loan_defaults_031_feature_selection_model_comps.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/micah-shull/loan_defaults/blob/main/loan_defaults_031_feature_selection_model_comps.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjJGNa0zVRt4"
      },
      "source": [
        "## Data Description\n",
        "\n",
        "This dataset contains information on clients' credit card behavior, provided by a financial institution in Taiwan. The target variable is `default_payment_next_month`, which indicates whether the client defaulted on their credit card payment the next month.\n",
        "\n",
        "url = https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients\n",
        "\n",
        "#### Variables:\n",
        "\n",
        "- **ID**: ID of each client.\n",
        "- **LIMIT_BAL**: Amount of given credit in NT dollars (includes individual and family/supplementary credit).\n",
        "- **SEX**: Gender (1 = male, 2 = female).\n",
        "- **EDUCATION**: Education level (1 = graduate school, 2 = university, 3 = high school, 4 = others, 5 = unknown, 6 = unknown).\n",
        "- **MARRIAGE**: Marital status (1 = married, 2 = single, 3 = others).\n",
        "- **AGE**: Age in years.\n",
        "\n",
        "#### Payment History (PAY_X):\n",
        "- **PAY_0**: Repayment status in September 2005 (-1 = pay duly, 1 = payment delay for one month, 2 = payment delay for two months, ... 8 = payment delay for eight months, 9 = payment delay for nine months and above).\n",
        "- **PAY_2**: Repayment status in August 2005.\n",
        "- **PAY_3**: Repayment status in July 2005.\n",
        "- **PAY_4**: Repayment status in June 2005.\n",
        "- **PAY_5**: Repayment status in May 2005.\n",
        "- **PAY_6**: Repayment status in April 2005.\n",
        "\n",
        "#### Bill Statement Amount (BILL_AMT_X):\n",
        "- **BILL_AMT1**: Amount of bill statement in September 2005 (NT dollars).\n",
        "- **BILL_AMT2**: Amount of bill statement in August 2005 (NT dollars).\n",
        "- **BILL_AMT3**: Amount of bill statement in July 2005 (NT dollars).\n",
        "- **BILL_AMT4**: Amount of bill statement in June 2005 (NT dollars).\n",
        "- **BILL_AMT5**: Amount of bill statement in May 2005 (NT dollars).\n",
        "- **BILL_AMT6**: Amount of bill statement in April 2005 (NT dollars).\n",
        "\n",
        "#### Previous Payment Amount (PAY_AMT_X):\n",
        "- **PAY_AMT1**: Amount of previous payment in September 2005 (NT dollars).\n",
        "- **PAY_AMT2**: Amount of previous payment in August 2005 (NT dollars).\n",
        "- **PAY_AMT3**: Amount of previous payment in July 2005 (NT dollars).\n",
        "- **PAY_AMT4**: Amount of previous payment in June 2005 (NT dollars).\n",
        "- **PAY_AMT5**: Amount of previous payment in May 2005 (NT dollars).\n",
        "- **PAY_AMT6**: Amount of previous payment in April 2005 (NT dollars).\n",
        "\n",
        "- **default_payment_next_month**: Default payment indicator (1 = yes, 0 = no).\n",
        "\n",
        "#### Explanation for Feature Reordering:\n",
        "The bill statement and payment amounts are listed in reverse chronological order in the dataset. To ensure that the feature names match the actual sequence of events, we reverse the column names for `BILL_AMT` and `PAY_AMT` features so that they correctly represent the time sequence from April 2005 to September 2005.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Comparison Plot"
      ],
      "metadata": {
        "id": "o7wFqhRaRrvB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from feature_selection_utils import plot_comparison_classification_metrics_seaborn\n",
        "import json\n",
        "import seaborn as sns\n",
        "\n",
        "# Define the files and their respective keys\n",
        "files = ['classification_reports_logreg.json', 'classification_reports_rf.json', 'classification_reports_hist_gb.json']\n",
        "lr = [\"logreg_baseline\", \"logreg_optimal\", \"logreg_optimal_with_threshold_0.4\"]\n",
        "rf = [\"rf_baseline\", \"rf_optimal\", \"rf_optimal_with_threshold_0.35\"]\n",
        "hg = [\"hist_gb_baseline\", \"hist_gb_optimal\", \"histgradient_optimal_with_threshold_0.2\"]\n",
        "\n",
        "# Define a list to hold all the classification reports and model names\n",
        "classification_reports = []\n",
        "model_names = []\n",
        "\n",
        "# Load the classification reports from each JSON file\n",
        "for file, keys, name_prefix in zip(files, [lr, rf, hg], ['Logistic Regression', 'Random Forest', 'HistGradientBoosting']):\n",
        "    with open(file, 'r') as json_file:\n",
        "        results = json.load(json_file)\n",
        "\n",
        "    # Extract reports and names\n",
        "    for key in keys:\n",
        "        classification_reports.append(results[key])\n",
        "        # Format model names\n",
        "        model_name = f'{name_prefix} ' + key.split('_')[-1].replace('baseline', 'Baseline').replace('optimal', 'Optimal Params').replace('threshold', 'with Threshold')\n",
        "        model_names.append(model_name)\n",
        "\n",
        "# Set the seaborn theme and style for better aesthetics\n",
        "sns.set_theme(style=\"whitegrid\", context='notebook', palette=\"Pastel1\")\n",
        "\n",
        "# Plot the comparison of classification metrics across all models\n",
        "plot_comparison_classification_metrics_seaborn(classification_reports, model_names)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pCJY3o-QGm1x",
        "outputId": "c2f818bb-5ec7-4795-defd-253235c03e71"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Comparison Table"
      ],
      "metadata": {
        "id": "5geaSpLsRJpc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "\n",
        "# Create a list to store the results for the table\n",
        "results_list = []\n",
        "\n",
        "# Load the classification reports from each JSON file\n",
        "for file, keys, name_prefix in zip(files, [lr, rf, hg], ['Logistic Regression', 'Random Forest', 'HistGradientBoosting']):\n",
        "    with open(file, 'r') as json_file:\n",
        "        results = json.load(json_file)\n",
        "\n",
        "    # Extract reports and store in the results list\n",
        "    for key in keys:\n",
        "        report = results[key]\n",
        "        model_name = f'{name_prefix} ' + key.split('_')[-1].replace('baseline', 'Baseline').replace('optimal', 'Optimal Params').replace('threshold', 'with Threshold')\n",
        "\n",
        "        # Collect key metrics for the table (can be customized)\n",
        "        results_list.append({\n",
        "            'Model': model_name,\n",
        "            'Precision Class 0': report['0']['precision'],\n",
        "            'Recall Class 0': report['0']['recall'],\n",
        "            'F1-Score Class 0': report['0']['f1-score'],\n",
        "            'Precision Class 1': report['1']['precision'],\n",
        "            'Recall Class 1': report['1']['recall'],\n",
        "            'F1-Score Class 1': report['1']['f1-score'],\n",
        "            'Precision Macro Avg': report['macro avg']['precision'],\n",
        "            'Recall Macro Avg': report['macro avg']['recall'],\n",
        "            'F1-Score Macro Avg': report['macro avg']['f1-score']\n",
        "        })\n",
        "\n",
        "# Convert the results list to a DataFrame for display\n",
        "results_df = pd.DataFrame(results_list)\n",
        "\n",
        "# Display the DataFrame\n",
        "display(results_df)  # For Jupyter Notebooks\n",
        "\n",
        "# Optionally, save the table to a CSV file for further use\n",
        "results_df.to_csv('combined_classification_report_metrics.csv', index=False)\n",
        "\n",
        "# Print the DataFrame to console (optional)\n",
        "# print(results_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "GnCgwd1ALN2K",
        "outputId": "b08a22c7-09b6-4c58-b414-3f730f4c3fbb"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                 Model  Precision Class 0  Recall Class 0  \\\n",
              "0         Logistic Regression Baseline           0.871145        0.817013   \n",
              "1   Logistic Regression Optimal Params           0.892834        0.603385   \n",
              "2              Logistic Regression 0.4           0.892834        0.603385   \n",
              "3               Random Forest Baseline           0.856698        0.885151   \n",
              "4         Random Forest Optimal Params           0.856698        0.883865   \n",
              "5                   Random Forest 0.35           0.883204        0.732376   \n",
              "6        HistGradientBoosting Baseline           0.853200        0.894150   \n",
              "7  HistGradientBoosting Optimal Params           0.847150        0.910864   \n",
              "8             HistGradientBoosting 0.2           0.878202        0.719949   \n",
              "\n",
              "   F1-Score Class 0  Precision Class 1  Recall Class 1  F1-Score Class 1  \\\n",
              "0          0.843211           0.471535        0.574661          0.518015   \n",
              "1          0.720113           0.348010        0.745098          0.474430   \n",
              "2          0.720113           0.348010        0.745098          0.474430   \n",
              "3          0.870692           0.542272        0.478884          0.508610   \n",
              "4          0.870070           0.539898        0.479638          0.507987   \n",
              "5          0.800750           0.411682        0.659125          0.506814   \n",
              "6          0.873195           0.551724        0.458522          0.500824   \n",
              "7          0.877852           0.573333        0.421569          0.485876   \n",
              "8          0.791240           0.396862        0.648567          0.492413   \n",
              "\n",
              "   Precision Macro Avg  Recall Macro Avg  F1-Score Macro Avg  \n",
              "0             0.671340          0.695837            0.680613  \n",
              "1             0.620422          0.674242            0.597271  \n",
              "2             0.620422          0.674242            0.597271  \n",
              "3             0.699485          0.682017            0.689651  \n",
              "4             0.698298          0.681752            0.689028  \n",
              "5             0.647443          0.695751            0.653782  \n",
              "6             0.702462          0.676336            0.687009  \n",
              "7             0.710242          0.666216            0.681864  \n",
              "8             0.637532          0.684258            0.641827  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Precision Class 0</th>\n",
              "      <th>Recall Class 0</th>\n",
              "      <th>F1-Score Class 0</th>\n",
              "      <th>Precision Class 1</th>\n",
              "      <th>Recall Class 1</th>\n",
              "      <th>F1-Score Class 1</th>\n",
              "      <th>Precision Macro Avg</th>\n",
              "      <th>Recall Macro Avg</th>\n",
              "      <th>F1-Score Macro Avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression Baseline</td>\n",
              "      <td>0.871145</td>\n",
              "      <td>0.817013</td>\n",
              "      <td>0.843211</td>\n",
              "      <td>0.471535</td>\n",
              "      <td>0.574661</td>\n",
              "      <td>0.518015</td>\n",
              "      <td>0.671340</td>\n",
              "      <td>0.695837</td>\n",
              "      <td>0.680613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression Optimal Params</td>\n",
              "      <td>0.892834</td>\n",
              "      <td>0.603385</td>\n",
              "      <td>0.720113</td>\n",
              "      <td>0.348010</td>\n",
              "      <td>0.745098</td>\n",
              "      <td>0.474430</td>\n",
              "      <td>0.620422</td>\n",
              "      <td>0.674242</td>\n",
              "      <td>0.597271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Logistic Regression 0.4</td>\n",
              "      <td>0.892834</td>\n",
              "      <td>0.603385</td>\n",
              "      <td>0.720113</td>\n",
              "      <td>0.348010</td>\n",
              "      <td>0.745098</td>\n",
              "      <td>0.474430</td>\n",
              "      <td>0.620422</td>\n",
              "      <td>0.674242</td>\n",
              "      <td>0.597271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest Baseline</td>\n",
              "      <td>0.856698</td>\n",
              "      <td>0.885151</td>\n",
              "      <td>0.870692</td>\n",
              "      <td>0.542272</td>\n",
              "      <td>0.478884</td>\n",
              "      <td>0.508610</td>\n",
              "      <td>0.699485</td>\n",
              "      <td>0.682017</td>\n",
              "      <td>0.689651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Random Forest Optimal Params</td>\n",
              "      <td>0.856698</td>\n",
              "      <td>0.883865</td>\n",
              "      <td>0.870070</td>\n",
              "      <td>0.539898</td>\n",
              "      <td>0.479638</td>\n",
              "      <td>0.507987</td>\n",
              "      <td>0.698298</td>\n",
              "      <td>0.681752</td>\n",
              "      <td>0.689028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest 0.35</td>\n",
              "      <td>0.883204</td>\n",
              "      <td>0.732376</td>\n",
              "      <td>0.800750</td>\n",
              "      <td>0.411682</td>\n",
              "      <td>0.659125</td>\n",
              "      <td>0.506814</td>\n",
              "      <td>0.647443</td>\n",
              "      <td>0.695751</td>\n",
              "      <td>0.653782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>HistGradientBoosting Baseline</td>\n",
              "      <td>0.853200</td>\n",
              "      <td>0.894150</td>\n",
              "      <td>0.873195</td>\n",
              "      <td>0.551724</td>\n",
              "      <td>0.458522</td>\n",
              "      <td>0.500824</td>\n",
              "      <td>0.702462</td>\n",
              "      <td>0.676336</td>\n",
              "      <td>0.687009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>HistGradientBoosting Optimal Params</td>\n",
              "      <td>0.847150</td>\n",
              "      <td>0.910864</td>\n",
              "      <td>0.877852</td>\n",
              "      <td>0.573333</td>\n",
              "      <td>0.421569</td>\n",
              "      <td>0.485876</td>\n",
              "      <td>0.710242</td>\n",
              "      <td>0.666216</td>\n",
              "      <td>0.681864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>HistGradientBoosting 0.2</td>\n",
              "      <td>0.878202</td>\n",
              "      <td>0.719949</td>\n",
              "      <td>0.791240</td>\n",
              "      <td>0.396862</td>\n",
              "      <td>0.648567</td>\n",
              "      <td>0.492413</td>\n",
              "      <td>0.637532</td>\n",
              "      <td>0.684258</td>\n",
              "      <td>0.641827</td>\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_df",
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"HistGradientBoosting Optimal Params\",\n          \"Logistic Regression Optimal Params\",\n          \"Random Forest 0.35\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision Class 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01747700356470118,\n        \"min\": 0.8471502590673575,\n        \"max\": 0.8928344958782498,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.8928344958782498,\n          0.8531997546514005,\n          0.8711446196024675\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall Class 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12325226399031433,\n        \"min\": 0.6033854724662524,\n        \"max\": 0.9108635097493036,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6033854724662524,\n          0.8941504178272981,\n          0.8170130704949646\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-Score Class 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06415478700857143,\n        \"min\": 0.720112517580872,\n        \"max\": 0.8778523489932886,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.720112517580872,\n          0.8731952291274325,\n          0.8432109685979655\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision Class 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09064989254190907,\n        \"min\": 0.3480098626276858,\n        \"max\": 0.5733333333333334,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.3480098626276858,\n          0.5517241379310345,\n          0.4715346534653465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall Class 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12532519582289145,\n        \"min\": 0.4215686274509804,\n        \"max\": 0.7450980392156863,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.7450980392156863,\n          0.45852187028657615,\n          0.5746606334841629\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-Score Class 1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0156933512099187,\n        \"min\": 0.4744297719087635,\n        \"max\": 0.5180149558123726,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.4744297719087635,\n          0.500823723228995,\n          0.5180149558123726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision Macro Avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.036688748494744156,\n        \"min\": 0.6204221792529678,\n        \"max\": 0.7102417962003454,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6204221792529678,\n          0.7024619462912175,\n          0.671339636533907\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall Macro Avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009882378229876296,\n        \"min\": 0.666216068600142,\n        \"max\": 0.6958368519895637,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6742417558409693,\n          0.6763361440569371,\n          0.6958368519895637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-Score Macro Avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03798159358149473,\n        \"min\": 0.5972711447448178,\n        \"max\": 0.6896513565250795,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5972711447448178,\n          0.6870094761782137,\n          0.6806129622051691\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Comparison Analysis\n",
        "\n",
        "The primary concern is Class 1 recall, which measures the model's ability to correctly identify positive instances Class 1 loan defaults.\n",
        "\n",
        "### **1. Logistic Regression**\n",
        "   - **Baseline**:\n",
        "     - Class 1 Recall: **0.574**\n",
        "     - Performance is moderate, correctly identifying about 57.5% of the positive instances.\n",
        "   - **Optimal Params & Threshold 0.4**:\n",
        "     - Class 1 Recall: **0.745**\n",
        "     - Significant improvement in recall (around 74.5%) after tuning parameters and adjusting the decision threshold.\n",
        "     - This improvement comes at the cost of Class 0 recall (0.603) and lower precision for Class 1 (0.348), indicating more false positives.\n",
        "\n",
        "### **2. Random Forest**\n",
        "   - **Baseline**:\n",
        "     - Class 1 Recall: **0.479**\n",
        "     - Lower recall for Class 1 compared to logistic regression baseline, indicating it misses more positive instances.\n",
        "   - **Optimal Params**:\n",
        "     - Class 1 Recall: **0.479**\n",
        "     - Similar to the baseline, tuning hyperparameters didn't improve Class 1 recall.\n",
        "   - **Threshold 0.35**:\n",
        "     - Class 1 Recall: **0.659**\n",
        "     - A noticeable increase in recall when adjusting the threshold, although it is still lower than the tuned logistic regression model.\n",
        "     - Class 1 precision is moderate (0.411), suggesting a better balance between recall and precision than the original models.\n",
        "\n",
        "### **3. HistGradientBoosting**\n",
        "   - **Baseline**:\n",
        "     - Class 1 Recall: **0.459**\n",
        "     - Similar to Random Forest, the baseline recall is relatively low.\n",
        "   - **Optimal Params**:\n",
        "     - Class 1 Recall: **0.422**\n",
        "     - Slight decrease in recall compared to the baseline, indicating that parameter tuning may not have favored recall in this case.\n",
        "   - **Threshold 0.2**:\n",
        "     - Class 1 Recall: **0.649**\n",
        "     - Increasing the recall substantially, but not as high as logistic regression.\n",
        "     - Precision for Class 1 drops to 0.397, suggesting more false positives but still a reasonable trade-off.\n",
        "\n",
        "### **Summary & Interpretation**\n",
        "- **Best Recall for Class 1**: Logistic Regression (with optimal params and threshold 0.4) achieves the highest recall at **0.745**, which means it's the best at identifying positive instances. However, this comes at the expense of lower precision for Class 1 (0.348), indicating an increase in false positives.\n",
        "- **Balanced Models**:\n",
        "  - **Random Forest (Threshold 0.35)**: Offers a good balance with a Class 1 recall of **0.659** and a Class 1 precision of **0.411**. This might be a better option if false positives are a concern.\n",
        "  - **HistGradientBoosting (Threshold 0.2)**: Achieves a Class 1 recall of **0.649** and precision of **0.397**, also providing a balanced approach.\n",
        "- **Trade-offs**: While higher recall is generally beneficial for capturing more positive cases, it often results in lower precision, meaning more false positives. Logistic regression with threshold adjustment is the most aggressive in favoring recall but at a higher cost in precision.\n",
        "\n",
        "### **Recommendation**\n",
        "- If **maximizing recall** is the absolute priority and you can tolerate a higher number of false positives, the **Logistic Regression (Optimal Params with Threshold 0.4)** is the best choice.\n",
        "- If you need a **balance between recall and precision** for Class 1, **Random Forest (Threshold 0.35)** offers a good compromise, achieving a high recall (0.659) with better precision than the logistic regression model.\n",
        "\n",
        "Overall, the choice of the model depends on the acceptable trade-off between recall and precision. If missing positive cases (false negatives) is more critical than incorrectly flagging some negative cases (false positives), then the Logistic Regression model with the threshold is optimal. Otherwise, one of the Random Forest or HistGradientBoosting models with adjusted thresholds might be more suitable."
      ],
      "metadata": {
        "id": "0zt2AoSMQ7hg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot Metrics by Model"
      ],
      "metadata": {
        "id": "ouTN_Q2nFHsR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from loan_data_utils import plot_classification_report_metrics\n",
        "import json\n",
        "\n",
        "# Dictionary to store filenames and their corresponding model keys and titles\n",
        "model_files = {\n",
        "    'classification_reports_logreg.json': {\n",
        "        'keys': ['logreg_baseline', 'logreg_optimal', 'logreg_optimal_with_threshold_0.4'],\n",
        "        'titles': ['Logistic Regression Baseline', 'Logistic Regression Optimal Params', 'Logistic Regression Optimal with Threshold 0.4']\n",
        "    },\n",
        "    'classification_reports_rf.json': {\n",
        "        'keys': ['rf_baseline', 'rf_optimal', 'rf_optimal_with_threshold_0.35'],\n",
        "        'titles': ['Random Forest Baseline', 'Random Forest Optimal Params', 'Random Forest Optimal with Threshold 0.35']\n",
        "    },\n",
        "    'classification_reports_hist_gb.json': {\n",
        "        'keys': ['hist_gb_baseline', 'hist_gb_optimal', 'histgradient_optimal_with_threshold_0.2'],\n",
        "        'titles': ['HistGradientBoosting Baseline', 'HistGradientBoosting Optimal Params', 'HistGradientBoosting Optimal with Threshold 0.2']\n",
        "    }\n",
        "}\n",
        "\n",
        "# Loop through each file, load the data, and plot the metrics for each model\n",
        "for file, model_info in model_files.items():\n",
        "    with open(file, 'r') as json_file:\n",
        "        classification_reports = json.load(json_file)\n",
        "\n",
        "        # Loop through each model key and title to plot the metrics\n",
        "        for key, title in zip(model_info['keys'], model_info['titles']):\n",
        "            plot_classification_report_metrics(classification_reports[key], title)\n"
      ],
      "metadata": {
        "id": "ecFjgTMdlUBm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2f2a682b-7fec-49da-b55b-7ce4c192c2a7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When interpreting the performance metrics from your models, especially the balance between precision and recall for class 1 (default), here are some key points to consider:\n",
        "\n",
        "### 1. **Understanding the Metrics**:\n",
        "- **Precision**: The proportion of true positives among all positive predictions. A high precision means fewer false positives, indicating that when the model predicts a default, it is often correct.\n",
        "- **Recall**: The proportion of true positives identified among all actual positives. High recall indicates that the model successfully identifies most cases of default.\n",
        "- **F1-Score**: The harmonic mean of precision and recall. A high F1-score suggests a balance between precision and recall.\n",
        "\n",
        "In contexts like default prediction, recall (class 1) is often more important than precision because the cost of missing a default (false negative) can be more severe than a false alarm (false positive).\n",
        "\n",
        "### 2. **Stable Performance vs. High Recall**:\n",
        "- **Stable Models**: Some models (e.g., Random Forest Baseline, HistGradientBoosting Baseline) show similar values across precision, recall, and F1-score for both classes. This stability suggests that the model treats both classes more evenly, offering balanced predictions. For instance:\n",
        "  - **HistGradientBoosting Baseline**: Precision and recall for class 0 (non-default) and class 1 (default) are relatively close (0.853 and 0.551, respectively).\n",
        "- **High Recall, Low Precision**: Models like \"Logistic Regression Optimal Params\" show high recall (0.745) but low precision (0.348) for class 1. This means the model identifies most defaults (high recall) but often misclassifies non-defaults as defaults (low precision). This trade-off is usually acceptable if the goal is to catch as many defaults as possible, even at the expense of more false positives.\n",
        "\n",
        "### 3. **Interpreting Which Model to Choose**:\n",
        "The choice of model depends on your primary goal:\n",
        "- **If Identifying Most Defaults (High Recall) is the Goal**: The \"Logistic Regression Optimal Params\" model and \"Logistic Regression 0.4\" have the highest recall (0.745) for class 1, meaning they catch more defaults but with lower precision. This approach is beneficial if missing a default is very costly.\n",
        "- **If Balanced Predictions are Preferred**: Models like \"Random Forest Baseline\" and \"HistGradientBoosting Baseline\" offer a more balanced precision-recall for class 1 (default), suggesting they are less likely to produce excessive false positives while still catching a reasonable amount of defaults.\n",
        "- **If Stability Across Classes is Needed**: Random Forest Baseline and HistGradientBoosting Baseline show balanced performance across class 0 and class 1, which might be preferred in situations where you want a model that performs reasonably well on both non-defaults and defaults.\n",
        "\n",
        "### 4. **Recommended Choice**:\n",
        "Given that your primary concern is **class 1 recall** (catching as many defaults as possible), the \"Logistic Regression Optimal Params\" or \"Logistic Regression 0.4\" might be better choices, despite their lower precision. However, this choice comes with the caveat that there will be more false positives (non-defaults predicted as defaults).\n",
        "\n",
        "If you need a more balanced model that still maintains a reasonable recall for class 1, then **Random Forest Baseline** or **HistGradientBoosting Baseline** could be a more appropriate choice.\n",
        "\n",
        "### Summary:\n",
        "- **High Recall (Class 1) Focus**: \"Logistic Regression Optimal Params\" (recall 0.745).\n",
        "- **Balanced Performance**: \"Random Forest Baseline\" or \"HistGradientBoosting Baseline\".\n",
        "  \n",
        "Ultimately, the best choice depends on whether maximizing recall (catching all defaults) or achieving balanced performance is more aligned with your business needs."
      ],
      "metadata": {
        "id": "V--jS90NUuP1"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3o1VV2ccDa1Z"
      },
      "source": [
        "#### Loan Data Utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0n51ttV4A8y",
        "outputId": "4e5b5aba-d80c-4a8b-d9c9-2fc7a7b93a71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Script successfully written to loan_data_utils.py\n"
          ]
        }
      ],
      "source": [
        "script_content=r'''\n",
        "# loan_data_utils.py\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import logging\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, ClassifierMixin\n",
        "\n",
        "import json\n",
        "\n",
        "# Configure logging\n",
        "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
        "\n",
        "# Define constants\n",
        "target = 'default_payment_next_month'\n",
        "\n",
        "# ------  Define columns  --------- #\n",
        "\n",
        "# column lists from loan data utils\n",
        "primary_columns = ['limit_bal', 'sex', 'education', 'marriage', 'age']\n",
        "bill_columns = ['bill_amt_4_april', 'bill_amt_5_may', 'bill_amt_6_june', 'bill_amt_7_july', 'bill_amt_8_august', 'bill_amt_9_september']\n",
        "pay_columns = ['pay_amt_4_april', 'pay_amt_5_may', 'pay_amt_6_june', 'pay_amt_7_july', 'pay_amt_8_august', 'pay_amt_9_september']\n",
        "delay_columns = ['pay_delay_4_april', 'pay_delay_5_may', 'pay_delay_6_june','pay_delay_7_july', 'pay_delay_8_august', 'pay_delay_9_september']\n",
        "categorical_columns = ['sex', 'marriage']\n",
        "ordinal_columns = delay_columns + ['education']\n",
        "\n",
        "# ---------  Load Data  --------- #\n",
        "\n",
        "def load_data_from_url(url):\n",
        "    try:\n",
        "        df = pd.read_excel(url, header=1)\n",
        "        logging.info(\"Data loaded successfully from URL.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error loading data from URL: {e}\")\n",
        "        return None\n",
        "    return df\n",
        "\n",
        "def split_features_target(df, target):\n",
        "    try:\n",
        "        X = df.drop(columns=[target])\n",
        "        y = df[target]\n",
        "        logging.info(\"Features and target split successfully.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error splitting features and target: {e}\")\n",
        "        return None, None\n",
        "    return X, y\n",
        "\n",
        "\n",
        "def clean_column_names(df):\n",
        "    try:\n",
        "        df.columns = [col.lower().replace(' ', '_') for col in df.columns]\n",
        "        logging.info(\"Column names cleaned successfully.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error cleaning column names: {e}\")\n",
        "    return df\n",
        "\n",
        "def remove_id_column(df):\n",
        "    if 'id' in df.columns:\n",
        "        df = df.drop(columns=['id'])\n",
        "        logging.info(\"ID column removed.\")\n",
        "    return df\n",
        "\n",
        "# ---------  Sex Column Processing  --------- #\n",
        "# ---- SEX: Gender (1 = male, 2 = female) ----#\n",
        "\n",
        "# results in cat.categories = ['Female', 'Male'][0,1]\n",
        "def process_sex_column(df):\n",
        "    try:\n",
        "        if 'sex' in df.columns:\n",
        "            df['sex'] = df['sex'].replace({1: 'Male', 2: 'Female'})\n",
        "            df['sex'] = df['sex'].astype('category')\n",
        "            logging.info(\"Sex column processed and converted to categorical successfully.\")\n",
        "        else:\n",
        "            logging.warning(\"Sex column not found in DataFrame.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error processing sex column: {e}\")\n",
        "    return df\n",
        "\n",
        "# results in cat.categories = ['Female', 'Male'][2,1] (alphabetically ordered)\n",
        "def process_sex_column_cat_codes_one_two(df):\n",
        "    try:\n",
        "        if 'sex' in df.columns:\n",
        "            # Convert numeric values to categorical, maintaining original codes\n",
        "            df['sex'] = df['sex'].astype('category')  # First, ensure the column is categorical\n",
        "\n",
        "            # Define the categories explicitly and map original values (1: Male, 2: Female)\n",
        "            df['sex'].cat.set_categories([1, 2], rename=True, inplace=True)  # Set categories as 1, 2\n",
        "            df['sex'] = df['sex'].replace({1: 'Male', 2: 'Female'})  # Replace numeric values with labels\n",
        "\n",
        "            logging.info(\"Sex column processed and converted to categorical successfully.\")\n",
        "        else:\n",
        "            logging.warning(\"Sex column not found in DataFrame.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error processing sex column: {e}\")\n",
        "    return df\n",
        "\n",
        "# ---------  Marriage Column Processing  --------- #\n",
        "\n",
        "# ---- MARRIAGE: Marital status (1 = married, 2 = single, 3 = other) ----#\n",
        "\n",
        "def process_marriage_column(df):\n",
        "    try:\n",
        "        if 'marriage' in df.columns:\n",
        "            df['marriage'] = df['marriage'].replace({0: 'Unknown/Others', 3: 'Unknown/Others'})\n",
        "            df['marriage'] = df['marriage'].replace({1: 'Married', 2: 'Single'})\n",
        "            df['marriage'] = pd.Categorical(df['marriage'], categories=['Married', 'Single', 'Unknown/Others'], ordered=False)\n",
        "            logging.info(\"Marriage column processed and converted to categorical successfully.\")\n",
        "        else:\n",
        "            logging.warning(\"Marriage column not found in DataFrame.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error processing marriage column: {e}\")\n",
        "    return df\n",
        "\n",
        "# ---------  Target Column Processing  --------- #\n",
        "\n",
        "def process_default_payment_column(df):\n",
        "    try:\n",
        "        if 'default_payment_next_month' in df.columns:\n",
        "            # Label the categories\n",
        "            df['default_payment_next_month'] = df['default_payment_next_month'].replace({0: 'No Default', 1: 'Default'})\n",
        "            df['default_payment_next_month'] = df['default_payment_next_month'].astype('category')\n",
        "            logging.info(\"Default payment column labeled and converted to categorical successfully.\")\n",
        "        else:\n",
        "            logging.warning(\"Default payment column not found in DataFrame.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error processing default payment column: {e}\")\n",
        "    return df\n",
        "\n",
        "\n",
        "# Convert labels to numeric for modeling\n",
        "def convert_default_payment_to_numeric(df):\n",
        "    try:\n",
        "        if 'default_payment_next_month' in df.columns:\n",
        "            df['default_payment_next_month'] = df['default_payment_next_month'].replace({'No Default': 0, 'Default': 1}).astype(int)\n",
        "            logging.info(\"Default payment column converted back to numeric for modeling.\")\n",
        "        else:\n",
        "            logging.warning(\"Default payment column not found in DataFrame.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error converting default payment column to numeric: {e}\")\n",
        "    return df\n",
        "\n",
        "# ---------  Pay Delay Column Processing  --------- #\n",
        "\n",
        "def rename_pay_columns(df):\n",
        "    try:\n",
        "        # Only rename if the original columns exist\n",
        "        if all(col in df.columns for col in ['pay_0', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6']):\n",
        "            pay_columns_new_names = {\n",
        "                'pay_0': 'pay_delay_9_september',\n",
        "                'pay_2': 'pay_delay_8_august',\n",
        "                'pay_3': 'pay_delay_7_july',\n",
        "                'pay_4': 'pay_delay_6_june',\n",
        "                'pay_5': 'pay_delay_5_may',\n",
        "                'pay_6': 'pay_delay_4_april'\n",
        "            }\n",
        "            df = df.rename(columns=pay_columns_new_names)\n",
        "            logging.info(\"Pay delay columns renamed successfully.\")\n",
        "        else:\n",
        "            logging.warning(\"Some or all of the expected pay columns are missing. Renaming skipped.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error renaming pay delay columns: {e}\")\n",
        "    return df\n",
        "\n",
        "def rename_bill_and_payment_columns(df):\n",
        "    try:\n",
        "        # Correct renaming of bill_amt and pay_amt columns\n",
        "        bill_amt_new_names = {\n",
        "            'bill_amt1': 'bill_amt_9_september',\n",
        "            'bill_amt2': 'bill_amt_8_august',\n",
        "            'bill_amt3': 'bill_amt_7_july',\n",
        "            'bill_amt4': 'bill_amt_6_june',\n",
        "            'bill_amt5': 'bill_amt_5_may',\n",
        "            'bill_amt6': 'bill_amt_4_april'\n",
        "        }\n",
        "\n",
        "        pay_amt_new_names = {\n",
        "            'pay_amt1': 'pay_amt_9_september',\n",
        "            'pay_amt2': 'pay_amt_8_august',\n",
        "            'pay_amt3': 'pay_amt_7_july',\n",
        "            'pay_amt4': 'pay_amt_6_june',\n",
        "            'pay_amt5': 'pay_amt_5_may',\n",
        "            'pay_amt6': 'pay_amt_4_april'\n",
        "        }\n",
        "\n",
        "        df = df.rename(columns={**bill_amt_new_names, **pay_amt_new_names})\n",
        "        logging.info(\"Bill and payment columns renamed successfully.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error renaming bill and payment columns: {e}\")\n",
        "    return df\n",
        "\n",
        "\n",
        "def label_pay_columns(df, pay_columns):\n",
        "    try:\n",
        "        # Only label the pay_delay_* columns, not pay_amt_*\n",
        "        pay_labels = {\n",
        "            -2: \"No consumption\",\n",
        "            -1: \"Paid in full\",\n",
        "            0: \"Revolving credit\",\n",
        "            1: \"1 month delay\",\n",
        "            2: \"2 months delay\",\n",
        "            3: \"3 months delay\",\n",
        "            4: \"4 months delay\",\n",
        "            5: \"5 months delay\",\n",
        "            6: \"6 months delay\",\n",
        "            7: \"7 months delay\",\n",
        "            8: \"8 months delay\",\n",
        "            9: \"9+ months delay\"\n",
        "        }\n",
        "\n",
        "        for col in pay_columns:\n",
        "            # Ensure we're only mapping the delay columns and not payment amount columns\n",
        "            if 'pay_delay' in col and col in df.columns:\n",
        "                df[col] = df[col].map(pay_labels)\n",
        "                logging.info(f\"Column {col} labeled successfully.\")\n",
        "            else:\n",
        "                logging.error(f\"Column {col} not found in DataFrame.\")\n",
        "\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error labeling pay columns: {e}\")\n",
        "    return df\n",
        "\n",
        "def convert_pay_columns_to_ordinal(df, pay_columns):\n",
        "    try:\n",
        "        pay_order = [\n",
        "            \"No consumption\",  # -2\n",
        "            \"Paid in full\",    # -1\n",
        "            \"Revolving credit\", # 0\n",
        "            \"1 month delay\",   # 1\n",
        "            \"2 months delay\",  # 2\n",
        "            \"3 months delay\",  # 3\n",
        "            \"4 months delay\",  # 4\n",
        "            \"5 months delay\",  # 5\n",
        "            \"6 months delay\",  # 6\n",
        "            \"7 months delay\",  # 7\n",
        "            \"8 months delay\",  # 8\n",
        "            \"9+ months delay\"  # 9\n",
        "        ]\n",
        "\n",
        "        for col in pay_columns:\n",
        "            # Ensure only delay columns are converted to categorical\n",
        "            if 'pay_delay' in col and col in df.columns:\n",
        "                df[col] = pd.Categorical(df[col], categories=pay_order, ordered=True)\n",
        "                logging.info(f\"Column {col} converted to ordinal successfully.\")\n",
        "            else:\n",
        "                logging.error(f\"Column {col} not found in DataFrame.\")\n",
        "\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error converting pay columns to ordinal categories: {e}\")\n",
        "    return df\n",
        "\n",
        "\n",
        "def convert_ordinal_to_category(df, ordinal_columns):\n",
        "    try:\n",
        "        for col in ordinal_columns:\n",
        "            if col in df.columns:\n",
        "                df[col] = df[col].astype('category')\n",
        "                logging.info(f\"Column {col} converted to category successfully.\")\n",
        "            else:\n",
        "                logging.error(f\"Column {col} not found in DataFrame.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error converting ordinal columns to category: {e}\")\n",
        "    return df\n",
        "\n",
        "# ---------  Education Column Processing  --------- #\n",
        "\n",
        "def convert_education_to_ordinal(df):\n",
        "    try:\n",
        "        # Explicitly map all values to handle known categories\n",
        "        education_mapping = {\n",
        "            1: 'Graduate School',\n",
        "            2: 'University',\n",
        "            3: 'High School',\n",
        "            4: 'Other/Unknown',\n",
        "            5: 'Other/Unknown',\n",
        "            6: 'Other/Unknown',\n",
        "            0: 'Other/Unknown'  # Handle the 0 value as well\n",
        "        }\n",
        "\n",
        "        df['education'] = df['education'].replace(education_mapping)\n",
        "\n",
        "        # Define the order of education categories\n",
        "        education_order = [\n",
        "            \"Other/Unknown\",    # Grouped 0, 4, 5, 6 together\n",
        "            \"High School\",      # 3\n",
        "            \"University\",       # 2\n",
        "            \"Graduate School\"   # 1\n",
        "        ]\n",
        "\n",
        "        if 'education' in df.columns:\n",
        "            # Convert the education column to a categorical type with the specified order\n",
        "            df['education'] = pd.Categorical(df['education'], categories=education_order, ordered=True)\n",
        "            logging.info(\"Education column converted to ordinal categories successfully.\")\n",
        "        else:\n",
        "            logging.error(\"Education column not found in DataFrame.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error converting education column to ordinal categories: {e}\")\n",
        "    return df\n",
        "\n",
        "\n",
        "#-------- Utility and validation functions first ----------#\n",
        "\n",
        "def check_column_integrity(df, expected_columns):\n",
        "    missing_columns = [col for col in expected_columns if col not in df.columns]\n",
        "    if missing_columns:\n",
        "        logging.error(f\"Missing columns: {missing_columns}\")\n",
        "    else:\n",
        "        logging.info(\"All expected columns are present.\")\n",
        "\n",
        "def validate_data_types(df, ordinal_columns):\n",
        "    for col in ordinal_columns:\n",
        "        if col in df.columns:\n",
        "            if not pd.api.types.is_categorical_dtype(df[col]):\n",
        "                logging.warning(f\"Column {col} is not correctly set as categorical.\")\n",
        "            else:\n",
        "                logging.info(f\"Column {col} is correctly set as categorical with the following categories: {df[col].cat.categories}\")\n",
        "        else:\n",
        "            logging.error(f\"Column {col} not found in DataFrame.\")\n",
        "    logging.info(\"Data type validation complete.\")\n",
        "\n",
        "def reorder_columns(df):\n",
        "    try:\n",
        "        # Define the order of the pay_delay columns in chronological order\n",
        "        pay_delay_order = [\n",
        "            'pay_delay_4_april',\n",
        "            'pay_delay_5_may',\n",
        "            'pay_delay_6_june',\n",
        "            'pay_delay_7_july',\n",
        "            'pay_delay_8_august',\n",
        "            'pay_delay_9_september'\n",
        "        ]\n",
        "\n",
        "        # Combine primary, bill, pay, and pay_delay columns with other columns\n",
        "        other_columns = [col for col in df.columns if col not in primary_columns + bill_columns + pay_columns + pay_delay_order]\n",
        "        df = df[primary_columns + bill_columns + pay_columns + pay_delay_order + other_columns]\n",
        "        logging.info(\"Columns reordered successfully.\")\n",
        "    except Exception as e:\n",
        "        logging.error(f\"Error reordering columns: {e}\")\n",
        "    return df\n",
        "\n",
        "\n",
        "#--------  Load and Preprocess Data  --------- #\n",
        "\n",
        "def load_and_preprocess_data(url, categorical_columns, target):\n",
        "    df = load_data_from_url(url)\n",
        "    if df is not None:\n",
        "        try:\n",
        "            # Include the target column from the start\n",
        "            df = clean_column_names(df)\n",
        "            df = remove_id_column(df)\n",
        "            df = rename_pay_columns(df)\n",
        "            df = rename_bill_and_payment_columns(df)\n",
        "            df = label_pay_columns(df, [col for col in df.columns if 'pay_delay' in col])\n",
        "            df = convert_pay_columns_to_ordinal(df, [col for col in df.columns if 'pay_delay' in col])\n",
        "            df = reorder_columns(df)\n",
        "            df = convert_education_to_ordinal(df)\n",
        "            df = process_sex_column(df)\n",
        "            # df = process_sex_column_cat_codes_one_two(df)\n",
        "            df = process_marriage_column(df)\n",
        "            # df = process_default_payment_column(df) # leave as numeric for modeling\n",
        "            df = convert_ordinal_to_category(df, ordinal_columns)\n",
        "\n",
        "            # Run validation checks\n",
        "            check_column_integrity(df, primary_columns + bill_columns + pay_columns + ordinal_columns)\n",
        "            validate_data_types(df, ordinal_columns)\n",
        "\n",
        "            # Drop duplicates consistently across both features and target\n",
        "            df = df.drop_duplicates()\n",
        "\n",
        "            logging.info(\"Data loaded and preprocessed successfully.\")\n",
        "            return df\n",
        "        except Exception as e:\n",
        "            logging.error(f\"Error in data preprocessing: {e}\")\n",
        "            return None\n",
        "    return None\n",
        "\n",
        "#--------  Load Preprocess Split Data  --------- #\n",
        "\n",
        "def load_preprocess_split_data(url, categorical_columns, target):\n",
        "    df = load_data_from_url(url)\n",
        "    if df is not None:\n",
        "        try:\n",
        "            df = clean_column_names(df)\n",
        "            df = remove_id_column(df)\n",
        "            df = rename_pay_columns(df)\n",
        "            df = rename_bill_and_payment_columns(df)\n",
        "            df = label_pay_columns(df, [col for col in df.columns if 'pay_delay' in col])\n",
        "            df = convert_pay_columns_to_ordinal(df, [col for col in df.columns if 'pay_delay' in col])\n",
        "            df = reorder_columns(df)\n",
        "            df = convert_education_to_ordinal(df)\n",
        "            df = process_sex_column(df)  # Process the sex column separately\n",
        "            df = process_marriage_column(df)  # Process the marriage column separately\n",
        "            df = convert_ordinal_to_category(df, ordinal_columns)\n",
        "\n",
        "            # Run validation checks\n",
        "            check_column_integrity(df, primary_columns + bill_columns + pay_columns + ordinal_columns)\n",
        "            validate_data_types(df, ordinal_columns)\n",
        "\n",
        "            X, y = split_features_target(df, target)\n",
        "            logging.info(\"Data loaded and preprocessed successfully.\")\n",
        "            return X, y\n",
        "        except Exception as e:\n",
        "            logging.error(f\"Error in data preprocessing: {e}\")\n",
        "            return None, None\n",
        "    return None, None\n",
        "\n",
        "\n",
        "def check_categorical_order(df, columns):\n",
        "    for col in columns:\n",
        "        if pd.api.types.is_categorical_dtype(df[col]):\n",
        "            print(f\"Column: {col}\")\n",
        "            print(f\"Categories: {df[col].cat.categories}\")\n",
        "            print(f\"Ordered: {df[col].cat.ordered}\\n\")\n",
        "        else:\n",
        "            print(f\"Column: {col} is not categorical.\\n\")\n",
        "\n",
        "#--------  Plot Categorical Data Functions  --------- #\n",
        "\n",
        "import warnings\n",
        "\n",
        "# Suppress the specific FutureWarning\n",
        "warnings.filterwarnings('ignore', category=FutureWarning, module='seaborn')\n",
        "\n",
        "def plot_categorical_distributions(df, categorical_columns):\n",
        "    \"\"\"Function to plot categorical distributions.\"\"\"\n",
        "    plt.figure(figsize=(16, 14))\n",
        "    sns.set_context('talk')\n",
        "    for i, column in enumerate(categorical_columns, 1):\n",
        "        plt.subplot(2, 2, i)\n",
        "        sns.countplot(data=df, x=column, hue=column, palette='Set3', legend=False)\n",
        "        plt.title(f'Distribution of {column}')\n",
        "        plt.xlabel(column)\n",
        "        plt.ylabel('Count')\n",
        "\n",
        "        # Add percentage annotations\n",
        "        total = len(df)\n",
        "        for p in plt.gca().patches:\n",
        "            percentage = 100 * p.get_height() / total\n",
        "            plt.gca().text(p.get_x() + p.get_width() / 2, p.get_height(),\n",
        "                           f'{percentage:.1f}%', ha='center', va='bottom')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "ord_cols = [\n",
        "    'pay_delay_4_april', 'pay_delay_5_may', 'pay_delay_6_june',\n",
        "    'pay_delay_7_july', 'pay_delay_8_august', 'pay_delay_9_september'\n",
        "]\n",
        "\n",
        "def plot_ordinal_distributions(df, ordinal_columns):\n",
        "    \"\"\"Function to plot ordinal distributions using barplot with hue.\"\"\"\n",
        "    plt.figure(figsize=(16, 25))\n",
        "    sns.set_context('talk', font_scale=0.8)\n",
        "    for i, column in enumerate(ord_cols, 1):\n",
        "        plt.subplot(3, 2, i)\n",
        "\n",
        "        # Get the counts for each category\n",
        "        category_counts = df[column].value_counts().sort_index()\n",
        "\n",
        "        sns.barplot(x=category_counts.index, y=category_counts.values, hue=category_counts.index, palette='Set3')\n",
        "\n",
        "        plt.title(f'Distribution of {column}')\n",
        "        plt.xlabel(column)\n",
        "        plt.ylabel('Count')\n",
        "\n",
        "        # Rotate x-tick labels for better legibility\n",
        "        plt.xticks(rotation=45, ha='right')\n",
        "\n",
        "        # Add percentage annotations\n",
        "        total = len(df)\n",
        "        for p in plt.gca().patches:\n",
        "            percentage = 100 * p.get_height() / total\n",
        "            plt.gca().text(p.get_x() + p.get_width() / 2, p.get_height(),\n",
        "                           f'{percentage:.1f}%', ha='center', va='bottom')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# --------- Custom Transformer for Threshold Classifier --------- #\n",
        "\n",
        "class ThresholdClassifier(BaseEstimator, ClassifierMixin):\n",
        "    def __init__(self, base_classifier=None, threshold=0.5):\n",
        "        # Default to LogisticRegression if no classifier is provided\n",
        "        self.base_classifier = base_classifier if base_classifier is not None else LogisticRegression(max_iter=1000)\n",
        "        self.threshold = threshold\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        # Fit the base classifier on the training data\n",
        "        self.base_classifier.fit(X, y)\n",
        "        return self\n",
        "\n",
        "    def predict(self, X):\n",
        "        # Get the predicted probabilities for class 1 (positive class)\n",
        "        probas = self.base_classifier.predict_proba(X)[:, 1]\n",
        "        # Apply the threshold to make class predictions\n",
        "        return (probas >= self.threshold).astype(int)\n",
        "\n",
        "    def predict_proba(self, X):\n",
        "        # Return the predicted probabilities from the base classifier\n",
        "        return self.base_classifier.predict_proba(X)\n",
        "\n",
        "\n",
        "#--------  Classification Report Functions  --------- #\n",
        "\n",
        "# Function to save the classification reports to a JSON file\n",
        "def save_classification_reports(reports, filename='classification_reports.json'):\n",
        "    with open(filename, 'w') as json_file:\n",
        "        json.dump(reports, json_file, indent=4)  # Use indent for pretty-printing\n",
        "    print(f\"Classification reports saved as {filename}\")\n",
        "\n",
        "\n",
        "# Plotting function with annotations\n",
        "def plot_classification_report_metrics(report, model_name):\n",
        "    metrics_df = pd.DataFrame({\n",
        "        'Class': ['Class 0', 'Class 0', 'Class 0', 'Class 1', 'Class 1', 'Class 1'],\n",
        "        'Metric': ['Precision', 'Recall', 'F1-score', 'Precision', 'Recall', 'F1-score'],\n",
        "        'Value': [\n",
        "            report['0']['precision'],\n",
        "            report['0']['recall'],\n",
        "            report['0']['f1-score'],\n",
        "            report['1']['precision'],\n",
        "            report['1']['recall'],\n",
        "            report['1']['f1-score']\n",
        "        ]\n",
        "    })\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    ax = sns.barplot(x='Class', y='Value', hue='Metric', data=metrics_df, palette='mako')\n",
        "    plt.title(f'Classification Report Metrics for {model_name}')\n",
        "    plt.ylabel('Score')\n",
        "    plt.ylim(0, 1)\n",
        "    plt.legend(loc='lower right')\n",
        "\n",
        "    # Annotate the bars with the values\n",
        "    for p in ax.patches:\n",
        "        height = p.get_height()\n",
        "        ax.annotate(f'{height:.2f}',\n",
        "                    (p.get_x() + p.get_width() / 2., height),\n",
        "                    ha='center', va='center',\n",
        "                    xytext=(0, 5),\n",
        "                    textcoords='offset points')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "#--------  Classification Report Functions for Categories  --------- #\n",
        "\n",
        "# Function to save classification reports with custom categories\n",
        "def save_classification_reports_with_categories(reports, filename='classification_reports.json'):\n",
        "    try:\n",
        "        # Adjust the keys for the class labels to match your custom categories\n",
        "        for model, report in reports.items():\n",
        "            if '0' in report and '1' in report:\n",
        "                report['No Default'] = report.pop('0')\n",
        "                report['Default'] = report.pop('1')\n",
        "\n",
        "        with open(filename, 'w') as json_file:\n",
        "            json.dump(reports, json_file, indent=4)  # Pretty-printing for clarity\n",
        "        print(f\"Classification reports with categories saved as {filename}\")\n",
        "    except Exception as e:\n",
        "        print(f\"Error saving classification reports: {e}\")\n",
        "\n",
        "# Updated plotting function with category names and annotations\n",
        "def plot_classification_report_metrics_with_categories(report, model_name):\n",
        "    metrics_df = pd.DataFrame({\n",
        "        'Class': ['No Default', 'No Default', 'No Default', 'Default', 'Default', 'Default'],\n",
        "        'Metric': ['Precision', 'Recall', 'F1-score', 'Precision', 'Recall', 'F1-score'],\n",
        "        'Value': [\n",
        "            report['No Default']['precision'],\n",
        "            report['No Default']['recall'],\n",
        "            report['No Default']['f1-score'],\n",
        "            report['Default']['precision'],\n",
        "            report['Default']['recall'],\n",
        "            report['Default']['f1-score']\n",
        "        ]\n",
        "    })\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    ax = sns.barplot(x='Class', y='Value', hue='Metric', data=metrics_df, palette='mako')\n",
        "    plt.title(f'Classification Report Metrics for {model_name}')\n",
        "    plt.ylabel('Score')\n",
        "    plt.ylim(0, 1)\n",
        "    plt.legend(loc='lower right')\n",
        "\n",
        "    # Annotate the bars with the values\n",
        "    for p in ax.patches:\n",
        "        height = p.get_height()\n",
        "        ax.annotate(f'{height:.2f}',\n",
        "                    (p.get_x() + p.get_width() / 2., height),\n",
        "                    ha='center', va='center',\n",
        "                    xytext=(0, 5),\n",
        "                    textcoords='offset points')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "'''\n",
        "\n",
        "# Write the script to a file\n",
        "with open(\"loan_data_utils.py\", \"w\") as file:\n",
        "    file.write(script_content)\n",
        "\n",
        "print(\"Script successfully written to loan_data_utils.py\")\n",
        "# Reload script to make functions available for use\n",
        "import importlib\n",
        "import loan_data_utils\n",
        "importlib.reload(loan_data_utils)\n",
        "\n",
        "from loan_data_utils import *\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONYYdNCIMlxu"
      },
      "source": [
        "#### EDA Utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOWzifWM6fRP",
        "outputId": "521649a7-9e73-4eac-dc38-b35cefab6449"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Script successfully written to eda_utils.py\n"
          ]
        }
      ],
      "source": [
        "script_content=r'''\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#--------   Data Overview   --------#\n",
        "\n",
        "def data_overview(X):\n",
        "    # Calculate missing values and their percentage\n",
        "    missing_values = X.isnull().sum().sum()\n",
        "    missing_percentage = (missing_values / len(X)) * 100\n",
        "\n",
        "    # Calculate the number of duplicate rows\n",
        "    duplicate_rows = X.duplicated().sum()\n",
        "\n",
        "    # Print missing values, missing percentage, and duplicate information\n",
        "    print(\"Missing Values:\", missing_values)\n",
        "    print(\"Missing Percentage:\", missing_percentage)\n",
        "    print(f\"Number of Duplicate Rows: {duplicate_rows}\\n\")\n",
        "\n",
        "    # Print basic information\n",
        "    X.info()\n",
        "\n",
        "#--------   Plot Class Distribution   --------#\n",
        "\n",
        "def plot_class_distribution(y_train, target_name):\n",
        "    sns.set()\n",
        "    sns.set(style=\"ticks\")\n",
        "    sns.set_context('notebook')\n",
        "    plt.figure(figsize=(8, 5))\n",
        "    sns.countplot(x=y_train, hue=y_train, palette='viridis')\n",
        "    plt.title(f'Class Distribution in Training Set: {target_name}')\n",
        "    plt.xlabel('Class')\n",
        "    plt.ylabel('Count')\n",
        "    plt.legend([], [], frameon=False)\n",
        "\n",
        "    # Calculate the percentage for each class\n",
        "    total = len(y_train)\n",
        "    class_counts = y_train.value_counts()\n",
        "    for i, count in enumerate(class_counts):\n",
        "        percentage = 100 * count / total\n",
        "        plt.text(i, count, f'{percentage:.1f}%', ha='center', va='bottom')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "#--------   Plot Univariate Distributions   --------#\n",
        "\n",
        "def plot_univariate_distributions(X, categorical_columns):\n",
        "    numeric_columns = X.select_dtypes(include=[np.number]).columns\n",
        "    sns.set(style=\"ticks\")\n",
        "    sns.set_context('talk')\n",
        "\n",
        "    # Plot histograms and KDE plots for numeric features\n",
        "    for col in numeric_columns:\n",
        "        plt.figure(figsize=(14, 5))\n",
        "\n",
        "        # Histogram and KDE plot\n",
        "        plt.subplot(1, 2, 1)\n",
        "        sns.histplot(X[col], kde=True, color='forestgreen')\n",
        "        plt.title(f'Distribution of {col}')\n",
        "        plt.xlabel(col)\n",
        "\n",
        "        # Boxplot to detect outliers\n",
        "        plt.subplot(1, 2, 2)\n",
        "        sns.boxplot(x=X[col], color='limegreen')\n",
        "        plt.title(f'Boxplot of {col}')\n",
        "        plt.xlabel(col)\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    # Plot bar plots for other categorical features\n",
        "    for col in categorical_columns:\n",
        "        plt.figure(figsize=(8, 5))\n",
        "        sns.countplot(x=X[col], hue=X[col], palette='viridis')\n",
        "        plt.title(f'Category Distribution of {col}')\n",
        "        plt.xlabel(col)\n",
        "        plt.ylabel('Count')\n",
        "        plt.show()\n",
        "\n",
        "def plot_pay_delay_distributions(X, pay_delay_columns, aggregate_threshold=10):\n",
        "    sns.set(style=\"ticks\")\n",
        "    sns.set_context('talk')\n",
        "\n",
        "    for col in pay_delay_columns:\n",
        "        # Aggregate categories with low counts into a single \"Other\" category\n",
        "        value_counts = X[col].value_counts()\n",
        "        low_count_categories = value_counts[value_counts < aggregate_threshold].index\n",
        "        X[col] = X[col].apply(lambda x: 'Other' if x in low_count_categories else x)\n",
        "\n",
        "        plt.figure(figsize=(10, 5))\n",
        "\n",
        "        # Count plot for pay_delay columns\n",
        "        sns.countplot(x=X[col], hue=X[col], palette='viridis', order=value_counts.index)\n",
        "        plt.title(f'Distribution of {col.replace(\"_\", \" \").title()} (Aggregated)')\n",
        "        plt.xlabel(col)\n",
        "        plt.ylabel('Count')\n",
        "\n",
        "        # Rotate the x-axis labels for better visibility\n",
        "        plt.xticks(rotation=45)\n",
        "        plt.show()\n",
        "\n",
        "'''\n",
        "\n",
        "# Write the script to a file\n",
        "with open(\"eda_utils.py\", \"w\") as file:\n",
        "    file.write(script_content)\n",
        "\n",
        "print(\"Script successfully written to eda_utils.py\")\n",
        "# Reload script to make functions available for use\n",
        "import importlib\n",
        "import eda_utils\n",
        "importlib.reload(eda_utils)\n",
        "\n",
        "from eda_utils import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqAvpP5-XbHn"
      },
      "source": [
        "#### Data Transform Utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0pD1L5wpiYv",
        "outputId": "1e442de0-95b2-448f-aff2-c61ad78eb2dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data_transformations_utils.py created successfully\n"
          ]
        }
      ],
      "source": [
        "script_content=r'''\n",
        "\n",
        "# data_transformations_utils.py\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import mstats\n",
        "from sklearn.preprocessing import RobustScaler, PowerTransformer, QuantileTransformer\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "import json\n",
        "import logging\n",
        "\n",
        "# Function to evaluate the baseline model\n",
        "def evaluate_baseline_model(preprocessor, X_train, X_test, y_train, y_test):\n",
        "    pipeline = Pipeline([\n",
        "        ('preprocessor', preprocessor),\n",
        "        ('classifier', LogisticRegression(max_iter=1000, random_state=42))\n",
        "    ])\n",
        "    pipeline.fit(X_train, y_train)\n",
        "    y_pred = pipeline.predict(X_test)\n",
        "    report = classification_report(y_test, y_pred, output_dict=True)\n",
        "    return report\n",
        "\n",
        "# Function to apply Winsorization (Capping)\n",
        "def winsorize_transform(X):\n",
        "    X_transformed = X.copy()\n",
        "    for col in X_transformed.columns:\n",
        "        X_transformed[col] = mstats.winsorize(X_transformed[col], limits=[0.05, 0.05])\n",
        "    return X_transformed\n",
        "\n",
        "# Function to apply the Combined Method: IQR + Winsorization + Robust Scaling\n",
        "def combined_outlier_removal(X):\n",
        "    # Step 1: Apply IQR method to remove outliers\n",
        "    def apply_iqr_outlier_removal(df):\n",
        "        df_cleaned = df.copy()\n",
        "        for col in df_cleaned.columns:\n",
        "            Q1 = df_cleaned[col].quantile(0.25)\n",
        "            Q3 = df_cleaned[col].quantile(0.75)\n",
        "            IQR = Q3 - Q1\n",
        "            lower_bound = Q1 - 1.5 * IQR\n",
        "            upper_bound = Q3 + 1.5 * IQR\n",
        "            df_cleaned = df_cleaned[(df_cleaned[col] >= lower_bound) & (df_cleaned[col] <= upper_bound)]\n",
        "        return df_cleaned\n",
        "\n",
        "    # Step 2: Apply Winsorization to cap extreme values\n",
        "    def apply_winsorization(df):\n",
        "        df_winsorized = df.copy()\n",
        "        for col in df_winsorized.columns:\n",
        "            df_winsorized[col] = mstats.winsorize(df_winsorized[col], limits=[0.05, 0.05])\n",
        "        return df_winsorized\n",
        "\n",
        "    # Step 3: Apply RobustScaler to normalize the data\n",
        "    def apply_robust_scaler(df):\n",
        "        scaler = RobustScaler()\n",
        "        return pd.DataFrame(scaler.fit_transform(df), columns=df.columns)\n",
        "\n",
        "    # Apply all the steps in sequence\n",
        "    X_cleaned = apply_iqr_outlier_removal(X)\n",
        "    X_winsorized = apply_winsorization(X_cleaned)\n",
        "    X_scaled = apply_robust_scaler(X_winsorized)\n",
        "\n",
        "    return X_scaled\n",
        "\n",
        "\n",
        "# Function to create the preprocessing pipeline\n",
        "def create_preprocessor(ordinal_columns, categorical_columns, numeric_columns, categories_for_encoder):\n",
        "    preprocessor = ColumnTransformer(\n",
        "        transformers=[\n",
        "            ('ordinal', Pipeline([\n",
        "                ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "                ('encoder', OrdinalEncoder(categories=categories_for_encoder))\n",
        "            ]), ordinal_columns),\n",
        "\n",
        "            ('categorical', Pipeline([\n",
        "                ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "                ('encoder', OneHotEncoder(drop='first', handle_unknown='ignore'))\n",
        "            ]), categorical_columns),\n",
        "\n",
        "            ('numeric', Pipeline([\n",
        "                ('imputer', SimpleImputer(strategy='median')),\n",
        "                ('scaler', StandardScaler())\n",
        "            ]), numeric_columns)\n",
        "        ],\n",
        "        remainder='passthrough'\n",
        "    )\n",
        "    return preprocessor\n",
        "\n",
        "# Function to evaluate models with different transformations\n",
        "def evaluate_method(preprocessor, X_train, X_test, y_train, y_test, method_name, classification_reports, pay_columns, transformation):\n",
        "    logging.info(f\"Evaluating method: {method_name}\")\n",
        "\n",
        "    # Apply the transformation only to the specified pay_columns\n",
        "    transformed_train = transformation(X_train[pay_columns])\n",
        "    transformed_test = transformation(X_test[pay_columns])\n",
        "\n",
        "    # Replace the original pay columns with the transformed data\n",
        "    X_train_transformed = X_train.copy()\n",
        "    X_test_transformed = X_test.copy()\n",
        "    X_train_transformed[pay_columns] = transformed_train\n",
        "    X_test_transformed[pay_columns] = transformed_test\n",
        "\n",
        "    # Build the pipeline\n",
        "    pipeline = Pipeline([\n",
        "        ('preprocessor', preprocessor),\n",
        "        ('classifier', LogisticRegression(max_iter=1000, random_state=42))\n",
        "    ])\n",
        "\n",
        "    # Train and evaluate the model\n",
        "    pipeline.fit(X_train_transformed, y_train)\n",
        "    y_pred = pipeline.predict(X_test_transformed)\n",
        "    report = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "    # Save the report in the dictionary under the method name\n",
        "    classification_reports[method_name] = report\n",
        "\n",
        "# Log transformation function for pay columns\n",
        "def log_transform(X, pay_columns):\n",
        "    X_transformed = X.copy()\n",
        "    for col in pay_columns:\n",
        "        # Apply log1p transformation and handle any potential NaN/Inf values\n",
        "        X_transformed[col] = np.log1p(X_transformed[col].replace([np.inf, -np.inf], np.nan)).fillna(0)\n",
        "    return X_transformed[pay_columns]\n",
        "\n",
        "'''\n",
        "\n",
        "with open('data_transformations_utils.py', 'w') as file:\n",
        "    file.write(script_content)\n",
        "\n",
        "print(\"data_transformations_utils.py created successfully\")\n",
        "# Reload script to make functions available for use\n",
        "import importlib\n",
        "import data_transformations_utils\n",
        "importlib.reload(data_transformations_utils)\n",
        "\n",
        "from data_transformations_utils import *\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Feature Engineering Script"
      ],
      "metadata": {
        "id": "jTdy0ysdGptt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9TfChdMGhTF",
        "outputId": "c669ae2f-1cf7-4a9c-c80a-cd288392cdbb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Script successfully written to feature_engineering_pipeline_utils.py\n"
          ]
        }
      ],
      "source": [
        "script_content = r'''\n",
        "# feature_engineering_pipeline_utils.py\n",
        "\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from scipy.stats import mstats\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# -----------  Define the columns All Engineered Features ------------#\n",
        "\n",
        "# numeric_features = ['age', 'limit_bal', 'bill_amt_4_april', 'bill_amt_5_may', 'bill_amt_6_june',\n",
        "#                     'bill_amt_7_july', 'bill_amt_8_august', 'bill_amt_9_september', 'pay_amt_4_april',\n",
        "#                     'pay_amt_5_may', 'pay_amt_6_june', 'pay_amt_7_july', 'pay_amt_8_august', 'pay_amt_9_september',\n",
        "#                     'severe_delay_count', 'cumulative_delay', 'severe_delay_payment_ratio', 'avg_payment_severe_delays',\n",
        "#                     'total_payments', 'avg_payment', 'payment_std', 'weighted_payment_sum', 'total_bills', 'debt_to_limit_ratio',\n",
        "#                     'rolling_avg_delay', 'education_risk_ratio', 'marriage_risk_ratio', 'education_woe', 'marriage_woe']\n",
        "\n",
        "# # Categorical Features (New categorical feature added)\n",
        "# categorical_features = ['sex', 'marriage', 'education_marriage', 'cumulative_delay_category']\n",
        "\n",
        "# # Ordinal Features\n",
        "# ordinal_features = delay_columns + ['education']\n",
        "\n",
        "# # Boolean Features (New flags added)\n",
        "# boolean_features = ['high_risk_delay', 'deferred_and_decreasing_payments', 'severe_deferred_and_decreasing_payments',\n",
        "#                     'severe_deferred_and_any_decreasing_payments', 'education_or_marriage_unknown_flag',\n",
        "#                     'education_and_marriage_unknown_flag']\n",
        "\n",
        "# -----------  Define the columns LogReg & RF Engineered Features ------------#\n",
        "\n",
        "\n",
        "\n",
        "# ------- Outlier Removal Transformer Winsor Robust Returns Pandas DataFrame --------#\n",
        "\n",
        "class WinsorRobustOutlierRemovalTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, columns):\n",
        "        self.columns = columns\n",
        "        self.scaler = RobustScaler()\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        self.scaler.fit(X[self.columns])\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        X_transformed = X.copy()\n",
        "\n",
        "        # Apply Winsorization (capping)\n",
        "        for col in self.columns:\n",
        "            X_transformed[col] = mstats.winsorize(X_transformed[col], limits=[0.05, 0.05])\n",
        "\n",
        "        # Apply Robust Scaling\n",
        "        X_transformed[self.columns] = self.scaler.transform(X_transformed[self.columns])\n",
        "\n",
        "        # Convert back to DataFrame if X was a DataFrame\n",
        "        if isinstance(X, pd.DataFrame):\n",
        "            X_transformed = pd.DataFrame(X_transformed, columns=X.columns, index=X.index)\n",
        "\n",
        "        return X_transformed\n",
        "\n",
        "# ------- Rolling Average Delay Transformer -------#\n",
        "\n",
        "class RollingAverageDelayTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns):\n",
        "        \"\"\"\n",
        "        Initialize the transformer with the columns to calculate the rolling average delay.\n",
        "        \"\"\"\n",
        "        self.delay_columns = delay_columns\n",
        "        # Embedded delay mapping inside the transformer\n",
        "        self.delay_mapping = {\n",
        "            'No consumption': 0,\n",
        "            'Paid in full': 0,\n",
        "            'Revolving credit': 0,\n",
        "            '1 month delay': 1,\n",
        "            '2 months delay': 2,\n",
        "            '3 months delay': 3,\n",
        "            '4 months delay': 4,\n",
        "            '5 months delay': 5,\n",
        "            '6 months delay': 6,\n",
        "            '7 months delay': 7,\n",
        "            '8 months delay': 8,\n",
        "            '9+ months delay': 9\n",
        "        }\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        # No fitting necessary, so we simply return the transformer.\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        \"\"\"\n",
        "        Transforms the input DataFrame by calculating the rolling average delay,\n",
        "        without modifying the original delay columns.\n",
        "\n",
        "        Parameters:\n",
        "        - X: pandas DataFrame.\n",
        "\n",
        "        Returns:\n",
        "        - Transformed DataFrame with the original columns and an additional\n",
        "          'rolling_avg_delay' column.\n",
        "        \"\"\"\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Use apply with map for each column instead of applymap\n",
        "        delay_numeric = X[self.delay_columns].apply(lambda col: col.map(self.delay_mapping).astype(float))\n",
        "\n",
        "        # Handle potential missing values after mapping (optional)\n",
        "        delay_numeric = delay_numeric.fillna(0)\n",
        "\n",
        "        # Calculate the rolling average delay and add it as a new feature in the original DataFrame\n",
        "        X['rolling_avg_delay'] = delay_numeric.mean(axis=1)\n",
        "\n",
        "        return X  # Return the DataFrame with the original and new feature\n",
        "\n",
        "\n",
        "\n",
        "# ------- High Risk Delay Feature Transformer -------#\n",
        "\n",
        "class HighRiskDelayFeatureTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Generate the 'high_risk_delay' feature\n",
        "        X['high_risk_delay'] = X[self.delay_columns].apply(\n",
        "            lambda row: any(delay in ['3 months delay', '4 months delay', '5 months delay',\n",
        "                                      '6 months delay', '7 months delay', '8 months delay',\n",
        "                                      '9+ months delay'] for delay in row),\n",
        "            axis=1\n",
        "        )\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Severe Delay Count Transformer -------#\n",
        "\n",
        "class SevereDelayCountTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Generate the 'severe_delay_count' feature\n",
        "        X['severe_delay_count'] = X[self.delay_columns].apply(\n",
        "            lambda row: sum(1 for delay in row if delay in ['3 months delay', '4 months delay',\n",
        "                                                            '5 months delay', '6 months delay',\n",
        "                                                            '7 months delay', '8 months delay',\n",
        "                                                            '9+ months delay']),\n",
        "            axis=1\n",
        "        )\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Cumulative Delay Transformer -------#\n",
        "\n",
        "class CumulativeDelayTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "        # Embedded delay mapping inside the transformer\n",
        "        self.delay_mapping = {\n",
        "            'No consumption': 0,\n",
        "            'Paid in full': 0,\n",
        "            'Revolving credit': 0,\n",
        "            '1 month delay': 1,\n",
        "            '2 months delay': 2,\n",
        "            '3 months delay': 3,\n",
        "            '4 months delay': 4,\n",
        "            '5 months delay': 5,\n",
        "            '6 months delay': 6,\n",
        "            '7 months delay': 7,\n",
        "            '8 months delay': 8,\n",
        "            '9+ months delay': 9\n",
        "        }\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        # No fitting necessary, so we simply return the transformer.\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Create a new DataFrame for delay columns mapped to numeric values\n",
        "        # delay_numeric = X[self.delay_columns].applymap(self.delay_mapping.get).astype(float)\n",
        "        delay_numeric = X[self.delay_columns].apply(lambda col: col.map(self.delay_mapping).astype(float))\n",
        "\n",
        "\n",
        "        # Handle potential missing values after mapping (optional)\n",
        "        delay_numeric = delay_numeric.fillna(0)\n",
        "\n",
        "        # Generate the 'cumulative_delay' feature by summing the numeric values across the delay columns\n",
        "        X['cumulative_delay'] = delay_numeric.sum(axis=1)\n",
        "\n",
        "        # Return the original DataFrame with the new 'cumulative_delay' feature added\n",
        "        return X\n",
        "\n",
        "# ------- Cumulative Delay Binning Transformer -------#\n",
        "\n",
        "class CumulativeDelayBinningTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, column='cumulative_delay', bins=[-np.inf, 0, 5, 10, 15, np.inf], labels=['No Delay', 'Low Delay', 'Moderate Delay', 'High Delay', 'Severe Delay']):\n",
        "        self.column = column\n",
        "        self.bins = bins\n",
        "        self.labels = labels\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Apply binning to the cumulative_delay column\n",
        "        X['cumulative_delay_category'] = pd.cut(X[self.column], bins=self.bins, labels=self.labels, ordered=True)\n",
        "\n",
        "        # Return the updated DataFrame with the new 'cumulative_delay_category' column\n",
        "        return X\n",
        "\n",
        "\n",
        "# ------- Avg Payment Severe Delays Transformer -------#\n",
        "\n",
        "class AvgPaymentSevereDelaysTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self):\n",
        "        pass  # No parameters to initialize for this transformer\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Define severe delay categories\n",
        "        delays = ['3 months delay', '4 months delay', '5 months delay', '6 months delay',\n",
        "                  '7 months delay', '8 months delay', '9+ months delay']\n",
        "\n",
        "        # Calculate the average payment for severe delays\n",
        "        def calculate_avg_payment_for_severe_delays(row):\n",
        "            total_payment = 0\n",
        "            severe_delay_months = 0\n",
        "            for i, month in enumerate(['april', 'may', 'june', 'july', 'august', 'september']):\n",
        "                if row[f'pay_delay_{i+4}_{month}'] in delays:\n",
        "                    total_payment += row[f'pay_amt_{i+4}_{month}']\n",
        "                    severe_delay_months += 1\n",
        "            return total_payment / severe_delay_months if severe_delay_months > 0 else 0\n",
        "\n",
        "        # Apply the function to each row\n",
        "        X['avg_payment_severe_delays'] = X.apply(calculate_avg_payment_for_severe_delays, axis=1)\n",
        "\n",
        "        return X\n",
        "\n",
        "\n",
        "# ------- Cumulative Delay Transformer -------#\n",
        "\n",
        "class CumulativeDelayTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "        self.delay_mapping = {\n",
        "            'No consumption': 0,\n",
        "            'Paid in full': 0,\n",
        "            'Revolving credit': 0,\n",
        "            '1 month delay': 1,\n",
        "            '2 months delay': 2,\n",
        "            '3 months delay': 3,\n",
        "            '4 months delay': 4,\n",
        "            '5 months delay': 5,\n",
        "            '6 months delay': 6,\n",
        "            '7 months delay': 7,\n",
        "            '8 months delay': 8,\n",
        "            '9+ months delay': 9\n",
        "        }\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Generate the 'cumulative_delay' feature\n",
        "        X['cumulative_delay'] = X[self.delay_columns].apply(\n",
        "            lambda row: sum(self.delay_mapping.get(delay, 0) for delay in row),\n",
        "            axis=1\n",
        "        ).astype(int)\n",
        "\n",
        "        return X\n",
        "\n",
        "\n",
        "# ------- Severe Delay Payment Ratio Transformer -------#\n",
        "\n",
        "class SevereDelayPaymentRatioTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, pay_columns, scaling_factor=10000):\n",
        "        self.pay_columns = pay_columns\n",
        "        self.scaling_factor = scaling_factor\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Calculate the total payment across the specified columns\n",
        "        X['total_payment'] = X[self.pay_columns].sum(axis=1)\n",
        "\n",
        "        # Calculate the severe delay payment ratio\n",
        "        X['severe_delay_payment_ratio'] = X.apply(\n",
        "            lambda row: (row['severe_delay_count'] / row['total_payment'] * self.scaling_factor)\n",
        "            if row['total_payment'] > 0 else 0,\n",
        "            axis=1\n",
        "        )\n",
        "\n",
        "        # Drop the temporary 'total_payment' column if desired\n",
        "        X.drop(columns=['total_payment'], inplace=True)\n",
        "\n",
        "        return X\n",
        "\n",
        "\n",
        "# ------- Deferred & Decreasing Payments Transformer -------#\n",
        "\n",
        "class DeferredAndDecreasingPaymentsTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns, pay_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "        self.pay_columns = pay_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Generate the 'deferred_and_decreasing_payments' feature\n",
        "        X['deferred_and_decreasing_payments'] = X.apply(self._check_deferred_and_decreasing, axis=1)\n",
        "\n",
        "        return X\n",
        "\n",
        "    def _check_deferred_and_decreasing(self, row):\n",
        "        delays = ['1 month delay', '2 months delay', '3 months delay',\n",
        "                  '4 months delay', '5 months delay', '6 months delay',\n",
        "                  '7 months delay', '8 months delay', '9+ months delay']\n",
        "\n",
        "        # Initialize list to store payments in months with any delay\n",
        "        delay_payments = []\n",
        "\n",
        "        # Iterate over the months and collect payments for any delay\n",
        "        for i, month in enumerate(['april', 'may', 'june', 'july', 'august', 'september']):\n",
        "            if row[f'pay_delay_{i+4}_{month}'] in delays:\n",
        "                delay_payments.append(row[f'pay_amt_{i+4}_{month}'])\n",
        "\n",
        "        # Check if payments are decreasing over time\n",
        "        for j in range(1, len(delay_payments)):\n",
        "            if delay_payments[j] > delay_payments[j-1]:\n",
        "                return False  # Payments are not consistently decreasing\n",
        "\n",
        "        return True if len(delay_payments) > 1 else False  # Return True if payments are decreasing\n",
        "\n",
        "\n",
        "# ------- Severe Deferred & Decreasing Payments Transformer -------#\n",
        "\n",
        "class SevereDeferredAndDecreasingPaymentsTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns, pay_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "        self.pay_columns = pay_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Generate the 'severe_deferred_and_decreasing_payments' feature\n",
        "        X['severe_deferred_and_decreasing_payments'] = X.apply(self._check_severe_deferred_and_decreasing, axis=1)\n",
        "\n",
        "        return X\n",
        "\n",
        "    def _check_severe_deferred_and_decreasing(self, row):\n",
        "        delays = ['3 months delay', '4 months delay', '5 months delay',\n",
        "                  '6 months delay', '7 months delay', '8 months delay', '9+ months delay']\n",
        "\n",
        "        # Check if there are deferred payments\n",
        "        deferred = any(row[f'pay_delay_{i+4}_{month}'] in delays for i, month in enumerate(['april', 'may', 'june', 'july', 'august', 'september']))\n",
        "\n",
        "        # Check if payments are decreasing\n",
        "        payments = [row[f'pay_amt_{i+4}_{month}'] for i, month in enumerate(['april', 'may', 'june', 'july', 'august', 'september'])]\n",
        "        decreasing = all(x >= y for x, y in zip(payments, payments[1:]))\n",
        "\n",
        "        # Return True if both conditions are met\n",
        "        return deferred and decreasing\n",
        "\n",
        "\n",
        "# ------- Severe Deferred & Any Decreasing Payments Transformer -------#\n",
        "\n",
        "class SevereDeferredAndAnyDecreasingPaymentsTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns, pay_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "        self.pay_columns = pay_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Generate the 'severe_deferred_and_any_decreasing_payments' feature\n",
        "        X['severe_deferred_and_any_decreasing_payments'] = X.apply(self._check_severe_deferred_and_any_decreasing, axis=1)\n",
        "\n",
        "        return X\n",
        "\n",
        "    def _check_severe_deferred_and_any_decreasing(self, row):\n",
        "        delays = ['3 months delay', '4 months delay', '5 months delay',\n",
        "                  '6 months delay', '7 months delay', '8 months delay', '9+ months delay']\n",
        "\n",
        "        # Check if there are severe deferred payments\n",
        "        deferred = any(row[f'pay_delay_{i+4}_{month}'] in delays for i, month in enumerate(['april', 'may', 'june', 'july', 'august', 'september']))\n",
        "\n",
        "        # Check if payments are decreasing (any decrease)\n",
        "        payments = [row[f'pay_amt_{i+4}_{month}'] for i, month in enumerate(['april', 'may', 'june', 'july', 'august', 'september'])]\n",
        "        decreasing = any(x > y for x, y in zip(payments, payments[1:]))  # Any decrease in payments\n",
        "\n",
        "        # Return True if both conditions are met\n",
        "        return deferred and decreasing\n",
        "\n",
        "# ------- Bill To Payment Ratio Transformer -------#\n",
        "\n",
        "class BillToPaymentRatioTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, month):\n",
        "        self.month = month\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Define the column names based on the month\n",
        "        ratio_column = f'bill_to_payment_ratio_{self.month}'\n",
        "        bill_column = f'bill_amt_{self.month}'\n",
        "        pay_column = f'pay_amt_{self.month}'\n",
        "\n",
        "        # Calculate the bill-to-payment ratio\n",
        "        X[ratio_column] = X[bill_column] / X[pay_column].replace(0, np.nan)\n",
        "        X[ratio_column] = X[ratio_column].fillna(0)\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Total Payments Transformer -------#\n",
        "\n",
        "class TotalPaymentsTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, pay_columns):\n",
        "        self.pay_columns = pay_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Calculate the total payments across the specified columns\n",
        "        X['total_payments'] = X[self.pay_columns].sum(axis=1)\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Average Payment Transformer -------#\n",
        "\n",
        "class AvgPaymentTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, total_payments_column='total_payments', num_months=6):\n",
        "        self.total_payments_column = total_payments_column\n",
        "        self.num_months = num_months\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Calculate the average payment\n",
        "        X['avg_payment'] = X[self.total_payments_column] / self.num_months\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Standard Deviation Transformer -------#\n",
        "\n",
        "class PaymentStdTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, pay_columns):\n",
        "        self.pay_columns = pay_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Calculate the standard deviation of payments across the specified columns\n",
        "        X['payment_std'] = X[self.pay_columns].std(axis=1)\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Weighted Payment Sum Transformer -------#\n",
        "\n",
        "class WeightedPaymentSumTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, pay_columns, weights=None):\n",
        "        self.pay_columns = pay_columns\n",
        "        self.weights = weights if weights is not None else [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6]  # Default weights\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Calculate the weighted payment sum across the specified columns\n",
        "        X['weighted_payment_sum'] = (X[self.pay_columns] * self.weights).sum(axis=1)\n",
        "\n",
        "        # Return the DataFrame with all original columns plus the new feature\n",
        "        return X\n",
        "\n",
        "\n",
        "# ------- Debt To Limit Ratio Transformer -------#\n",
        "\n",
        "class TotalBillsTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, bill_columns):\n",
        "        self.bill_columns = bill_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Calculate the total bills across the specified columns\n",
        "        X['total_bills'] = X[self.bill_columns].sum(axis=1)\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Debt To Limit Ratio Transformer -------#\n",
        "\n",
        "class DebtToLimitRatioTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, total_bills_column='total_bills', limit_bal_column='limit_bal'):\n",
        "        self.total_bills_column = total_bills_column\n",
        "        self.limit_bal_column = limit_bal_column\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Handle cases where limit_bal is zero to avoid division by zero\n",
        "        epsilon = 1e-10  # Small value to avoid division by zero\n",
        "        X['debt_to_limit_ratio'] = X[self.total_bills_column] / (X[self.limit_bal_column] + epsilon)\n",
        "\n",
        "        # Handle infinite values without using inplace\n",
        "        X['debt_to_limit_ratio'] = X['debt_to_limit_ratio'].replace([float('inf'), -float('inf')], 0)\n",
        "\n",
        "        return X\n",
        "\n",
        "\n",
        "# ------- Education & Marriage Interaction Transformer -------#\n",
        "class EducationMarriageInteractionTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, education_column='education', marriage_column='marriage'):\n",
        "        self.education_column = education_column\n",
        "        self.marriage_column = marriage_column\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Check if the column already exists to avoid overwriting\n",
        "        if 'education_marriage' in X.columns:\n",
        "            raise ValueError(\"Column 'education_marriage' already exists in the DataFrame.\")\n",
        "\n",
        "        # Create the education_marriage interaction feature\n",
        "        X['education_marriage'] = X[self.education_column].astype(str) + '_' + X[self.marriage_column].astype(str)\n",
        "        X['education_marriage'] = X['education_marriage'].astype('category')\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Education & Marriage Unknown Flag Transformer -------#\n",
        "class EducationMarriageUnknownFlagTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self):\n",
        "        pass\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self  # No fitting necessary\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Check if the columns already exist to avoid overwriting\n",
        "        if 'education_or_marriage_unknown_flag' in X.columns or 'education_and_marriage_unknown_flag' in X.columns:\n",
        "            raise ValueError(\"One or both of the flag columns ('education_or_marriage_unknown_flag', 'education_and_marriage_unknown_flag') already exist in the DataFrame.\")\n",
        "\n",
        "        # Feature for when either education or marriage is \"Other/Unknown\"\n",
        "        X['education_or_marriage_unknown_flag'] = ((X['education'] == 'Other/Unknown') |\n",
        "                                                   (X['marriage'] == 'Unknown/Others')).astype('bool')\n",
        "\n",
        "        # Feature for when both education and marriage are \"Other/Unknown\"\n",
        "        X['education_and_marriage_unknown_flag'] = ((X['education'] == 'Other/Unknown') &\n",
        "                                                    (X['marriage'] == 'Unknown/Others')).astype('bool')\n",
        "\n",
        "        return X\n",
        "\n",
        "\n",
        "# ------- Risk Ratio Transformer -------#\n",
        "\n",
        "class RiskRatioTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, education_col='education', marriage_col='marriage'):\n",
        "        self.education_col = education_col\n",
        "        self.marriage_col = marriage_col\n",
        "        self.education_default_rates = None\n",
        "        self.marriage_default_rates = None\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        if y is None:\n",
        "            raise ValueError(\"Target variable 'y' is required for fit.\")\n",
        "\n",
        "        X = X.copy()\n",
        "        X['target'] = y  # Add the target values to X temporarily\n",
        "\n",
        "        # Calculate the default rates based on the target\n",
        "        self.education_default_rates = X.groupby(self.education_col, observed=True)['target'].mean()\n",
        "        self.marriage_default_rates = X.groupby(self.marriage_col, observed=True)['target'].mean()\n",
        "\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        X = X.copy()\n",
        "\n",
        "        # Convert categorical columns to object type to avoid issues with adding new categories\n",
        "        X[self.education_col] = X[self.education_col].astype(object)\n",
        "        X[self.marriage_col] = X[self.marriage_col].astype(object)\n",
        "\n",
        "        # Map the default rates, filling NaN for unseen categories with a default value (e.g., 0 or the mean of the default rates)\n",
        "        X['education_risk_ratio'] = X[self.education_col].map(self.education_default_rates).fillna(0).astype(float)\n",
        "        X['marriage_risk_ratio'] = X[self.marriage_col].map(self.marriage_default_rates).fillna(0).astype(float)\n",
        "\n",
        "        # Drop the target column if it exists\n",
        "        if 'target' in X.columns:\n",
        "            X.drop(columns=['target'], inplace=True)\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- WoE & IV Transformer -------#\n",
        "\n",
        "class WoeIvTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, features, target_col):\n",
        "        self.features = features\n",
        "        self.target_col = target_col\n",
        "        self.woe_dict = {}  # To store WoE for each feature\n",
        "        self.iv_dict = {}   # To store IV for each feature\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        if y is None:\n",
        "            raise ValueError(\"Target variable 'y' is required for fit.\")\n",
        "\n",
        "        # Ensure `y` is a pandas Series\n",
        "        y = pd.Series(y, name=self.target_col)\n",
        "\n",
        "        for feature in self.features:\n",
        "            if feature not in X.columns:\n",
        "                raise ValueError(f\"Feature '{feature}' is not present in the DataFrame.\")\n",
        "\n",
        "            # Create a DataFrame from the feature in X and the target (y)\n",
        "            df = pd.DataFrame({feature: X[feature], self.target_col: y})\n",
        "\n",
        "            # Group by the feature and calculate counts and sums (events)\n",
        "            grouped = df.groupby(feature, observed=True)[self.target_col].agg(['count', 'sum'])\n",
        "\n",
        "            # Calculate non-events and rates\n",
        "            grouped['non_event'] = grouped['count'] - grouped['sum']\n",
        "            grouped['event_rate'] = grouped['sum'] / grouped['sum'].sum()\n",
        "            grouped['non_event_rate'] = grouped['non_event'] / grouped['non_event'].sum()\n",
        "\n",
        "            # Avoid division by zero in WoE calculation\n",
        "            grouped.replace({'event_rate': {0: 1e-6}, 'non_event_rate': {0: 1e-6}}, inplace=True)\n",
        "\n",
        "            # Calculate WoE and IV\n",
        "            grouped['woe'] = np.log(grouped['event_rate'] / grouped['non_event_rate'])\n",
        "            grouped['iv'] = (grouped['event_rate'] - grouped['non_event_rate']) * grouped['woe']\n",
        "\n",
        "            iv = grouped['iv'].sum()  # Total IV for the feature\n",
        "\n",
        "            # Store WoE and IV in dictionaries\n",
        "            self.woe_dict[feature] = grouped['woe']\n",
        "            self.iv_dict[feature] = iv\n",
        "\n",
        "        return self\n",
        "\n",
        "    def transform(self, X):\n",
        "        \"\"\"\n",
        "        Apply the WoE transformation to the given features in the DataFrame `X`\n",
        "        using the WoE values learned during `fit()`.\n",
        "        \"\"\"\n",
        "        X = X.copy()  # Avoid modifying the original DataFrame\n",
        "\n",
        "        # Map the WoE values to the features and add them as new columns\n",
        "        for feature in self.features:\n",
        "            if feature in self.woe_dict:\n",
        "                X[f'{feature}_woe'] = X[feature].map(self.woe_dict[feature]).fillna(0)\n",
        "\n",
        "        return X\n",
        "\n",
        "    def get_iv(self):\n",
        "        \"\"\"\n",
        "        Return the IV (Information Value) for each feature.\n",
        "        \"\"\"\n",
        "        return self.iv_dict\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# ------- Combined Feature Engineering Transformer for IMBPipeline -------#\n",
        "\n",
        "class CombinedFeatureEngineeringTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns, pay_columns, bill_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "        self.pay_columns = pay_columns\n",
        "        self.bill_columns = bill_columns\n",
        "\n",
        "        # Add the target-dependent transformers here\n",
        "        self.risk_ratio_transformer = RiskRatioTransformer()\n",
        "        self.woe_iv_transformer = WoeIvTransformer(features=['education', 'marriage'], target_col='target')\n",
        "\n",
        "        # Track whether these transformers have been fitted\n",
        "        self.risk_ratio_fitted = False\n",
        "        self.woe_iv_fitted = False\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        \"\"\"\n",
        "        Fit all the transformers that require y here.\n",
        "        The target (y) is used for RiskRatioTransformer and WoeIvTransformer.\n",
        "        \"\"\"\n",
        "        # Fit the transformers that don't need y (some transformers may have fit methods)\n",
        "        # Other transformers like RollingAverageDelayTransformer may not require fit, so we can skip them\n",
        "\n",
        "        if y is not None:\n",
        "            self.risk_ratio_transformer.fit(X, y)  # Fit using X and y\n",
        "            self.woe_iv_transformer.fit(X, y)  # Fit using X and y\n",
        "            self.risk_ratio_fitted = True  # Mark that the transformer has been fitted\n",
        "            self.woe_iv_fitted = True  # Mark that the transformer has been fitted\n",
        "\n",
        "        return self\n",
        "\n",
        "    def transform(self, X, y=None):\n",
        "        \"\"\"\n",
        "        Apply all the transformations. This method should not depend on `y` during inference.\n",
        "        \"\"\"\n",
        "        # Apply transformations that do not require target (y)\n",
        "        X = RollingAverageDelayTransformer(self.delay_columns).transform(X)\n",
        "        X = HighRiskDelayFeatureTransformer(self.delay_columns).transform(X)\n",
        "        X = SevereDelayCountTransformer(self.delay_columns).transform(X)\n",
        "        X = CumulativeDelayTransformer(self.delay_columns).transform(X)\n",
        "        X = CumulativeDelayBinningTransformer().transform(X)\n",
        "\n",
        "        # Combine pay_columns and bill_columns for relevant transformations\n",
        "        X = SevereDelayPaymentRatioTransformer(self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = AvgPaymentSevereDelaysTransformer().transform(X)\n",
        "        X = DeferredAndDecreasingPaymentsTransformer(self.delay_columns, self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = SevereDeferredAndDecreasingPaymentsTransformer(self.delay_columns, self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = SevereDeferredAndAnyDecreasingPaymentsTransformer(self.delay_columns, self.pay_columns + self.bill_columns).transform(X)\n",
        "\n",
        "        X = TotalPaymentsTransformer(self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = AvgPaymentTransformer().transform(X)\n",
        "        X = PaymentStdTransformer(self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = WeightedPaymentSumTransformer(self.pay_columns + self.bill_columns).transform(X)\n",
        "\n",
        "        X = TotalBillsTransformer(self.bill_columns).transform(X)\n",
        "        X = DebtToLimitRatioTransformer(total_bills_column='total_bills', limit_bal_column='limit_bal').transform(X)\n",
        "        X = EducationMarriageInteractionTransformer().transform(X)\n",
        "        X = EducationMarriageUnknownFlagTransformer().transform(X)\n",
        "\n",
        "        # print('Before Risk Ratio', X.columns) # debug check\n",
        "\n",
        "        # Apply the learned transformations that depend on target (y) if they've been fitted\n",
        "        if self.risk_ratio_fitted:\n",
        "            X = self.risk_ratio_transformer.transform(X)  # Transform using the parameters learned in fit\n",
        "\n",
        "        # print('After Risk Ratio', X.columns) # debug check\n",
        "\n",
        "        if self.woe_iv_fitted:\n",
        "            X = self.woe_iv_transformer.transform(X)  # Transform using the parameters learned in fit\n",
        "\n",
        "        # print('Data after All Feature Engineering', X.columns) # debug check\n",
        "\n",
        "        return X\n",
        "\n",
        "\n",
        "# ------- Combined Feature Engineering Transformer for Logistic Regression -------#\n",
        "\n",
        "class CombinedFeatureEngineeringTransformer_LogisticRegression(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns, pay_columns, bill_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "        self.pay_columns = pay_columns\n",
        "        self.bill_columns = bill_columns\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        # This method doesn't need to do anything for this transformer\n",
        "        return self\n",
        "\n",
        "    def transform(self, X, y=None):\n",
        "        # Apply only the necessary transformations for logistic regression\n",
        "        X = RollingAverageDelayTransformer(self.delay_columns).transform(X)\n",
        "        X = CumulativeDelayTransformer(self.delay_columns).transform(X)\n",
        "        X = CumulativeDelayBinningTransformer().transform(X)\n",
        "\n",
        "        return X\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# ------- Combined Feature Engineering Transformer for Tree-Based Models -------#\n",
        "\n",
        "class CombinedFeatureEngineeringTransformer_RandomForest(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, delay_columns, pay_columns, bill_columns):\n",
        "        self.delay_columns = delay_columns\n",
        "        self.pay_columns = pay_columns\n",
        "        self.bill_columns = bill_columns\n",
        "\n",
        "        # Add the target-dependent transformers here\n",
        "        self.risk_ratio_transformer = RiskRatioTransformer()\n",
        "        self.woe_iv_transformer = WoeIvTransformer(features=['education', 'marriage'], target_col='target')\n",
        "\n",
        "        # Track whether these transformers have been fitted\n",
        "        self.risk_ratio_fitted = False\n",
        "        self.woe_iv_fitted = False\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        if y is not None:\n",
        "            self.risk_ratio_transformer.fit(X, y)\n",
        "            self.woe_iv_transformer.fit(X, y)\n",
        "            self.risk_ratio_fitted = True\n",
        "            self.woe_iv_fitted = True\n",
        "        return self\n",
        "\n",
        "    def transform(self, X, y=None):\n",
        "        \"\"\"\n",
        "        Apply the selected transformations.\n",
        "        \"\"\"\n",
        "        # Only retain the required transformers\n",
        "        X = HighRiskDelayFeatureTransformer(self.delay_columns).transform(X)\n",
        "        X = SevereDelayCountTransformer(self.delay_columns).transform(X)\n",
        "        X = CumulativeDelayBinningTransformer().transform(X)  # Creates cumulative_delay_category_*\n",
        "        X = SevereDelayPaymentRatioTransformer(self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = AvgPaymentSevereDelaysTransformer().transform(X)\n",
        "        X = DeferredAndDecreasingPaymentsTransformer(self.delay_columns, self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = SevereDeferredAndDecreasingPaymentsTransformer(self.delay_columns, self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = SevereDeferredAndAnyDecreasingPaymentsTransformer(self.delay_columns, self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = TotalPaymentsTransformer(self.pay_columns + self.bill_columns).transform(X)\n",
        "        X = TotalBillsTransformer(self.bill_columns).transform(X)\n",
        "\n",
        "        # Apply the learned transformations that depend on target (y) if they've been fitted\n",
        "        if self.risk_ratio_fitted:\n",
        "            X = self.risk_ratio_transformer.transform(X)\n",
        "        if self.woe_iv_fitted:\n",
        "            X = self.woe_iv_transformer.transform(X)\n",
        "\n",
        "        return X\n",
        "\n",
        "\n",
        "'''\n",
        "\n",
        "# Write the script to a file\n",
        "with open(\"feature_engineering_pipeline_utils.py\", \"w\") as file:\n",
        "    file.write(script_content)\n",
        "\n",
        "print(\"Script successfully written to feature_engineering_pipeline_utils.py\")\n",
        "# Reload script to make functions available for use\n",
        "import importlib\n",
        "import feature_engineering_pipeline_utils\n",
        "importlib.reload(feature_engineering_pipeline_utils)\n",
        "\n",
        "from feature_engineering_pipeline_utils import *"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Feature Selection Script"
      ],
      "metadata": {
        "id": "I3L0w6TkqNZ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "script_content = \"\"\"\n",
        "\n",
        "import json\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "# Helper function to save classification reports and selected features to JSON files\n",
        "def save_results_to_json(results_dict, features_dict, method_name, classification_report, selected_features):\n",
        "    # Load existing results if available\n",
        "    try:\n",
        "        with open('classification_reports.json', 'r') as f:\n",
        "            results_dict.update(json.load(f))\n",
        "        with open('selected_features.json', 'r') as f:\n",
        "            features_dict.update(json.load(f))\n",
        "    except FileNotFoundError:\n",
        "        pass  # Files don't exist yet, which is fine for the first run\n",
        "\n",
        "    # Add new method results\n",
        "    results_dict[method_name] = classification_report\n",
        "    features_dict[method_name] = selected_features\n",
        "\n",
        "    # Save the updated results\n",
        "    with open('classification_reports.json', 'w') as f:\n",
        "        json.dump(results_dict, f, indent=4)\n",
        "\n",
        "    with open('selected_features.json', 'w') as f:\n",
        "        json.dump(features_dict, f, indent=4)\n",
        "\n",
        "#--------   Plot Selected Features  --------#\n",
        "\n",
        "def plot_selected_features(selected_features, model_name, figsize=(16,16)):\n",
        "\n",
        "    sns.set_theme(\"notebook\")\n",
        "    sns.set_style(\"ticks\")\n",
        "    features = list(selected_features.keys())\n",
        "    importances = list(selected_features.values())\n",
        "    plt.figure(figsize=figsize)\n",
        "    sns.barplot(x=importances, y=features, hue=importances, palette='viridis')\n",
        "    plt.title(f'Selected Features and their Importance for {model_name}')\n",
        "    plt.xlabel('Importance')\n",
        "    plt.ylabel('')\n",
        "    plt.legend(loc='lower right')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# -----  Plot Classification Report Comparison ------#\n",
        "\n",
        "import warnings\n",
        "\n",
        "# Suppress the specific FutureWarning\n",
        "warnings.filterwarnings('ignore', category=FutureWarning, module='seaborn')\n",
        "\n",
        "def plot_comparison_classification_metrics_seaborn(reports, model_names):\n",
        "    '''\n",
        "    Plots Precision, Recall, and F1-score comparisons for multiple filter methods using Seaborn barplots.\n",
        "\n",
        "    Args:\n",
        "    - reports: A list of classification reports (dict format).\n",
        "    - model_names: A list of model names corresponding to the reports.\n",
        "    '''\n",
        "    # Initialize lists to collect metric values for each model\n",
        "    precision_values = []\n",
        "    recall_values = []\n",
        "    f1_values = []\n",
        "\n",
        "    # For each report, extract the values for Precision, Recall, and F1-score (for both classes)\n",
        "    for report in reports:\n",
        "        precision_values.append([\n",
        "            report['0']['precision'],  # Class 0 (No Default)\n",
        "            report['1']['precision']   # Class 1 (Default)\n",
        "        ])\n",
        "        recall_values.append([\n",
        "            report['0']['recall'],     # Class 0 (No Default)\n",
        "            report['1']['recall']      # Class 1 (Default)\n",
        "        ])\n",
        "        f1_values.append([\n",
        "            report['0']['f1-score'],   # Class 0 (No Default)\n",
        "            report['1']['f1-score']    # Class 1 (Default)\n",
        "        ])\n",
        "\n",
        "    # Convert to DataFrame for easy plotting\n",
        "    df_precision = pd.DataFrame(precision_values, columns=['No Default', 'Default'], index=model_names).reset_index()\n",
        "    df_recall = pd.DataFrame(recall_values, columns=['No Default', 'Default'], index=model_names).reset_index()\n",
        "    df_f1 = pd.DataFrame(f1_values, columns=['No Default', 'Default'], index=model_names).reset_index()\n",
        "\n",
        "    # Melt the DataFrames for seaborn\n",
        "    df_precision_melt = df_precision.melt(id_vars='index', var_name='Class', value_name='Precision')\n",
        "    df_recall_melt = df_recall.melt(id_vars='index', var_name='Class', value_name='Recall')\n",
        "    df_f1_melt = df_f1.melt(id_vars='index', var_name='Class', value_name='F1-score')\n",
        "\n",
        "    # Set up the subplots (1x3 grid)\n",
        "    fig, axes = plt.subplots(1, 3, figsize=(12, 12))\n",
        "\n",
        "    # Plot Precision\n",
        "    sns.barplot(x='index', y='Precision', hue='Class', data=df_precision_melt, ax=axes[0])\n",
        "    axes[0].set_title('Precision Comparison')\n",
        "    axes[0].set_ylim(0, 1)\n",
        "    axes[0].set_ylabel('Score')\n",
        "    # axes[0].set_xlabel('Filter Method')\n",
        "    axes[0].legend(loc='lower right')\n",
        "    axes[0].tick_params(axis='x', rotation=90)  # Rotate x-ticks\n",
        "\n",
        "    # Plot Recall\n",
        "    sns.barplot(x='index', y='Recall', hue='Class', data=df_recall_melt, ax=axes[1])\n",
        "    axes[1].set_title('Recall Comparison')\n",
        "    axes[1].set_ylim(0, 1)\n",
        "    axes[1].set_ylabel('Score')\n",
        "    # axes[1].set_xlabel('Filter Method')\n",
        "    axes[1].legend(loc='lower right')\n",
        "    axes[1].tick_params(axis='x', rotation=90)  # Rotate x-ticks\n",
        "\n",
        "    # Plot F1-score\n",
        "    sns.barplot(x='index', y='F1-score', hue='Class', data=df_f1_melt, ax=axes[2])\n",
        "    axes[2].set_title('F1-score Comparison')\n",
        "    axes[2].set_ylim(0, 1)\n",
        "    axes[2].set_ylabel('Score')\n",
        "    # axes[2].set_xlabel('Filter Method')\n",
        "    axes[2].legend(loc='lower right')\n",
        "    axes[2].tick_params(axis='x', rotation=90)  # Rotate x-ticks\n",
        "\n",
        "    # Set the overall title for the figure\n",
        "    plt.suptitle('Classification Report Metric Comparison Across Filter Methods', fontsize=16)\n",
        "\n",
        "    # Adjust layout\n",
        "    plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "# Write the script to a file\n",
        "with open(\"feature_selection_utils.py\", \"w\") as file:\n",
        "    file.write(script_content)\n",
        "\n",
        "print(\"Script successfully written to feature_selection_utils.py\")\n",
        "# Reload script to make functions available for use\n",
        "import importlib\n",
        "import feature_selection_utils\n",
        "importlib.reload(feature_selection_utils)\n",
        "\n",
        "from feature_selection_utils import *"
      ],
      "metadata": {
        "id": "jpE14ST6qQIB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f51a4e39-3956-4ee2-de9f-3cbac6867d26"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Script successfully written to feature_selection_utils.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cU9U02Y5CpJ7"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM3GV95HzoCFlMn1KbB3gqz",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}