LBh8vakpCR06NABnp6eCAsLw4IFCxAUFIQ1a9YY1FamF4mIiEgR2T1Vxg6kN6x8x44d0bFjx3zqEli8eDE+++wzdO3aFQDw3Xffwc3NDSEhIejTpw8iIiKwd+9enD17Fo0aNQIALFu2DJ06dcLChQvh7u6OjRs3Ij09HevWrYNKpUKtWrUQHh6OL7/8Uic4exH2dBEREZEicu5eNHZRyq1btxAbGwtfX195naOjI5o0aYKTJ08CAE6ePAknJyc54AIAX19fmJmZ4fTp03KZVq1aQaVSyWX8/PwQGRmJR48eFbg97OkiIiKiYicpKUnntZWVFaysrAyqIzY2FgDg5uams97NzU3eFhsbC1dXV53tFhYWcHZ21inj5eWVq46cbaVKlSpQe9jTRURERIoQCi0A4OHhAUdHR3mZO3euKd9KoWBPFxERESlCyclR79y5A7VaLa83tJcLADQaDQDg/v37KFu2rLz+/v37qF+/vlwmLi5OZ7/MzEzEx8fL+2s0Gty/f1+nTM7rnDIFwZ4uIiIiKnbUarXO8jJBl5eXFzQaDfbv3y+vS0pKwunTp+Hj4wMA8PHxQUJCAsLCwuQyBw4cgFarRZMmTeQyR44cQUZGhlwmNDQU1atXL3BqEWDQRUREREpRMr9YQMnJyQgPD0d4eDiA7MHz4eHhiI6OhiRJGDt2LGbPno0dO3bg8uXLeP/99+Hu7o5u3boBALy9veHv74+hQ4fizJkzOH78OAIDA9GnTx+4u7sDAPr27QuVSoXBgwfjypUr+Pnnn7FkyRJ89NFHBrWV6UUiIiJShhJ3Hxq4/7lz59C2bVv5dU4gFBAQgODgYHzyySdISUnBsGHDkJCQgBYtWmDv3r2wtraW99m4cSMCAwPRrl07mJmZoWfPnli6dKm83dHREfv27cPIkSPRsGFDlClTBtOmTTNouggAkIQwdEYMepUkJSXB0dERj65XgtqBHZv039NmyNCibgJRkcjMSMXJfdORmJioMzaqMOT81lQKngIzW+sX76CH9kkq/hrwuUnabWrs6SIiIiJFvMxjfPKqo6Ri0EVERESKUPLuxZKI+SYiIiIiE2BPFxERESlDSAYPhM+zjhKKQRcREREpgmO69GN6kYiIiMgE2NNFREREyniJyU3zrKOEYtBFREREiuDdi/oVKOjasWNHgSt86623XroxRERERCVVgYKunOcTvYgkScjKyjKmPURERPQqK8HpQWMVKOjSarWF3Q4iIiJ6xTG9qJ9Rdy+mpqYq1Q4iIiKiEs3goCsrKwuzZs1CuXLlYG9vj7/++gsAMHXqVHz77beKN5CIiIheEUKhpYQyOOj6/PPPERwcjPnz50OlUsnra9eujbVr1yraOCIiIqKSwuCg67vvvsOaNWvQr18/mJuby+vr1auHa9euKdo4IiIiepVICi0lk8HzdP3999+oUqVKrvVarRYZGRmKNIqIiIheQZwcVS+De7pq1qyJo0eP5lq/efNmvPbaa4o0ioiIiKikMbina9q0aQgICMDff/8NrVaLrVu3IjIyEt999x127dpVGG0kIiKiVwF7uvQyuKera9eu2LlzJ37//XfY2dlh2rRpiIiIwM6dO9G+ffvCaCMRERG9CoSkzFJCvdSzF1u2bInQ0FCl20JERERUYr30A6/PnTuHiIgIANnjvBo2bKhYo4iIiOjVI0T2YmwdJZXBQdfdu3fx7rvv4vjx43BycgIAJCQkoFmzZvjpp59Qvnx5pdtIRERErwKO6dLL4DFdQ4YMQUZGBiIiIhAfH4/4+HhERERAq9ViyJAhhdFGIiIioleewT1dhw8fxokTJ1C9enV5XfXq1bFs2TK0bNlS0cYRERHRK0SJgfAcSP8vDw+PPCdBzcrKgru7uyKNIiIiolePJLIXY+soqQxOLy5YsACjRo3CuXPn5HXnzp3DmDFjsHDhQkUbR0RERFRSFKinq1SpUpCkf7v7UlJS0KRJE1hYZO+emZkJCwsLDBo0CN26dSuUhhIREVExx4H0ehUo6Fq8eHEhN4OIiIheeRzTpVeBgq6AgIDCbgcRERFRifbSk6MCQGpqKtLT03XWqdVqoxpEREREryimF/UyeCB9SkoKAgMD4erqCjs7O5QqVUpnISIiov8oodBSQhkcdH3yySc4cOAAVq1aBSsrK6xduxYzZsyAu7s7vvvuu8JoIxEREdErz+D04s6dO/Hdd9+hTZs2GDhwIFq2bIkqVarA09MTGzduRL9+/QqjnURERFTcMb2ol8E9XfHx8ahUqRKA7PFb8fHxAIAWLVrgyJEjyraOiIiIXh05dy8au5RQBgddlSpVwq1btwAANWrUwC+//AIguwcs5wHYRERERKTL4KBr4MCBuHjxIgBg0qRJWLFiBaytrTFu3DhMmDBB8QYSERHRqyHnMUDGLiWVwWO6xo0bJ/+/r68vrl27hrCwMFSpUgV169ZVtHFERET0CuGYLr0M7ul6nqenJ3r06MGAi4iIiEwuKysLU6dOhZeXF2xsbFC5cmXMmjULQvwbvQkhMG3aNJQtWxY2Njbw9fXFjRs3dOqJj49Hv379oFar4eTkhMGDByM5OVnRthaop2vp0qUFrnD06NEv3RgiIiIiQ3zxxRdYtWoVNmzYgFq1auHcuXMYOHAgHB0d5Zhk/vz5WLp0KTZs2AAvLy9MnToVfn5+uHr1KqytrQEA/fr1Q0xMDEJDQ5GRkYGBAwdi2LBh2LRpk2JtLVDQ9dVXXxWoMkmSGHQRERH9R0kwfkyWofcunjhxAl27dkXnzp0BABUrVsSPP/6IM2fOAMju5Vq8eDE+++wzdO3aFQDw3Xffwc3NDSEhIejTpw8iIiKwd+9enD17Fo0aNQIALFu2DJ06dcLChQvh7u5u3Jv6fwUKunLuVqRXV/dqdWAhWRZ1M4hMzgpni7oJREXCXGQUdRNMolmzZlizZg2uX7+OatWq4eLFizh27Bi+/PJLANkxTGxsLHx9feV9HB0d0aRJE5w8eRJ9+vTByZMn4eTkJAdcQPa4dTMzM5w+fRrdu3dXpK1GPXuRiIiISKbEPFv/v39SUpLOaisrK1hZWeUqPmnSJCQlJaFGjRowNzdHVlYWPv/8c3my9tjYWACAm5ubzn5ubm7yttjYWLi6uupst7CwgLOzs1xGCUYPpCciIiICoOizFz08PODo6Cgvc+fOzfOQv/zyCzZu3IhNmzbh/Pnz2LBhAxYuXIgNGzYU3vt8SezpIiIiomLnzp07UKvV8uu8erkAYMKECZg0aRL69OkDAKhTpw5u376NuXPnIiAgABqNBgBw//59lC1bVt7v/v37qF+/PgBAo9EgLi5Op97MzEzEx8fL+yuBPV1ERESkDAV7utRqtc6SX9D15MkTmJnphjPm5ubQarUAAC8vL2g0Guzfv1/enpSUhNOnT8PHxwcA4OPjg4SEBISFhcllDhw4AK1WiyZNmhhxQnSxp4uIiIgUocSM8obu36VLF3z++eeoUKECatWqhQsXLuDLL7/EoEGDsuuTJIwdOxazZ89G1apV5Skj3N3d0a1bNwCAt7c3/P39MXToUKxevRoZGRkIDAxEnz59FLtzEXjJoOvo0aP4+uuvcfPmTWzevBnlypXD999/Dy8vL7Ro0UKxxhERERHps2zZMkydOhUjRoxAXFwc3N3d8cEHH2DatGlymU8++QQpKSkYNmwYEhIS0KJFC+zdu1eeowsANm7ciMDAQLRr1w5mZmbo2bOnQfOUFoQknp2ytQC2bNmC/v37o1+/fvj+++9x9epVVKpUCcuXL8fu3buxe/duRRtIxklKSoKjoyPaoCunjCAi+g/JFBk4hO1ITEzUGRtVGHJ+ayrO/hxmzwQyL0Obmoqoz6aYpN2mZvCYrtmzZ2P16tX45ptvYGn574948+bNcf78eUUbR0RERK8QBcd0lUQGB12RkZFo1apVrvWOjo5ISEhQok1EREREJY7BQZdGo8Gff/6Za/2xY8dQqVIlRRpFREREr56cgfTGLiWVwUHX0KFDMWbMGJw+fRqSJOHevXvYuHEjxo8fj+HDhxdGG4mIiOhVkDMjvbFLCWXw3YuTJk2CVqtFu3bt8OTJE7Rq1QpWVlYYP348Ro0aVRhtJCIiInrlGRx0SZKEKVOmYMKECfjzzz+RnJyMmjVrwt7evjDaR0RERK8KJQbCl+D04ktPjqpSqVCzZk0l20JERESvsKKYHPVVYnDQ1bZtW0hS/vnWAwcOGNUgIiIiopLI4KAr5+GQOTIyMhAeHo4//vgDAQEBSrWLiIiIXjVML+plcND11Vdf5bk+KCgIycnJRjeIiIiIXlFKTPlQgoMug6eMyM97772HdevWKVUdERERUYny0gPpn3fy5EmdB0cSERHRfwzTi3oZHHT16NFD57UQAjExMTh37hymTp2qWMOIiIjoFcOgSy+Dgy5HR0ed12ZmZqhevTpmzpyJDh06KNYwIiIiopLEoKArKysLAwcORJ06dVCqVKnCahMRERG9gjhPl34GDaQ3NzdHhw4dkJCQUEjNISIiIiqZDL57sXbt2vjrr78Koy1EREREJZbBQdfs2bMxfvx47Nq1CzExMUhKStJZiIiI6D9KKLSUUAUe0zVz5kx8/PHH6NSpEwDgrbfe0nkckBACkiQhKytL+VYSERFRsccxXfoVOOiaMWMGPvzwQxw8eLAw20NERERUIhU46BIiO/Rs3bp1oTWGiIiIXnEluKfKWAZNGfFsOpGIiIhIBydH1cugoKtatWovDLzi4+ONahARERFRSWRQ0DVjxoxcM9ITERERARxI/yIGBV19+vSBq6trYbWFiIiIXmVML+pV4Hm6OJ6LiIiI6OUZfPciERERUV6YXtSvwEGXVqstzHYQERHRq47pRb0MfgwQERERERnOoIH0RERERPliT5deDLqIiIhIERzTpR/Ti0REREQmwJ4uIiIiUgbTi3ox6CIiIiJlMOjSi+lFIiIiIhNgTxcREREpggPp9WPQRURERMpgelEvpheJiIiITIBBFxERESkiJ71o7GKov//+G++99x5Kly4NGxsb1KlTB+fOnZO3CyEwbdo0lC1bFjY2NvD19cWNGzd06oiPj0e/fv2gVqvh5OSEwYMHIzk52dhTooNBFxERESlDKLQY4NGjR2jevDksLS2xZ88eXL16FYsWLUKpUqXkMvPnz8fSpUuxevVqnD59GnZ2dvDz80Nqaqpcpl+/frhy5QpCQ0Oxa9cuHDlyBMOGDXvJE5E3jukiIiKiV9YXX3wBDw8PrF+/Xl7n5eUl/78QAosXL8Znn32Grl27AgC+++47uLm5ISQkBH369EFERAT27t2Ls2fPolGjRgCAZcuWoVOnTli4cCHc3d0VaSt7uoiIiEgZCvZ0JSUl6SxpaWl5HnLHjh1o1KgR3n77bbi6uuK1117DN998I2+/desWYmNj4evrK69zdHREkyZNcPLkSQDAyZMn4eTkJAdcAODr6wszMzOcPn3a+PPy/xh0ERERkSIkhRYA8PDwgKOjo7zMnTs3z2P+9ddfWLVqFapWrYrffvsNw4cPx+jRo7FhwwYAQGxsLADAzc1NZz83Nzd5W2xsLFxdXXW2W1hYwNnZWS6jBKYXiYiIqNi5c+cO1Gq1/NrKyirPclqtFo0aNcKcOXMAAK+99hr++OMPrF69GgEBASZpa0Gxp4uIiIiUoWB6Ua1W6yz5BV1ly5ZFzZo1ddZ5e3sjOjoaAKDRaAAA9+/f1ylz//59eZtGo0FcXJzO9szMTMTHx8tllMCgi4iIiBRRFFNGNG/eHJGRkTrrrl+/Dk9PTwDZg+o1Gg32798vb09KSsLp06fh4+MDAPDx8UFCQgLCwsLkMgcOHIBWq0WTJk1e8mzkxvQiERERvbLGjRuHZs2aYc6cOejduzfOnDmDNWvWYM2aNQAASZIwduxYzJ49G1WrVoWXlxemTp0Kd3d3dOvWDUB2z5i/vz+GDh2K1atXIyMjA4GBgejTp49idy4CDLqIiIhIKUXwGKDGjRtj27ZtmDx5MmbOnAkvLy8sXrwY/fr1k8t88sknSElJwbBhw5CQkIAWLVpg7969sLa2lsts3LgRgYGBaNeuHczMzNCzZ08sXbrUyDejSxJClOCnHFFSUhIcHR3RBl1hIVkWdXOIiMhEMkUGDmE7EhMTdQakF4ac35paH8yBucr6xTvokZWeiitff2qSdpsax3QRERERmQDTi0RERKSIl3124vN1lFQMuoiIiEgZRTCm61XC9CIRERGRCbCni4iIiBTB9KJ+DLqIiIhIGUwv6sX0IhEREZEJsKeLiIiIFMH0on4MuoiIiEgZTC/qxfQiERERkQmwp4uIiIiUwZ4uvRh0ERERkSI4pks/pheJiIiITIA9XURERKQMphf1YtBFREREipCEgCSMi5qM3b84Y3qRiIiIyATY00VERETKYHpRLwZdREREpAjevagf04tEREREJsCeLiIiIlIG04t6MegiIiIiRTC9qB/Ti0REREQmwJ4uIiIiUgbTi3ox6CIiIiJFML2oH9OLRERERCbAni4iIiJSBtOLejHoIiIiIsWU5PSgsZheJCIiIjIB9nQRERGRMoTIXoyto4Ri0EVERESK4N2L+jG9SERERGQC7OkiIiIiZfDuRb0YdBEREZEiJG32YmwdJRXTi0REREQmwKBLAWvWrIGHhwfMzMywePFiReqMioqCJEkIDw9XpD4qXLWbJGPGhlvYdP4Kfrt3ET7+iTrb3/s4FmuPXMP2Py9j89U/MO/nm6j+WkoRtZao8NnYZeHDGX/juzNXsePmJXy14waq1XtS1M2iwiYUWkqo/2zQNWDAAEiSBEmSYGlpCTc3N7Rv3x7r1q2DVlvwvs2kpCQEBgZi4sSJ+PvvvzFs2LBCae+hQ4cgSRISEhIKpX4yjrWtFn9dscbyT8vnuf3vv6ywYko5fPBGNXzcrQpi76gw98e/4OicaeKWEpnGuEV30KDVY8wfVQEftquOsMMOmPfzTZTWZBR106gQ5dy9aOxSUv1ngy4A8Pf3R0xMDKKiorBnzx60bdsWY8aMwZtvvonMzIL9GEZHRyMjIwOdO3dG2bJlYWtrW8itpuLo3EE1NswvixN7HfPcfnBbKVw46oDYaCvcvm6NNUHusFNr4VXzqYlbSlT4VNZatOiUiLWz3fHHaXvci7LCD4s0uBdlhTff/6eom0dUZP7TQZeVlRU0Gg3KlSuHBg0a4NNPP8X27duxZ88eBAcHAwASEhIwZMgQuLi4QK1W44033sDFixcBAMHBwahTpw4AoFKlSpAkCVFRUbh58ya6du0KNzc32Nvbo3Hjxvj99991ji1JEkJCQnTWOTk5ycd9VlRUFNq2bQsAKFWqFCRJwoABAxQ9F2Q6FpZadHrvIZITzfDXVZuibg6R4szNBcwtgPQ0SWd9WqqEWq8zrV6i5UyOauzykubNmwdJkjB27Fh5XWpqKkaOHInSpUvD3t4ePXv2xP3793X2i46ORufOnWFrawtXV1dMmDChwJ0vhvhPB115eeONN1CvXj1s3boVAPD2228jLi4Oe/bsQVhYGBo0aIB27dohPj4e77zzjhxMnTlzBjExMfDw8EBycjI6deqE/fv348KFC/D390eXLl0QHR39Um3y8PDAli1bAACRkZGIiYnBkiVLlHnDZDJNfJMQcuMydt66jO5DH2Byn8pIiucNxFTyPE0xx9Vztug79j6c3TJgZibwRo9H8G74BM5uTKmXZEWZXjx79iy+/vpr1K1bV2f9uHHjsHPnTvz66684fPgw7t27hx49esjbs7Ky0LlzZ6Snp+PEiRPYsGEDgoODMW3aNGNORZ4YdOWhRo0aiIqKwrFjx3DmzBn8+uuvaNSoEapWrYqFCxfCyckJmzdvho2NDUqXLg0AcHFxgUajgbm5OerVq4cPPvgAtWvXRtWqVTFr1ixUrlwZO3bseKn2mJubw9nZGQDg6uoKjUYDR8e801hpaWlISkrSWah4CD9uhxHtq2HcW1Vw7pAaU76+DcfSHN9CJdP8URUgScCPF65iV9QldBv8AIdCnCBK8HQAVHSSk5PRr18/fPPNNyhVqpS8PjExEd9++y2+/PJLvPHGG2jYsCHWr1+PEydO4NSpUwCAffv24erVq/jhhx9Qv359dOzYEbNmzcKKFSuQnp6uaDsZdOVBCAFJknDx4kUkJyfLXZI5y61bt3Dz5s18909OTsb48ePh7e0NJycn2NvbIyIi4qV7ugwxd+5cODo6youHh0ehH5MKJu2pOe5FWeHaeTt89bEHsjIB/3fji7pZRIUi5rYVJvSsgrcq18Z7jWpidOdqsLAUiLmtKuqmUWFS8O7F5zsQ0tLS8j3syJEj0blzZ/j6+uqsDwsLQ0ZGhs76GjVqoEKFCjh58iQA4OTJk6hTpw7c3NzkMn5+fkhKSsKVK1de/lzkgbmNPERERMDLywvJyckoW7YsDh06lKuMk5NTvvuPHz8eoaGhWLhwIapUqQIbGxv06tVLJ2KWJAniubx1RobxvR6TJ0/GRx99JL9OSkpi4FVMSWaApVUJvk2HCNl/bKQ9NYe9YyYatn6MtbPdi7pJVIiUfPbi879d06dPR1BQUK7yP/30E86fP4+zZ8/m2hYbGwuVSpXrN9vNzQ2xsbFymWcDrpztOduUxKDrOQcOHMDly5cxbtw4lC9fHrGxsbCwsEDFihULXMfx48cxYMAAdO/eHUB2z1dUVJROGRcXF8TExMivb9y4gSdP8p/DRqXK/uswKytL77GtrKxgZWVV4LaSMqxts+Du9W9QrfFIR6VaT/E4wRxJ8eboOyYOJ/epEX/fEmrnTLw18B+U0WTg6E6noms0USFq2DoJkgTcuWmFcl7pGDL1Hu78aY19PzsXddPoFXHnzh2o1Wr5dV6/bXfu3MGYMWMQGhoKa2trUzbvpfyng660tDTExsYiKysL9+/fx969ezF37ly8+eabeP/992FmZgYfHx9069YN8+fPR7Vq1XDv3j3873//Q/fu3dGoUaM8661atSq2bt2KLl26QJIkTJ06NdfcX2+88QaWL18OHx8fZGVlYeLEibC0tMy3rZ6enpAkCbt27UKnTp1gY2MDe3t7Rc8Hvbxq9Z5iwZZ/U84fzrgHANj3cyksnVQe5aukYerbUVA7Z+HxI3Ncv2iLj7tXwe3rxf9Lguhl2Km1GDg5BmXKZuBxgjmO73bE+nllkZUpvXhnenUZefehXAcAtVqtE3TlJSwsDHFxcWjQoIG8LisrC0eOHMHy5cvx22+/IT09HQkJCTq9Xffv34dGowEAaDQanDlzRqfenLsbc8oo5T8ddO3duxdly5aFhYUFSpUqhXr16mHp0qUICAiAmVn2cLfdu3djypQpGDhwIB48eACNRoNWrVrl6op81pdffolBgwahWbNmKFOmDCZOnJhrQPuiRYswcOBAtGzZEu7u7liyZAnCwsLyrbNcuXKYMWMGJk2ahIEDB+L999/Pc3oJKhqXTtrDz71evttnDalousYQFQNHdjrhCHty/3OUTC8WRLt27XD58mWddQMHDkSNGjUwceJEeHh4wNLSEvv370fPnj0BZM8CEB0dDR8fHwCAj48PPv/8c8TFxcHV1RUAEBoaCrVajZo1axr3Zp4jiecHFlGJkpSUBEdHR7RBV1hI+fekERFRyZIpMnAI25GYmPjCHiNj5fzW+HScCQtL43rwMzNScXLPtJdud5s2bVC/fn35sXzDhw/H7t27ERwcDLVajVGjRgEATpw4ASC7Z6x+/fpwd3fH/PnzERsbi/79+2PIkCGYM2eOUe/lef/pni4iIiJSkBLPTlS4K+irr76CmZkZevbsibS0NPj5+WHlypXydnNzc+zatQvDhw+Hj48P7OzsEBAQgJkzZyrbEDDoIiIiIoWYOr2Yl+dnHLC2tsaKFSuwYsWKfPfx9PTE7t27jTtwAXCeLiIiIiITYE8XERERKUMrshdj6yihGHQRERGRMorhmK7ihOlFIiIiIhNgTxcREREpQoICA+kVaUnxxKCLiIiIlKHgjPQlEdOLRERERCbAni4iIiJSRHGYp6s4Y9BFREREyuDdi3oxvUhERERkAuzpIiIiIkVIQkAyciC8sfsXZwy6iIiISBna/1+MraOEYnqRiIiIyATY00VERESKYHpRPwZdREREpAzevagX04tEREREJsCeLiIiIlIGHwOkF4MuIiIiUgRnpNeP6UUiIiIiE2BPFxERESmD6UW9GHQRERGRIiRt9mJsHSUV04tEREREJsCeLiIiIlIG04t6MegiIiIiZXByVL2YXiQiIiIyAfZ0ERERkSL47EX9GHQRERGRMjimSy+mF4mIiIhMgD1dREREpAwBwNh5tkpuRxeDLiIiIlIGx3Tpx/QiERERkQmwp4uIiIiUIaDAQHpFWlIsMegiIiIiZfDuRb2YXiQiIiIyAfZ0ERERkTK0ACQF6iihGHQRERGRInj3on5MLxIRERGZAIMuIiIiUkbOQHpjFwPMnTsXjRs3hoODA1xdXdGtWzdERkbqlElNTcXIkSNRunRp2Nvbo2fPnrh//75OmejoaHTu3Bm2trZwdXXFhAkTkJmZafQpeRaDLiIiIlJGEQRdhw8fxsiRI3Hq1CmEhoYiIyMDHTp0QEpKilxm3Lhx2LlzJ3799VccPnwY9+7dQ48ePeTtWVlZ6Ny5M9LT03HixAls2LABwcHBmDZtmmKnBgAkIUpw8pSQlJQER0dHtEFXWEiWRd0cIiIykUyRgUPYjsTERKjV6kI9Vs5vTbua42FhbmVUXZlZadh/deFLt/vBgwdwdXXF4cOH0apVKyQmJsLFxQWbNm1Cr169AADXrl2Dt7c3Tp48iaZNm2LPnj148803ce/ePbi5uQEAVq9ejYkTJ+LBgwdQqVRGvacc7OkiIiIiZRRBT9fzEhMTAQDOzs4AgLCwMGRkZMDX11cuU6NGDVSoUAEnT54EAJw8eRJ16tSRAy4A8PPzQ1JSEq5cuWJUe57FuxeJiIhIGQpOGZGUlKSz2srKClZW+nvRtFotxo4di+bNm6N27doAgNjYWKhUKjg5OemUdXNzQ2xsrFzm2YArZ3vONqWwp4uIiIiKHQ8PDzg6OsrL3LlzX7jPyJEj8ccff+Cnn34yQQsNx54uIiIiUoSS83TduXNHZ0zXi3q5AgMDsWvXLhw5cgTly5eX12s0GqSnpyMhIUGnt+v+/fvQaDRymTNnzujUl3N3Y04ZJbCni4iIiJSh4JgutVqts+QXdAkhEBgYiG3btuHAgQPw8vLS2d6wYUNYWlpi//798rrIyEhER0fDx8cHAODj44PLly8jLi5OLhMaGgq1Wo2aNWsqdnrY00VERESvrJEjR2LTpk3Yvn07HBwc5DFYjo6OsLGxgaOjIwYPHoyPPvoIzs7OUKvVGDVqFHx8fNC0aVMAQIcOHVCzZk30798f8+fPR2xsLD777DOMHDnyhT1shmDQRURERMrQCkAyciYqrWH7r1q1CgDQpk0bnfXr16/HgAEDAABfffUVzMzM0LNnT6SlpcHPzw8rV66Uy5qbm2PXrl0YPnw4fHx8YGdnh4CAAMycOdOot/I8Bl1ERESkDAWmfDB0/4JMN2ptbY0VK1ZgxYoV+Zbx9PTE7t27DTq2oTimi4iIiMgE2NNFREREClGgpwsl90E5DLqIiIhIGUWQXnyVML1IREREZALs6SIiIiJlaAWMTg8aePfiq4RBFxERESlDaLMXY+sooZheJCIiIjIB9nQRERGRMjiQXi8GXURERKQMjunSi+lFIiIiIhNgTxcREREpg+lFvRh0ERERkTIEFAi6FGlJscT0IhEREZEJsKeLiIiIlMH0ol4MuoiIiEgZWi0AIyc31XJyVCIiIiIyAnu6iIiISBlML+rFoIuIiIiUwaBLL6YXiYiIiEyAPV1ERESkDD4GSC8GXURERKQIIbQQwri7D43dvzhjepGIiIjIBNjTRURERMoQwvj0YAkeSM+gi4iIiJQhFBjTVYKDLqYXiYiIiEyAPV1ERESkDK0WkIwcCF+CB9Iz6CIiIiJlML2oF9OLRERERCbAni4iIiJShNBqIYxML5bkeboYdBEREZEymF7Ui+lFIiIiIhNgTxcREREpQysAiT1d+WHQRURERMoQAoCxU0aU3KCL6UUiIiIiE2BPFxERESlCaAWEkelFUYJ7uhh0ERERkTKEFsanF0vulBFMLxIRERGZAHu6iIiISBFML+rHoIuIiIiUwfSiXgy6SricvxgykWH0JMFERPTqyEQGANP2HCnxW5PT7pKIQVcJ9/jxYwDAMewu4pYQEVFRePz4MRwdHQv1GCqVChqNBsdilfmt0Wg0UKlUitRVnEiiJCdPCVqtFvfu3YODgwMkSSrq5vznJCUlwcPDA3fu3IFarS7q5hCZFK//oiWEwOPHj+Hu7g4zs8K/by41NRXp6emK1KVSqWBtba1IXcUJe7pKODMzM5QvX76om/Gfp1ar+aND/1m8/otOYfdwPcva2rpEBkpK4pQRRERERCbAoIuIiIjIBBh0ERUiKysrTJ8+HVZWVkXdFCKT4/VPpIsD6YmIiIhMgD1dRERERCbAoIuIiIjIBBh0ERWCQ4cOQZIkJCQkFHifJ0+eoGfPnlCr1Qbvq09QUBDq16+vSF1E+qxZswYeHh4wMzPD4sWLFakzKioKkiQhPDxckfqIihKDLioRBgwYAEmSMG/ePJ31ISEhr8yksBs2bMDRo0dx4sQJxMTEFNr8OgMGDEC3bt0KpW569eR8diRJgqWlJdzc3NC+fXusW7cOWm3Bn4GXlJSEwMBATJw4EX///TeGDRtWKO19mT9oiIoLBl1UYlhbW+OLL77Ao0ePiropL+XmzZvw9vZG7dq1odFoXplgkV59/v7+iImJQVRUFPbs2YO2bdtizJgxePPNN5GZmVmgOqKjo5GRkYHOnTujbNmysLW1LeRWE716GHRRieHr6wuNRoO5c+fmW2bLli2oVasWrKysULFiRSxatEhne8WKFTFnzhwMGjQIDg4OqFChAtasWfPCY+/evRvVqlWDjY0N2rZti6ioqFxljh07hpYtW8LGxgYeHh4YPXo0UlJSAABt2rTBokWLcOTIEUiShDZt2gAAvv/+ezRq1AgODg7QaDTo27cv4uLi5DqDg4Ph5OSkcxx9vXtBQUHYsGEDtm/fLvduHDp06IXvj0o2KysraDQalCtXDg0aNMCnn36K7du3Y8+ePQgODgYAJCQkYMiQIXBxcYFarcYbb7yBixcvAsi+DuvUqQMAqFSpEiRJQlRUFG7evImuXbvCzc0N9vb2aNy4MX7//XedY0uShJCQEJ11Tk5O8nGfFRUVhbZt2wIASpUqBUmSMGDAAEXPBVFhYtBFJYa5uTnmzJmDZcuW4e7du7m2h4WFoXfv3ujTpw8uX76MoKAgTJ06NdeX+6JFi9CoUSNcuHABI0aMwPDhwxEZGZnvce/cuYMePXqgS5cuCA8Px5AhQzBp0iSdMjdv3oS/vz969uyJS5cu4eeff8axY8cQGBgIANi6dSuGDh0KHx8fxMTEYOvWrQCAjIwMzJo1CxcvXkRISAiioqKM+pEZP348evfuLfdsxMTEoFmzZi9dH5Vcb7zxBurVqydfi2+//Tbi4uKwZ88ehIWFoUGDBmjXrh3i4+PxzjvvyMHUmTNnEBMTAw8PDyQnJ6NTp07Yv38/Lly4AH9/f3Tp0gXR0dEv1SYPDw9s2bIFABAZGYmYmBgsWbJEmTdMZAqCqAQICAgQXbt2FUII0bRpUzFo0CAhhBDbtm0TOZd53759Rfv27XX2mzBhgqhZs6b82tPTU7z33nvya61WK1xdXcWqVavyPfbkyZN16hBCiIkTJwoA4tGjR0IIIQYPHiyGDRumU+bo0aPCzMxMPH36VAghxJgxY0Tr1q31vs+zZ88KAOLx48dCCCHWr18vHB0ddco8+56FEGL69OmiXr168utnzxWRvuvhnXfeEd7e3uLo0aNCrVaL1NRUne2VK1cWX3/9tRBCiAsXLggA4tatW3qPV6tWLbFs2TL5NQCxbds2nTKOjo5i/fr1Qgghbt26JQCICxcuCCGEOHjwoM5ni+hVwp4uKnG++OILbNiwARERETrrIyIi0Lx5c511zZs3x40bN5CVlSWvq1u3rvz/kiRBo9HIKb2OHTvC3t4e9vb2qFWrllxvkyZNdOr18fHReX3x4kUEBwfL+9rb28PPzw9arRa3bt3K972EhYWhS5cuqFChAhwcHNC6dWsAeOmeAiJDCCEgSRIuXryI5ORklC5dWucavnXrFm7evJnv/snJyRg/fjy8vb3h5OQEe3t7RERE8Pql/yyLom4AkdJatWoFPz8/TJ48+aVScZaWljqvJUmS7+Jau3Ytnj59mmc5fZKTk/HBBx9g9OjRubZVqFAhz31SUlLg5+cHPz8/bNy4ES4uLoiOjoafnx/S09MBAGZmZhDPPVQiIyOjwO0i0iciIgJeXl5ITk5G2bJl8xz/9/yYwmeNHz8eoaGhWLhwIapUqQIbGxv06tVLvn6B7M8Xr2H6r2DQRSXSvHnzUL9+fVSvXl1e5+3tjePHj+uUO378OKpVqwZzc/MC1VuuXLlc67y9vbFjxw6ddadOndJ53aBBA1y9ehVVqlQp6FvAtWvX8PDhQ8ybNw8eHh4AgHPnzumUcXFxwePHj5GSkgI7OzsAeOF8RiqVSqdnjygvBw4cwOXLlzFu3DiUL18esbGxsLCwQMWKFQtcx/HjxzFgwAB0794dQPYfH8/fZOLi4oKYmBj59Y0bN/DkyZN861SpVADAa5heSUwvUolUp04d9OvXD0uXLpXXffzxx9i/fz9mzZqF69evY8OGDVi+fDnGjx9v1LE+/PBD3LhxAxMmTEBkZCQ2bdqUa3D+xIkTceLECQQGBiI8PBw3btzA9u3b5YH0ealQoQJUKhWWLVuGv/76Czt27MCsWbN0yjRp0gS2trb49NNPcfPmzTyP/byKFSvi0qVLiIyMxD///MNeBUJaWhpiY2Px999/4/z585gzZw66du2KN998E++//z58fX3h4+ODbt26Yd++fYiKisKJEycwZcqUXH8IPKtq1arYunUrwsPDcfHiRfTt2zfX3F9vvPEGli9fjgsXLuDcuXP48MMP9fYie3p6QpIk7Nq1Cw8ePEBycrJi54GosDHoohJr5syZOl/wDRo0wC+//IKffvoJtWvXxrRp0zBz5kyjbzmvUKECtmzZgpCQENSrVw+rV6/GnDlzdMrUrVsXhw8fxvXr19GyZUu89tprmDZtGtzd3fOt18XFBcHBwfj1119Rs2ZNzJs3DwsXLtQp4+zsjB9++AG7d+9GnTp18OOPPyIoKEhve4cOHYrq1aujUaNGcHFxydX7R/89e/fuRdmyZVGxYkX4+/vj4MGDWLp0KbZv3w5zc3NIkoTdu3ejVatWGDhwIKpVq4Y+ffrg9u3bcHNzy7feL7/8EqVKlUKzZs3QpUsX+Pn5oUGDBjplFi1aBA8PD7Rs2RJ9+/bF+PHj9c7xVa5cOcyYMQOTJk2Cm5ub3j9ciIobSTyfTCciIiIixbGni4iIiMgEGHQRERERmQCDLiIiIiITYNBFREREZAIMuoiIiIhMgEEXERERkQkw6CIiIiIyAQZdRERERCbAoIuIitSAAQPQrVs3+XWbNm0wduxYk7fj0KFDkCQJCQkJ+ZaRJAkhISEFrjMoKAj169c3ql1RUVGQJOmFz9QkouKPQRcR5TJgwABIkgRJkqBSqVClShXMnDkTmZmZhX7srVu35nrGZH4KEigRERUXFkXdACIqnvz9/bF+/XqkpaVh9+7dGDlyJCwtLTF58uRcZdPT06FSqRQ5rrOzsyL1EBEVN+zpIqI8WVlZQaPRwNPTE8OHD4evry927NgB4N+U4Oeffw53d3dUr14dAHDnzh307t0bTk5OcHZ2RteuXREVFSXXmZWVhY8++ghOTk4oXbo0PvnkEzz/+Nfn04tpaWmYOHEiPDw8YGVlhSpVquDbb79FVFQU2rZtCwAoVaoUJEmSH16u1Woxd+5ceHl5wcbGBvXq1cPmzZt1jrN7925Uq1YNNjY2aNu2rU47C2rixImoVq0abG1tUalSJUydOhUZGRm5yn399dfw8PCAra0tevfujcTERJ3ta9euhbe3N6ytrVGjRg2sXLnS4LYQUfHHoIuICsTGxgbp6eny6/379yMyMhKhoaHYtWsXMjIy4OfnBwcHBxw9ehTHjx+Hvb09/P395f0WLVqE4OBgrFu3DseOHUN8fDy2bdum97jvv/8+fvzxRyxduhQRERH4+uuvYW9vDw8PD2zZsgUAEBkZiZiYGCxZsgQAMHfuXHz33XdYvXo1rly5gnHjxuG9997D4cOHAWQHhz169ECXLl0QHh6OIUOGYNKkSQafEwcHBwQHB+Pq1atYsmQJvvnmG3z11Vc6Zf7880/88ssv2LlzJ/bu3YsLFy5gxIgR8vaNGzdi2rRp+PzzzxEREYE5c+Zg6tSp2LBhg8HtIaJiThARPScgIEB07dpVCCGEVqsVoaGhwsrKSowfP17e7ubmJtLS0uR9vv/+e1G9enWh1WrldWlpacLGxkb89ttvQgghypYtK+bPny9vz8jIEOXLl5ePJYQQrVu3FmPGjBFCCBEZGSkAiNDQ0DzbefDgQQFAPHr0SF6XmpoqbG1txYkTJ3TKDh48WLz77rtCCCEmT54satasqbN94sSJuep6HgCxbdu2fLcvWLBANGzYUH49ffp0YW5uLu7evSuv27NnjzAzMxMxMTFCCCEqV64sNm3apFPPrFmzhI+PjxBCiFu3bgkA4sKFC/kel4heDRzTRUR52rVrF+zt7ZGRkQGtVou+ffsiKChI3l6nTh2dcVwXL17En3/+CQcHB516UlNTcfPmTSQmJiImJgZNmjSRt1lYWKBRo0a5Uow5wsPDYW5ujtatWxe43X/++SeePHmC9u3b66xPT0/Ha6+9BgCIiIjQaQcA+Pj4FPgYOX7++WcsXboUN2/eRHJyMjIzM6FWq3XKVKhQAeXKldM5jlarRWRkJBwcHHDz5k0MHjwYQ4cOlctkZmbC0dHR4PYQUfHGoIuI8tS2bVusWrUKKpUK7u7usLDQ/bqws7PTeZ2cnIyGDRti48aNuepycXF5qTbY2NgYvE9ycjIA4H//+59OsANkj1NTysmTJ9GvXz/MmDEDfn5+cHR0xE8//YRFixYZ3NZvvvkmVxBobm6uWFuJqHhg0EVEebKzs0OVKlUKXL5Bgwb4+eef4erqmqu3J0fZsmVx+vRptGrVCkB2j05YWBgaNGiQZ/k6depAq9Xi8OHD8PX1zbU9p6ctKytLXlezZk1YWVkhOjo63x4yb29v+aaAHKdOnXrxm3zGiRMn4OnpiSlTpsjrbt++natcdHQ07t27B3d3d/k4ZmZmqF69Otzc3ODu7o6//voL/fr1M+j4RPTq4UB6IlJEv379UKZMGXTt2hVHjx7FrVu3cOjQIYwePRp3794FAIwZMwbz5s1DSEgIrl27hhEjRuidY6tixYoICAjAoEGDEBISItf5yy+/AAA8PT0hSRJ27dqFBw8eIDk5GQ4ODhg/fjzGjRuHDRs24ObNmzh//jyWLVsmD07/8MMPcePGDUyYMAGRkZHYtGkTgoODDXq/VatWRXR0NH766SfcvHkTS5cuzfOmAGtrawQEBODixYs4evQoRo8ejd69e0Oj0QAAZsyYgblz52Lp0qW4fv06Ll++jPXr1+PLL780qD1EVPwx6CIiRdja2uLIkSOoUKECevToAW9vbwwePBipqalyz9fHH3+M/v37IyAgAD4+PnBwcED37t311rtq1Sr06tULI0aMQI0aNTB06FCkpKQAAMqVK4cZM2Zg0qRJcHNzQ2BgIABg1qxZmDp1KubOnQtvb2/4+/vjf//7H7y8vABkj7PasmULQkJCUK9ePaxevRpz5swx6P2+9dZbGDduHAIDA1G/fn2cOHECU6dOzVWuSpUq6NGjBzp16oQOHTqgbt26OlNCDBkyBGvXrsX69etRp04dtG7dGsHBwXJbiajkkER+I1iJiIiISDHs6SIiIiIyAQZdRERERCbAoIuIiIjIBBh0EREREZkAgy4iIiIiE2DQRURERGQCDLqIiIiITIBBFxEREZEJMOgiIiIiMgEGXUREREQmwKCLiIiIyAQYdBERERGZwP8BGmGGQnBRQdUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "Random Forest\n",
            "Tuned threshold: 0.025\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " Non-default     0.9906    0.9090    0.9481      1978\n",
            "     Default     0.0270    0.2273    0.0483        22\n",
            "\n",
            "    accuracy                         0.9015      2000\n",
            "   macro avg     0.5088    0.5681    0.4982      2000\n",
            "weighted avg     0.9800    0.9015    0.9382      2000\n",
            "\n"
          ]
        },
        {
          "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 fitted_models:\n",
        "    tuned_predictions = test_prediction_store[model_name]\n",
        "\n",
        "    print(\"=\" * 80)\n",
        "    print(model_name)\n",
        "    print(\n",
        "        f\"Tuned threshold: \"\n",
        "        f\"{selected_thresholds[model_name]:.3f}\"\n",
        "    )\n",
        "    print(\n",
        "        classification_report(\n",
        "            y_test,\n",
        "            tuned_predictions,\n",
        "            target_names=[\"Non-default\", \"Default\"],\n",
        "            digits=4,\n",
        "            zero_division=0,\n",
        "        )\n",
        "    )\n",
        "\n",
        "    ConfusionMatrixDisplay(\n",
        "        confusion_matrix=confusion_matrix(\n",
        "            y_test,\n",
        "            tuned_predictions,\n",
        "        ),\n",
        "        display_labels=[\"Non-default\", \"Default\"],\n",
        "    ).plot(values_format=\"d\")\n",
        "\n",
        "    plt.title(\n",
        "        f\"{model_name}: Test Confusion Matrix \"\n",
        "        f\"(Threshold={selected_thresholds[model_name]:.3f})\"\n",
        "    )\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "c2ff52d1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "c2ff52d1",
        "outputId": "436d7bb7-01f8-44d4-a502-159ecb20179d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "for model_name, probabilities in test_probability_store.items():\n",
        "    RocCurveDisplay.from_predictions(\n",
        "        y_test,\n",
        "        probabilities,\n",
        "        name=model_name,\n",
        "        ax=plt.gca(),\n",
        "    )\n",
        "\n",
        "plt.plot([0, 1], [0, 1], linestyle=\"--\", label=\"No skill\")\n",
        "plt.title(\"Test ROC Curves\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "55e754d1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "55e754d1",
        "outputId": "36b7e23d-f517-4ae1-87ed-e70c158af72d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "for model_name, probabilities in test_probability_store.items():\n",
        "    PrecisionRecallDisplay.from_predictions(\n",
        "        y_test,\n",
        "        probabilities,\n",
        "        name=model_name,\n",
        "        ax=plt.gca(),\n",
        "    )\n",
        "\n",
        "plt.axhline(\n",
        "    y=y_test.mean(),\n",
        "    linestyle=\"--\",\n",
        "    label=\"No-skill prevalence\",\n",
        ")\n",
        "plt.title(\"Test Precision-Recall Curves\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03dd3da7",
      "metadata": {
        "id": "03dd3da7"
      },
      "source": [
        "## 12. Logistic Regression interpretation\n",
        "\n",
        "Coefficients are extracted from the fitted, leakage-free pipeline. Positive coefficients increase estimated default log-odds; negative coefficients reduce them. For one-hot encoded variables, interpretation is relative to the omitted reference category. Coefficients indicate association rather than causation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "3949c462",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "3949c462",
        "outputId": "b7bb1e09-6631-41ad-c8a7-e0620dbf2d87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Largest absolute Logistic Regression coefficients:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                       Feature  Coefficient  Odds Ratio  \\\n",
              "35                               interest_rate       0.5402      1.7164   \n",
              "69                                    state_NY       0.3784      1.4600   \n",
              "93             loan_purpose_debt_consolidation      -0.3453      0.7080   \n",
              "2                               debt_to_income      -0.3255      0.7222   \n",
              "124                               sub_grade_D1       0.2554      1.2909   \n",
              "9                           total_credit_lines      -0.2409      0.7859   \n",
              "86                           homeownership_OWN       0.2322      1.2613   \n",
              "89                    verified_income_Verified       0.2308      1.2596   \n",
              "25                           total_debit_limit       0.2296      1.2581   \n",
              "141                       issue_month_Jan-2018       0.2288      1.2571   \n",
              "57                                    state_MI       0.2045      1.2269   \n",
              "88             verified_income_Source Verified       0.1776      1.1944   \n",
              "94               loan_purpose_home_improvement       0.1768      1.1934   \n",
              "116                               sub_grade_B3      -0.1700      0.8436   \n",
              "90   verification_income_joint_Source Verified       0.1645      1.1789   \n",
              "45                                    state_FL       0.1570      1.1700   \n",
              "47                                    state_HI       0.1526      1.1648   \n",
              "13                    num_collections_last_12m      -0.1515      0.8594   \n",
              "144              disbursement_method_DirectPay      -0.1507      0.8601   \n",
              "104                                    grade_B      -0.1500      0.8607   \n",
              "\n",
              "     Absolute Coefficient  \n",
              "35                 0.5402  \n",
              "69                 0.3784  \n",
              "93                 0.3453  \n",
              "2                  0.3255  \n",
              "124                0.2554  \n",
              "9                  0.2409  \n",
              "86                 0.2322  \n",
              "89                 0.2308  \n",
              "25                 0.2296  \n",
              "141                0.2288  \n",
              "57                 0.2045  \n",
              "88                 0.1776  \n",
              "94                 0.1768  \n",
              "116                0.1700  \n",
              "90                 0.1645  \n",
              "45                 0.1570  \n",
              "47                 0.1526  \n",
              "13                 0.1515  \n",
              "144                0.1507  \n",
              "104                0.1500  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-826032e2-059b-4d43-b0de-9827dfd58180\" 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>Feature</th>\n",
              "      <th>Coefficient</th>\n",
              "      <th>Odds Ratio</th>\n",
              "      <th>Absolute Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>interest_rate</td>\n",
              "      <td>0.5402</td>\n",
              "      <td>1.7164</td>\n",
              "      <td>0.5402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>state_NY</td>\n",
              "      <td>0.3784</td>\n",
              "      <td>1.4600</td>\n",
              "      <td>0.3784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>loan_purpose_debt_consolidation</td>\n",
              "      <td>-0.3453</td>\n",
              "      <td>0.7080</td>\n",
              "      <td>0.3453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>debt_to_income</td>\n",
              "      <td>-0.3255</td>\n",
              "      <td>0.7222</td>\n",
              "      <td>0.3255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>sub_grade_D1</td>\n",
              "      <td>0.2554</td>\n",
              "      <td>1.2909</td>\n",
              "      <td>0.2554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>total_credit_lines</td>\n",
              "      <td>-0.2409</td>\n",
              "      <td>0.7859</td>\n",
              "      <td>0.2409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>homeownership_OWN</td>\n",
              "      <td>0.2322</td>\n",
              "      <td>1.2613</td>\n",
              "      <td>0.2322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>verified_income_Verified</td>\n",
              "      <td>0.2308</td>\n",
              "      <td>1.2596</td>\n",
              "      <td>0.2308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>total_debit_limit</td>\n",
              "      <td>0.2296</td>\n",
              "      <td>1.2581</td>\n",
              "      <td>0.2296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>issue_month_Jan-2018</td>\n",
              "      <td>0.2288</td>\n",
              "      <td>1.2571</td>\n",
              "      <td>0.2288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>state_MI</td>\n",
              "      <td>0.2045</td>\n",
              "      <td>1.2269</td>\n",
              "      <td>0.2045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>verified_income_Source Verified</td>\n",
              "      <td>0.1776</td>\n",
              "      <td>1.1944</td>\n",
              "      <td>0.1776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>loan_purpose_home_improvement</td>\n",
              "      <td>0.1768</td>\n",
              "      <td>1.1934</td>\n",
              "      <td>0.1768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>sub_grade_B3</td>\n",
              "      <td>-0.1700</td>\n",
              "      <td>0.8436</td>\n",
              "      <td>0.1700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>verification_income_joint_Source Verified</td>\n",
              "      <td>0.1645</td>\n",
              "      <td>1.1789</td>\n",
              "      <td>0.1645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>state_FL</td>\n",
              "      <td>0.1570</td>\n",
              "      <td>1.1700</td>\n",
              "      <td>0.1570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>state_HI</td>\n",
              "      <td>0.1526</td>\n",
              "      <td>1.1648</td>\n",
              "      <td>0.1526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>num_collections_last_12m</td>\n",
              "      <td>-0.1515</td>\n",
              "      <td>0.8594</td>\n",
              "      <td>0.1515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>disbursement_method_DirectPay</td>\n",
              "      <td>-0.1507</td>\n",
              "      <td>0.8601</td>\n",
              "      <td>0.1507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>grade_B</td>\n",
              "      <td>-0.1500</td>\n",
              "      <td>0.8607</td>\n",
              "      <td>0.1500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            style=\"display:none;\">\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(coefficient_table\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"interest_rate\",\n          \"num_collections_last_12m\",\n          \"state_FL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24551294075658694,\n        \"min\": -0.3453,\n        \"max\": 0.5402,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.5402,\n          -0.1515,\n          0.157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Odds Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26418004680819285,\n        \"min\": 0.708,\n        \"max\": 1.7164,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1.7164,\n          0.8594,\n          1.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Absolute Coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09881533161993585,\n        \"min\": 0.15,\n        \"max\": 0.5402,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.5402,\n          0.1515,\n          0.157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "logistic_preprocessor = best_logistic.named_steps[\"preprocessor\"]\n",
        "logistic_classifier = best_logistic.named_steps[\"classifier\"]\n",
        "\n",
        "logistic_feature_names = (\n",
        "    logistic_preprocessor.get_feature_names_out()\n",
        ")\n",
        "logistic_coefficients = logistic_classifier.coef_.ravel()\n",
        "\n",
        "coefficient_table = pd.DataFrame(\n",
        "    {\n",
        "        \"Feature\": logistic_feature_names,\n",
        "        \"Coefficient\": logistic_coefficients,\n",
        "        \"Odds Ratio\": np.exp(\n",
        "            np.clip(logistic_coefficients, -20, 20)\n",
        "        ),\n",
        "        \"Absolute Coefficient\": np.abs(\n",
        "            logistic_coefficients\n",
        "        ),\n",
        "    }\n",
        ").sort_values(\n",
        "    \"Absolute Coefficient\",\n",
        "    ascending=False,\n",
        ")\n",
        "\n",
        "print(\"Largest absolute Logistic Regression coefficients:\")\n",
        "display(coefficient_table.head(20).round(4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "df71cd5d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "df71cd5d",
        "outputId": "119c0511-4d30-48bc-b68f-cd9d1f3a5505"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "strongest_positive = (\n",
        "    coefficient_table.sort_values(\n",
        "        \"Coefficient\",\n",
        "        ascending=False,\n",
        "    )\n",
        "    .head(10)\n",
        ")\n",
        "\n",
        "strongest_negative = (\n",
        "    coefficient_table.sort_values(\n",
        "        \"Coefficient\",\n",
        "        ascending=True,\n",
        "    )\n",
        "    .head(10)\n",
        ")\n",
        "\n",
        "coefficient_plot_data = pd.concat(\n",
        "    [strongest_negative, strongest_positive]\n",
        ").sort_values(\"Coefficient\")\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.barh(\n",
        "    coefficient_plot_data[\"Feature\"],\n",
        "    coefficient_plot_data[\"Coefficient\"],\n",
        ")\n",
        "plt.axvline(0, linewidth=1)\n",
        "plt.title(\n",
        "    \"Logistic Regression: Strongest Positive and Negative Coefficients\"\n",
        ")\n",
        "plt.xlabel(\"Coefficient on default log-odds\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd028d6e",
      "metadata": {
        "id": "dd028d6e"
      },
      "source": [
        "## 13. Random Forest model-agnostic permutation importance\n",
        "\n",
        "Permutation importance is calculated on the untouched test set using PR-AUC. It measures the reduction in predictive performance when each original feature is randomly disrupted. This avoids relying exclusively on impurity-based feature importance, which may favour high-cardinality variables."
      ]
    },
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            "text/plain": [
              "                             Feature  Mean PR-AUC decrease  Standard deviation\n",
              "41                     interest_rate              0.012319            0.002431\n",
              "24  months_since_last_credit_inquiry              0.007592            0.000767\n",
              "42                       installment              0.003439            0.000096\n",
              "43                             grade              0.003309            0.002974\n",
              "45                       issue_month              0.002823            0.001286\n",
              "3                      annual_income              0.002085            0.001632\n",
              "2                      homeownership              0.001734            0.002270\n",
              "29                 total_debit_limit              0.001058            0.000712\n",
              "23               accounts_opened_24m              0.000828            0.000922\n",
              "39                       loan_amount              0.000723            0.001379\n",
              "0                         emp_length              0.000456            0.001210\n",
              "1                              state              0.000391            0.001652\n",
              "33                 num_mort_accounts              0.000279            0.000901\n",
              "5                     debt_to_income              0.000097            0.001740\n",
              "40                              term              0.000048            0.000972\n",
              "18      num_historical_failed_to_pay              0.000043            0.000112\n",
              "22      current_installment_accounts              0.000003            0.001900\n",
              "26        num_accounts_120d_past_due              0.000000            0.000000\n",
              "20           current_accounts_delinq              0.000000            0.000000\n",
              "27         num_accounts_30d_past_due              0.000000            0.000000"
            ],
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              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Mean PR-AUC decrease</th>\n",
              "      <th>Standard deviation</th>\n",
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              "      <th>41</th>\n",
              "      <td>interest_rate</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>months_since_last_credit_inquiry</td>\n",
              "      <td>0.007592</td>\n",
              "      <td>0.000767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>installment</td>\n",
              "      <td>0.003439</td>\n",
              "      <td>0.000096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>grade</td>\n",
              "      <td>0.003309</td>\n",
              "      <td>0.002974</td>\n",
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              "      <th>45</th>\n",
              "      <td>issue_month</td>\n",
              "      <td>0.002823</td>\n",
              "      <td>0.001286</td>\n",
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              "      <th>3</th>\n",
              "      <td>annual_income</td>\n",
              "      <td>0.002085</td>\n",
              "      <td>0.001632</td>\n",
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              "      <th>2</th>\n",
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              "      <th>29</th>\n",
              "      <td>total_debit_limit</td>\n",
              "      <td>0.001058</td>\n",
              "      <td>0.000712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>accounts_opened_24m</td>\n",
              "      <td>0.000828</td>\n",
              "      <td>0.000922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>loan_amount</td>\n",
              "      <td>0.000723</td>\n",
              "      <td>0.001379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>emp_length</td>\n",
              "      <td>0.000456</td>\n",
              "      <td>0.001210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>state</td>\n",
              "      <td>0.000391</td>\n",
              "      <td>0.001652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>num_mort_accounts</td>\n",
              "      <td>0.000279</td>\n",
              "      <td>0.000901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>debt_to_income</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>0.001740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>term</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.000972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>num_historical_failed_to_pay</td>\n",
              "      <td>0.000043</td>\n",
              "      <td>0.000112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>current_installment_accounts</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.001900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>num_accounts_120d_past_due</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>current_accounts_delinq</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>num_accounts_30d_past_due</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "rf_permutation = permutation_importance(\n",
        "    best_random_forest,\n",
        "    X_test,\n",
        "    y_test,\n",
        "    scoring=\"average_precision\",\n",
        "    n_repeats=3,\n",
        "    random_state=RANDOM_STATE,\n",
        "    n_jobs=N_JOBS,\n",
        ")\n",
        "\n",
        "permutation_table = pd.DataFrame(\n",
        "    {\n",
        "        \"Feature\": X_test.columns,\n",
        "        \"Mean PR-AUC decrease\": rf_permutation.importances_mean,\n",
        "        \"Standard deviation\": rf_permutation.importances_std,\n",
        "    }\n",
        ").sort_values(\n",
        "    \"Mean PR-AUC decrease\",\n",
        "    ascending=False,\n",
        ")\n",
        "\n",
        "display(permutation_table.head(20).round(6))\n",
        "\n",
        "top_permutation = (\n",
        "    permutation_table.head(15)\n",
        "    .sort_values(\"Mean PR-AUC decrease\")\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(9, 7))\n",
        "plt.barh(\n",
        "    top_permutation[\"Feature\"],\n",
        "    top_permutation[\"Mean PR-AUC decrease\"],\n",
        ")\n",
        "plt.title(\n",
        "    \"Random Forest Permutation Importance on Test PR-AUC\"\n",
        ")\n",
        "plt.xlabel(\"Decrease in PR-AUC after permutation\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "df84485c",
      "metadata": {
        "id": "df84485c"
      },
      "source": [
        "## 14. Partial dependence\n",
        "\n",
        "Partial dependence illustrates the model's average predicted response as one feature varies. It must not be interpreted as a causal effect, particularly where financial predictors are correlated."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "0046efae",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0046efae",
        "outputId": "09046da4-b441-46d5-d33e-e7c799758cd9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "partial_dependence_features = [\n",
        "    feature\n",
        "    for feature in [\n",
        "        \"interest_rate\",\n",
        "        \"debt_to_income\",\n",
        "        \"annual_income\",\n",
        "    ]\n",
        "    if feature in X_test.columns\n",
        "]\n",
        "\n",
        "for feature in partial_dependence_features:\n",
        "    plt.figure(figsize=(7, 5))\n",
        "    PartialDependenceDisplay.from_estimator(\n",
        "        best_random_forest,\n",
        "        X_test,\n",
        "        features=[feature],\n",
        "        kind=\"average\",\n",
        "        grid_resolution=25,\n",
        "        ax=plt.gca(),\n",
        "    )\n",
        "    plt.title(\n",
        "        f\"Random Forest Partial Dependence: {feature}\"\n",
        "    )\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a16274e",
      "metadata": {
        "id": "3a16274e"
      },
      "source": [
        "## 15. SHAP analysis for Random Forest\n",
        "\n",
        "SHAP values are calculated on a limited test sample for computational efficiency. The chart identifies transformed features that most strongly influence model predictions. SHAP explanations remain associational and should be interpreted with the fairness and selection-bias limitations of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "05427954",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "05427954",
        "outputId": "c3fe1484-8e4e-4da1-ad5b-75e8f1c5cf5d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x750 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import shap\n",
        "\n",
        "shap_sample_size = min(300, len(X_test))\n",
        "X_shap_raw = X_test.sample(\n",
        "    n=shap_sample_size,\n",
        "    random_state=RANDOM_STATE,\n",
        ")\n",
        "\n",
        "rf_preprocessor = best_random_forest.named_steps[\n",
        "    \"preprocessor\"\n",
        "]\n",
        "rf_classifier = best_random_forest.named_steps[\n",
        "    \"classifier\"\n",
        "]\n",
        "\n",
        "X_shap_transformed = rf_preprocessor.transform(X_shap_raw)\n",
        "\n",
        "if hasattr(X_shap_transformed, \"toarray\"):\n",
        "    X_shap_dense = X_shap_transformed.toarray()\n",
        "else:\n",
        "    X_shap_dense = np.asarray(X_shap_transformed)\n",
        "\n",
        "rf_feature_names = rf_preprocessor.get_feature_names_out()\n",
        "\n",
        "shap_explainer = shap.TreeExplainer(rf_classifier)\n",
        "shap_values_raw = shap_explainer.shap_values(X_shap_dense)\n",
        "\n",
        "if isinstance(shap_values_raw, list):\n",
        "    shap_values_default = shap_values_raw[1]\n",
        "else:\n",
        "    shap_array = np.asarray(shap_values_raw)\n",
        "    if shap_array.ndim == 3:\n",
        "        shap_values_default = shap_array[:, :, 1]\n",
        "    else:\n",
        "        shap_values_default = shap_array\n",
        "\n",
        "shap.summary_plot(\n",
        "    shap_values_default,\n",
        "    X_shap_dense,\n",
        "    feature_names=rf_feature_names,\n",
        "    plot_type=\"bar\",\n",
        "    max_display=15,\n",
        "    show=False,\n",
        ")\n",
        "plt.title(\"Random Forest SHAP Global Importance\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e1c934b6",
      "metadata": {
        "id": "e1c934b6"
      },
      "source": [
        "## 16. Proxy-group fairness audit\n",
        "\n",
        "The dataset contains no direct gender or race attributes. Therefore, protected-group fairness cannot be established. The following analysis is a **descriptive proxy audit**, not proof of legal or ethical fairness.\n",
        "\n",
        "The audit reports:\n",
        "\n",
        "- actual default rate;\n",
        "- predicted default rate;\n",
        "- predicted non-default rate as an approval proxy;\n",
        "- true-positive rate for default;\n",
        "- false-positive rate;\n",
        "- default precision;\n",
        "- approval-rate ratio relative to the group with the highest predicted non-default rate.\n",
        "\n",
        "Small-group metrics are suppressed because the test set contains very few defaults."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "1b7bc652",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1b7bc652",
        "outputId": "053dace7-d65b-4cd2-a43e-efc538c6531c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fairness audit model selected using validation F2: Logistic Regression\n",
            "Decision threshold: 0.015\n"
          ]
        }
      ],
      "source": [
        "validation_f2_scores = {}\n",
        "\n",
        "for model_name, model in fitted_models.items():\n",
        "    valid_probabilities = model.predict_proba(X_valid)[:, 1]\n",
        "    tuned_predictions = (\n",
        "        valid_probabilities\n",
        "        >= selected_thresholds[model_name]\n",
        "    ).astype(int)\n",
        "\n",
        "    validation_f2_scores[model_name] = fbeta_score(\n",
        "        y_valid,\n",
        "        tuned_predictions,\n",
        "        beta=2,\n",
        "        zero_division=0,\n",
        "    )\n",
        "\n",
        "audit_model_name = max(\n",
        "    validation_f2_scores,\n",
        "    key=validation_f2_scores.get,\n",
        ")\n",
        "audit_predictions = test_prediction_store[audit_model_name]\n",
        "\n",
        "print(\n",
        "    \"Fairness audit model selected using validation F2:\",\n",
        "    audit_model_name,\n",
        ")\n",
        "print(\n",
        "    \"Decision threshold:\",\n",
        "    round(selected_thresholds[audit_model_name], 4),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "2a8e6fa2",
      "metadata": {
        "id": "2a8e6fa2"
      },
      "outputs": [],
      "source": [
        "def group_fairness_audit(\n",
        "    group_values,\n",
        "    y_true,\n",
        "    y_pred,\n",
        "    minimum_group_size=30,\n",
        "):\n",
        "    audit_frame = pd.DataFrame(\n",
        "        {\n",
        "            \"group\": pd.Series(group_values).reset_index(\n",
        "                drop=True\n",
        "            ),\n",
        "            \"actual\": pd.Series(y_true).reset_index(drop=True),\n",
        "            \"predicted\": pd.Series(y_pred).reset_index(\n",
        "                drop=True\n",
        "            ),\n",
        "        }\n",
        "    )\n",
        "\n",
        "    rows = []\n",
        "\n",
        "    for group_name, group_data in audit_frame.groupby(\n",
        "        \"group\",\n",
        "        dropna=False,\n",
        "    ):\n",
        "        n = len(group_data)\n",
        "        if n < minimum_group_size:\n",
        "            continue\n",
        "\n",
        "        actual = group_data[\"actual\"].to_numpy()\n",
        "        predicted = group_data[\"predicted\"].to_numpy()\n",
        "\n",
        "        tn, fp, fn, tp = confusion_matrix(\n",
        "            actual,\n",
        "            predicted,\n",
        "            labels=[0, 1],\n",
        "        ).ravel()\n",
        "\n",
        "        true_positive_rate = (\n",
        "            tp / (tp + fn) if (tp + fn) > 0 else np.nan\n",
        "        )\n",
        "        false_positive_rate = (\n",
        "            fp / (fp + tn) if (fp + tn) > 0 else np.nan\n",
        "        )\n",
        "        default_precision = (\n",
        "            tp / (tp + fp) if (tp + fp) > 0 else np.nan\n",
        "        )\n",
        "        predicted_default_rate = predicted.mean()\n",
        "        approval_proxy_rate = 1 - predicted_default_rate\n",
        "\n",
        "        rows.append(\n",
        "            {\n",
        "                \"Group\": group_name,\n",
        "                \"N\": n,\n",
        "                \"Actual Default Rate\": actual.mean(),\n",
        "                \"Predicted Default Rate\": predicted_default_rate,\n",
        "                \"Predicted Non-default Rate (Approval Proxy)\": approval_proxy_rate,\n",
        "                \"True Positive Rate (Default)\": true_positive_rate,\n",
        "                \"False Positive Rate\": false_positive_rate,\n",
        "                \"Precision (Default)\": default_precision,\n",
        "            }\n",
        "        )\n",
        "\n",
        "    result = pd.DataFrame(rows)\n",
        "\n",
        "    if not result.empty:\n",
        "        maximum_approval_proxy = result[\n",
        "            \"Predicted Non-default Rate (Approval Proxy)\"\n",
        "        ].max()\n",
        "        result[\"Approval-Rate Ratio\"] = (\n",
        "            result[\n",
        "                \"Predicted Non-default Rate (Approval Proxy)\"\n",
        "            ]\n",
        "            / maximum_approval_proxy\n",
        "        )\n",
        "\n",
        "    return result.sort_values(\"N\", ascending=False)"
      ]
    },
    {
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      "execution_count": 34,
      "id": "b78077b0",
      "metadata": {
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          "height": 1000
        },
        "id": "b78077b0",
        "outputId": "66397230-d205-4fef-8f8a-73bed328d959"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "Proxy audit: homeownership\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      Group    N  Actual Default Rate  Predicted Default Rate  \\\n",
              "0  MORTGAGE  932               0.0064                  0.1599   \n",
              "2      RENT  793               0.0126                  0.2219   \n",
              "1       OWN  275               0.0218                  0.2182   \n",
              "\n",
              "   Predicted Non-default Rate (Approval Proxy)  True Positive Rate (Default)  \\\n",
              "0                                       0.8401                        0.3333   \n",
              "2                                       0.7781                        0.5000   \n",
              "1                                       0.7818                        0.3333   \n",
              "\n",
              "   False Positive Rate  Precision (Default)  Approval-Rate Ratio  \n",
              "0               0.1587               0.0134               1.0000  \n",
              "2               0.2184               0.0284               0.9261  \n",
              "1               0.2156               0.0333               0.9306  "
            ],
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          "text": [
            "================================================================================\n",
            "Proxy audit: verified_income\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "             Group    N  Actual Default Rate  Predicted Default Rate  \\\n",
              "1  Source Verified  801               0.0075                  0.2497   \n",
              "0     Not Verified  761               0.0079                  0.0841   \n",
              "2         Verified  438               0.0228                  0.2763   \n",
              "\n",
              "   Predicted Non-default Rate (Approval Proxy)  True Positive Rate (Default)  \\\n",
              "1                                       0.7503                        0.3333   \n",
              "0                                       0.9159                        0.0000   \n",
              "2                                       0.7237                        0.7000   \n",
              "\n",
              "   False Positive Rate  Precision (Default)  Approval-Rate Ratio  \n",
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              "        Group     N  Actual Default Rate  Predicted Default Rate  \\\n",
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              "  Group    N  Actual Default Rate  Predicted Default Rate  \\\n",
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            "================================================================================\n",
            "Proxy audit: loan_purpose\n"
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              "                Group    N  Actual Default Rate  Predicted Default Rate  \\\n",
              "1  debt_consolidation  997               0.0140                  0.1555   \n",
              "0         credit_card  463               0.0000                  0.1706   \n",
              "4               other  195               0.0103                  0.2769   \n",
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              "3      major_purchase   63               0.0476                  0.3016   \n",
              "5      small_business   33               0.0000                  0.3636   \n",
              "\n",
              "   Predicted Non-default Rate (Approval Proxy)  True Positive Rate (Default)  \\\n",
              "1                                       0.8445                        0.3571   \n",
              "0                                       0.8294                           NaN   \n",
              "4                                       0.7231                        0.5000   \n",
              "2                                       0.7574                           NaN   \n",
              "3                                       0.6984                        0.3333   \n",
              "5                                       0.6364                           NaN   \n",
              "\n",
              "   False Positive Rate  Precision (Default)  Approval-Rate Ratio  \n",
              "1               0.1526               0.0323               1.0000  \n",
              "0               0.1706               0.0000               0.9820  \n",
              "4               0.2746               0.0185               0.8562  \n",
              "2               0.2426               0.0000               0.8968  \n",
              "3               0.3000               0.0526               0.8270  \n",
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              "      <th>1</th>\n",
              "      <td>debt_consolidation</td>\n",
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            "================================================================================\n",
            "Proxy audit: state\n"
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            "text/plain": [
              "   Group    N  Actual Default Rate  Predicted Default Rate  \\\n",
              "1     CA  317               0.0158                  0.1672   \n",
              "2     FL  166               0.0241                  0.2470   \n",
              "10    TX  150               0.0067                  0.1800   \n",
              "8     NY  148               0.0203                  0.4054   \n",
              "4     IL   78               0.0000                  0.1667   \n",
              "3     GA   72               0.0139                  0.1667   \n",
              "7     NJ   67               0.0149                  0.1045   \n",
              "9     OH   62               0.0323                  0.1290   \n",
              "6     NC   61               0.0328                  0.2459   \n",
              "0     AZ   54               0.0000                  0.1852   \n",
              "5     MD   52               0.0192                  0.2308   \n",
              "11    VA   50               0.0000                  0.1200   \n",
              "\n",
              "    Predicted Non-default Rate (Approval Proxy)  True Positive Rate (Default)  \\\n",
              "1                                        0.8328                        0.4000   \n",
              "2                                        0.7530                        0.2500   \n",
              "10                                       0.8200                        1.0000   \n",
              "8                                        0.5946                        0.6667   \n",
              "4                                        0.8333                           NaN   \n",
              "3                                        0.8333                        1.0000   \n",
              "7                                        0.8955                        0.0000   \n",
              "9                                        0.8710                        0.5000   \n",
              "6                                        0.7541                        0.0000   \n",
              "0                                        0.8148                           NaN   \n",
              "5                                        0.7692                        0.0000   \n",
              "11                                       0.8800                           NaN   \n",
              "\n",
              "    False Positive Rate  Precision (Default)  Approval-Rate Ratio  \n",
              "1                0.1635               0.0377               0.9300  \n",
              "2                0.2469               0.0244               0.8409  \n",
              "10               0.1745               0.0370               0.9157  \n",
              "8                0.4000               0.0333               0.6640  \n",
              "4                0.1667               0.0000               0.9306  \n",
              "3                0.1549               0.0833               0.9306  \n",
              "7                0.1061               0.0000               1.0000  \n",
              "9                0.1167               0.1250               0.9726  \n",
              "6                0.2542               0.0000               0.8421  \n",
              "0                0.1852               0.0000               0.9099  \n",
              "5                0.2353               0.0000               0.8590  \n",
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              "      <td>0.2500</td>\n",
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              "      <td>0.0244</td>\n",
              "      <td>0.8409</td>\n",
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              "      <td>0.0370</td>\n",
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              "      <th>8</th>\n",
              "      <td>NY</td>\n",
              "      <td>148</td>\n",
              "      <td>0.0203</td>\n",
              "      <td>0.4054</td>\n",
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              "      <td>0.6667</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.0333</td>\n",
              "      <td>0.6640</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IL</td>\n",
              "      <td>78</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1667</td>\n",
              "      <td>0.8333</td>\n",
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              "      <td>0.1667</td>\n",
              "      <td>0.0000</td>\n",
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              "      <td>72</td>\n",
              "      <td>0.0139</td>\n",
              "      <td>0.1667</td>\n",
              "      <td>0.8333</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.1549</td>\n",
              "      <td>0.0833</td>\n",
              "      <td>0.9306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NJ</td>\n",
              "      <td>67</td>\n",
              "      <td>0.0149</td>\n",
              "      <td>0.1045</td>\n",
              "      <td>0.8955</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1061</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0000</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>OH</td>\n",
              "      <td>62</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.1290</td>\n",
              "      <td>0.8710</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.1167</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.9726</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NC</td>\n",
              "      <td>61</td>\n",
              "      <td>0.0328</td>\n",
              "      <td>0.2459</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>0.2542</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.8421</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AZ</td>\n",
              "      <td>54</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>0.8148</td>\n",
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              "      <td>0.1852</td>\n",
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              "      <td>0.9099</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>MD</td>\n",
              "      <td>52</td>\n",
              "      <td>0.0192</td>\n",
              "      <td>0.2308</td>\n",
              "      <td>0.7692</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2353</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.8590</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
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              "      <td>50</td>\n",
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              "      <td>0.1200</td>\n",
              "      <td>0.0000</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"    display(table\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"MD\",\n          \"AZ\",\n          \"CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78,\n        \"min\": 50,\n        \"max\": 317,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          52,\n          54,\n          317\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actual Default Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011640134332245797,\n        \"min\": 0.0,\n        \"max\": 0.0328,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.0328,\n          0.0241,\n          0.0139\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted Default Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08073843288951395,\n        \"min\": 0.1045,\n        \"max\": 0.4054,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.1045,\n          0.1672,\n          0.2308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted Non-default Rate (Approval Proxy)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08073843288951392,\n        \"min\": 0.5946,\n        \"max\": 0.8955,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.8955,\n          0.8328,\n          0.7692\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True Positive Rate (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40286648463783187,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.4,\n          0.25,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"False Positive Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08168818022136856,\n        \"min\": 0.1061,\n        \"max\": 0.4,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.2353,\n          0.1852,\n          0.1635\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.039755376042020084,\n        \"min\": 0.0,\n        \"max\": 0.125,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0377,\n          0.0244,\n          0.0833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Approval-Rate Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09015618896720795,\n        \"min\": 0.664,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          1.0,\n          0.93,\n          0.859\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {}
        }
      ],
      "source": [
        "fairness_tables = {}\n",
        "\n",
        "proxy_features = [\n",
        "    feature\n",
        "    for feature in [\n",
        "        \"homeownership\",\n",
        "        \"verified_income\",\n",
        "        \"application_type\",\n",
        "        \"grade\",\n",
        "        \"loan_purpose\",\n",
        "        \"state\",\n",
        "    ]\n",
        "    if feature in X_test.columns\n",
        "]\n",
        "\n",
        "for feature in proxy_features:\n",
        "    minimum_size = 50 if feature == \"state\" else 30\n",
        "\n",
        "    table = group_fairness_audit(\n",
        "        X_test[feature],\n",
        "        y_test,\n",
        "        audit_predictions,\n",
        "        minimum_group_size=minimum_size,\n",
        "    )\n",
        "\n",
        "    fairness_tables[feature] = table\n",
        "\n",
        "    print(\"=\" * 80)\n",
        "    print(f\"Proxy audit: {feature}\")\n",
        "    display(table.round(4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "aa5d505a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "aa5d505a",
        "outputId": "2551c5be-1710-459c-81ab-cce2813098c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Proxy audit: annual-income bands\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  Group    N  Actual Default Rate  Predicted Default Rate  \\\n",
              "3  (95000.0, 1200000.0]  527               0.0133                  0.2106   \n",
              "0     (-0.001, 45000.0]  526               0.0114                  0.2186   \n",
              "1    (45000.0, 65000.0]  480               0.0062                  0.1729   \n",
              "2    (65000.0, 95000.0]  465               0.0129                  0.1613   \n",
              "\n",
              "   Predicted Non-default Rate (Approval Proxy)  True Positive Rate (Default)  \\\n",
              "3                                       0.7894                        0.2857   \n",
              "0                                       0.7814                        0.3333   \n",
              "1                                       0.8271                        0.6667   \n",
              "2                                       0.8387                        0.5000   \n",
              "\n",
              "   False Positive Rate  Precision (Default)  Approval-Rate Ratio  \n",
              "3               0.2096               0.0180               0.9412  \n",
              "0               0.2173               0.0174               0.9316  \n",
              "1               0.1698               0.0241               0.9861  \n",
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              "      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-d17987d9-49c3-46e3-be67-b88b62cb172c 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-d17987d9-49c3-46e3-be67-b88b62cb172c');\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(income_fairness_table\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"interval[float64, right]\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"(-0.001, 45000.0]\",\n          \"(65000.0, 95000.0]\",\n          \"(95000.0, 1200000.0]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 465,\n        \"max\": 527,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          526,\n          465,\n          527\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actual Default Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0032705758922041035,\n        \"min\": 0.0062,\n        \"max\": 0.0133,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0114,\n          0.0129,\n          0.0133\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted Default Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02802100402674156,\n        \"min\": 0.1613,\n        \"max\": 0.2186,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2186,\n          0.1613,\n          0.2106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted Non-default Rate (Approval Proxy)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.028021004026741556,\n        \"min\": 0.7814,\n        \"max\": 0.8387,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.7814,\n          0.8387,\n          0.7894\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"True Positive Rate (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17322542875301727,\n        \"min\": 0.2857,\n        \"max\": 0.6667,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.3333,\n          0.5,\n          0.2857\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"False Positive Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.029568338923021468,\n        \"min\": 0.1569,\n        \"max\": 0.2173,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2173,\n          0.1569,\n          0.2096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision (Default)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010527859864822164,\n        \"min\": 0.0174,\n        \"max\": 0.04,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0174,\n          0.04,\n          0.018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Approval-Rate Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03342607515099551,\n        \"min\": 0.9316,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9316,\n          1.0,\n          0.9412\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Income bands are constructed from training-set quantiles only.\n",
        "income_quantiles = X_train[\"annual_income\"].quantile(\n",
        "    [0.00, 0.25, 0.50, 0.75, 1.00]\n",
        ").to_numpy()\n",
        "income_quantiles = np.unique(income_quantiles)\n",
        "\n",
        "X_test_income_band = pd.cut(\n",
        "    X_test[\"annual_income\"],\n",
        "    bins=income_quantiles,\n",
        "    include_lowest=True,\n",
        "    duplicates=\"drop\",\n",
        ")\n",
        "\n",
        "income_fairness_table = group_fairness_audit(\n",
        "    X_test_income_band,\n",
        "    y_test,\n",
        "    audit_predictions,\n",
        "    minimum_group_size=30,\n",
        ")\n",
        "\n",
        "fairness_tables[\"annual_income_band\"] = (\n",
        "    income_fairness_table\n",
        ")\n",
        "\n",
        "print(\"Proxy audit: annual-income bands\")\n",
        "display(income_fairness_table.round(4))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "460ba82d",
      "metadata": {
        "id": "460ba82d"
      },
      "source": [
        "## 17. Save reproducible outputs\n",
        "\n",
        "The exported tables and fitted pipelines can be used to update the report and presentation. Do not manually copy results from the superseded notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "0923d363",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0923d363",
        "outputId": "b8820832-0be9-457d-fc7f-354ed9c04a7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outputs saved to: /content/credit_risk_outputs\n"
          ]
        }
      ],
      "source": [
        "OUTPUT_DIRECTORY = Path(\"credit_risk_outputs\")\n",
        "OUTPUT_DIRECTORY.mkdir(exist_ok=True)\n",
        "\n",
        "leakage_audit.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"leakage_audit.csv\",\n",
        "    index=False,\n",
        ")\n",
        "imbalance_benchmark.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"imbalance_strategy_comparison.csv\",\n",
        "    index=False,\n",
        ")\n",
        "cv_comparison.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"cross_validation_summary.csv\",\n",
        "    index=False,\n",
        ")\n",
        "validation_threshold_results.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"validation_threshold_results.csv\",\n",
        "    index=False,\n",
        ")\n",
        "final_test_results.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"final_test_results.csv\",\n",
        "    index=False,\n",
        ")\n",
        "coefficient_table.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"logistic_coefficients.csv\",\n",
        "    index=False,\n",
        ")\n",
        "permutation_table.to_csv(\n",
        "    OUTPUT_DIRECTORY / \"random_forest_permutation_importance.csv\",\n",
        "    index=False,\n",
        ")\n",
        "\n",
        "for feature_name, table in fairness_tables.items():\n",
        "    safe_name = feature_name.replace(\" \", \"_\")\n",
        "    table.to_csv(\n",
        "        OUTPUT_DIRECTORY\n",
        "        / f\"fairness_audit_{safe_name}.csv\",\n",
        "        index=False,\n",
        "    )\n",
        "\n",
        "joblib.dump(\n",
        "    {\n",
        "        \"pipeline\": best_logistic,\n",
        "        \"threshold\": selected_thresholds[\n",
        "            \"Logistic Regression\"\n",
        "        ],\n",
        "        \"imbalance_strategy\": selected_strategies[\n",
        "            \"Logistic Regression\"\n",
        "        ],\n",
        "    },\n",
        "    OUTPUT_DIRECTORY / \"logistic_regression_pipeline.joblib\",\n",
        ")\n",
        "\n",
        "joblib.dump(\n",
        "    {\n",
        "        \"pipeline\": best_random_forest,\n",
        "        \"threshold\": selected_thresholds[\n",
        "            \"Random Forest\"\n",
        "        ],\n",
        "        \"imbalance_strategy\": selected_strategies[\n",
        "            \"Random Forest\"\n",
        "        ],\n",
        "    },\n",
        "    OUTPUT_DIRECTORY / \"random_forest_pipeline.joblib\",\n",
        ")\n",
        "\n",
        "print(f\"Outputs saved to: {OUTPUT_DIRECTORY.resolve()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "289da587",
      "metadata": {
        "id": "289da587"
      },
      "source": [
        "## 18. Automatically generated result summary\n",
        "\n",
        "Use this output to verify the report conclusion. The model with the highest ROC-AUC is not necessarily the model with the highest default recall at a selected threshold."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "e1b366cf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1b366cf",
        "outputId": "300fa1f2-54bb-453e-e9ff-3aceaa76263e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FINAL INTERPRETATION CHECK\n",
            "--------------------------------------------------------------------------------\n",
            "Higher test ROC-AUC: Logistic Regression\n",
            "Higher test PR-AUC: Logistic Regression\n",
            "Higher default recall at tuned threshold: Logistic Regression\n",
            "Higher default F1 at tuned threshold: Random Forest\n",
            "\n",
            "Do not claim that one model is universally superior. Discuss discrimination, threshold behaviour, false positives, false negatives, interpretability, and fairness separately.\n"
          ]
        }
      ],
      "source": [
        "tuned_test_results = final_test_results[\n",
        "    final_test_results[\"Threshold Type\"]\n",
        "    == \"Validation-tuned F2\"\n",
        "].copy()\n",
        "\n",
        "best_roc_model = tuned_test_results.loc[\n",
        "    tuned_test_results[\"ROC-AUC\"].idxmax(),\n",
        "    \"Model\",\n",
        "]\n",
        "best_pr_model = tuned_test_results.loc[\n",
        "    tuned_test_results[\"PR-AUC\"].idxmax(),\n",
        "    \"Model\",\n",
        "]\n",
        "best_recall_model = tuned_test_results.loc[\n",
        "    tuned_test_results[\"Recall (Default)\"].idxmax(),\n",
        "    \"Model\",\n",
        "]\n",
        "best_f1_model = tuned_test_results.loc[\n",
        "    tuned_test_results[\"F1 (Default)\"].idxmax(),\n",
        "    \"Model\",\n",
        "]\n",
        "\n",
        "print(\"FINAL INTERPRETATION CHECK\")\n",
        "print(\"-\" * 80)\n",
        "print(\"Higher test ROC-AUC:\", best_roc_model)\n",
        "print(\"Higher test PR-AUC:\", best_pr_model)\n",
        "print(\"Higher default recall at tuned threshold:\", best_recall_model)\n",
        "print(\"Higher default F1 at tuned threshold:\", best_f1_model)\n",
        "print()\n",
        "print(\n",
        "    \"Do not claim that one model is universally superior. \"\n",
        "    \"Discuss discrimination, threshold behaviour, false positives, \"\n",
        "    \"false negatives, interpretability, and fairness separately.\"\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
}