loan_defaults_024_feature_engineering_cleanup.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_024_feature_engineering_cleanup.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",
      "metadata": {
        "id": "_vARfjW4_LcJ"
      },
      "source": [
        "## Load & Preprocess Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "6HxbQMjvTcYL",
        "outputId": "4036aca3-9f4f-4cca-b6b0-cfbdef902e87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values: 0\n",
            "Missing Percentage: 0.0\n",
            "Number of Duplicate Rows: 0\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 29965 entries, 0 to 29999\n",
            "Data columns (total 24 columns):\n",
            " #   Column                      Non-Null Count  Dtype   \n",
            "---  ------                      --------------  -----   \n",
            " 0   limit_bal                   29965 non-null  int64   \n",
            " 1   sex                         29965 non-null  category\n",
            " 2   education                   29965 non-null  category\n",
            " 3   marriage                    29965 non-null  category\n",
            " 4   age                         29965 non-null  int64   \n",
            " 5   bill_amt_4_april            29965 non-null  int64   \n",
            " 6   bill_amt_5_may              29965 non-null  int64   \n",
            " 7   bill_amt_6_june             29965 non-null  int64   \n",
            " 8   bill_amt_7_july             29965 non-null  int64   \n",
            " 9   bill_amt_8_august           29965 non-null  int64   \n",
            " 10  bill_amt_9_september        29965 non-null  int64   \n",
            " 11  pay_amt_4_april             29965 non-null  int64   \n",
            " 12  pay_amt_5_may               29965 non-null  int64   \n",
            " 13  pay_amt_6_june              29965 non-null  int64   \n",
            " 14  pay_amt_7_july              29965 non-null  int64   \n",
            " 15  pay_amt_8_august            29965 non-null  int64   \n",
            " 16  pay_amt_9_september         29965 non-null  int64   \n",
            " 17  pay_delay_4_april           29965 non-null  category\n",
            " 18  pay_delay_5_may             29965 non-null  category\n",
            " 19  pay_delay_6_june            29965 non-null  category\n",
            " 20  pay_delay_7_july            29965 non-null  category\n",
            " 21  pay_delay_8_august          29965 non-null  category\n",
            " 22  pay_delay_9_september       29965 non-null  category\n",
            " 23  default_payment_next_month  29965 non-null  int64   \n",
            "dtypes: category(9), int64(15)\n",
            "memory usage: 3.9 MB\n",
            "Number of Duplicate Rows After Dropping: 0\n",
            "Column: sex\n",
            "Categories: Index(['Female', 'Male'], dtype='object')\n",
            "Ordered: False\n",
            "\n",
            "Column: marriage\n",
            "Categories: Index(['Married', 'Single', 'Unknown/Others'], dtype='object')\n",
            "Ordered: False\n",
            "\n",
            "Column: pay_delay_4_april\n",
            "Categories: Index(['No consumption', 'Paid in full', 'Revolving credit', '1 month delay',\n",
            "       '2 months delay', '3 months delay', '4 months delay', '5 months delay',\n",
            "       '6 months delay', '7 months delay', '8 months delay',\n",
            "       '9+ months delay'],\n",
            "      dtype='object')\n",
            "Ordered: True\n",
            "\n",
            "Column: pay_delay_5_may\n",
            "Categories: Index(['No consumption', 'Paid in full', 'Revolving credit', '1 month delay',\n",
            "       '2 months delay', '3 months delay', '4 months delay', '5 months delay',\n",
            "       '6 months delay', '7 months delay', '8 months delay',\n",
            "       '9+ months delay'],\n",
            "      dtype='object')\n",
            "Ordered: True\n",
            "\n",
            "Column: pay_delay_6_june\n",
            "Categories: Index(['No consumption', 'Paid in full', 'Revolving credit', '1 month delay',\n",
            "       '2 months delay', '3 months delay', '4 months delay', '5 months delay',\n",
            "       '6 months delay', '7 months delay', '8 months delay',\n",
            "       '9+ months delay'],\n",
            "      dtype='object')\n",
            "Ordered: True\n",
            "\n",
            "Column: pay_delay_7_july\n",
            "Categories: Index(['No consumption', 'Paid in full', 'Revolving credit', '1 month delay',\n",
            "       '2 months delay', '3 months delay', '4 months delay', '5 months delay',\n",
            "       '6 months delay', '7 months delay', '8 months delay',\n",
            "       '9+ months delay'],\n",
            "      dtype='object')\n",
            "Ordered: True\n",
            "\n",
            "Column: pay_delay_8_august\n",
            "Categories: Index(['No consumption', 'Paid in full', 'Revolving credit', '1 month delay',\n",
            "       '2 months delay', '3 months delay', '4 months delay', '5 months delay',\n",
            "       '6 months delay', '7 months delay', '8 months delay',\n",
            "       '9+ months delay'],\n",
            "      dtype='object')\n",
            "Ordered: True\n",
            "\n",
            "Column: pay_delay_9_september\n",
            "Categories: Index(['No consumption', 'Paid in full', 'Revolving credit', '1 month delay',\n",
            "       '2 months delay', '3 months delay', '4 months delay', '5 months delay',\n",
            "       '6 months delay', '7 months delay', '8 months delay',\n",
            "       '9+ months delay'],\n",
            "      dtype='object')\n",
            "Ordered: True\n",
            "\n",
            "Column: default_payment_next_month is not categorical.\n",
            "\n",
            "Target as numeric\n",
            "int64\n",
            "default_payment_next_month\n",
            "0    23335\n",
            "1     6630\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1400 with 2 Axes>"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x2500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from loan_data_utils import (load_data_from_url, clean_column_names, remove_id_column, rename_pay_columns, rename_bill_and_payment_columns,load_and_preprocess_data,\n",
        "                             label_pay_columns, convert_pay_columns_to_ordinal, reorder_columns, convert_education_to_ordinal, process_sex_column, process_sex_column_cat_codes_one_two,\n",
        "                             process_marriage_column, process_default_payment_column, convert_ordinal_to_category, check_column_integrity, validate_data_types,\n",
        "                             check_categorical_order, primary_columns, bill_columns, pay_columns, ordinal_columns, categorical_columns,\n",
        "                             plot_categorical_distributions, plot_ordinal_distributions)\n",
        "from eda_utils import data_overview, plot_univariate_distributions, plot_class_distribution\n",
        "import logging\n",
        "\n",
        "# Define your URL, categorical columns, and target\n",
        "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls\"\n",
        "categorical_columns = ['sex', 'marriage']\n",
        "target = 'default_payment_next_month'\n",
        "\n",
        "# Now, load the data\n",
        "df = load_and_preprocess_data(url, categorical_columns, target)\n",
        "\n",
        "if df is not None:\n",
        "    # Ensure the target column is treated as a categorical variable\n",
        "    # df[target] = df[target].astype('category')\n",
        "\n",
        "    # Print data overview\n",
        "    data_overview(df)\n",
        "\n",
        "    # Drop duplicate rows (optional if you didn’t remove them earlier)\n",
        "    df = df.drop_duplicates()\n",
        "\n",
        "    # Confirm the duplicates are removed\n",
        "    print(f\"Number of Duplicate Rows After Dropping: {df.duplicated().sum()}\")\n",
        "\n",
        "    # List of columns to check\n",
        "    pay_cols = [col for col in df.columns if col.startswith('pay_delay')]\n",
        "\n",
        "    # Check the order of categorical columns\n",
        "    check_categorical_order(df, categorical_columns + pay_cols + [target])\n",
        "else:\n",
        "    print(\"Error loading and preprocessing data.\")\n",
        "\n",
        "print('Target as numeric')\n",
        "print(df['default_payment_next_month'].dtype)\n",
        "print(df[target].value_counts())\n",
        "\n",
        "# visualize the categorical data\n",
        "plot_categorical_distributions(df, categorical_columns)\n",
        "plot_ordinal_distributions(df, ordinal_columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Changes to the Pipeline:\n",
        "\n",
        "1. **Reusing Fitted Transformers**:\n",
        "   - The main change was ensuring that the test data pipeline reuses the already fitted `CombinedFeatureEngineeringTransformer` (which includes `RiskRatioTransformer` and `WoeIvTransformer`) from the training pipeline.\n",
        "   - Instead of re-fitting these transformers on the test data (which would fail because `y` is not available during inference), the fitted transformers are used to **only transform** the test data.\n",
        "\n",
        "#### Specific Code Changes:\n",
        "```python\n",
        "# Step 1: Define the full pipeline (with SMOTE) for the training data\n",
        "smote_preprocessing_pipeline = IMBPipeline(steps=[\n",
        "    ('outlier_removal', WinsorRobustOutlierRemovalTransformer(columns=pay_columns)),  # Outlier removal\n",
        "    ('feature_engineering', CombinedFeatureEngineeringTransformer(delay_columns, pay_columns, bill_columns)),  # Feature engineering\n",
        "    ('preprocessor', preprocessor),  # Preprocessing\n",
        "    ('smote', SMOTE())  # SMOTE for resampling\n",
        "])\n",
        "\n",
        "# Step 2: Define the preprocessing pipeline for the test data (without SMOTE)\n",
        "test_preprocessing_pipeline = Pipeline(steps=[\n",
        "    ('outlier_removal', WinsorRobustOutlierRemovalTransformer(columns=pay_columns)),  # Outlier removal\n",
        "    # Reuse the fitted CombinedFeatureEngineeringTransformer from the training pipeline\n",
        "    ('feature_engineering', smote_preprocessing_pipeline.named_steps['feature_engineering']),  \n",
        "    ('preprocessor', preprocessor)  # Preprocessing (without SMOTE)\n",
        "])\n",
        "\n",
        "```\n",
        "\n",
        "### Explanation of Changes:\n",
        "\n",
        "1. **Reusing Fitted Transformers**:\n",
        "   - In the **original code**, you were re-instantiating the `CombinedFeatureEngineeringTransformer` for the test data in step 2. However, this transformer needs to be fitted with `y` to learn the risk ratios and WoE values. Since `y` is not available during inference, the test pipeline should not re-fit these transformers but rather reuse the fitted ones from the training pipeline.\n",
        "   \n",
        "   - In the **updated code**, the test pipeline reuses the already fitted `CombinedFeatureEngineeringTransformer` from the `smote_preprocessing_pipeline`. This is done by using the line:\n",
        "     ```python\n",
        "     ('feature_engineering', smote_preprocessing_pipeline.named_steps['feature_engineering']),\n",
        "     ```\n",
        "\n",
        "   - This ensures that the test pipeline applies the learned transformations (such as `education_risk_ratio`, `marriage_risk_ratio`, `education_woe`, `marriage_woe`) to the test data without needing to re-fit the transformer.\n",
        "\n",
        "2. **Separate Preprocessing for Test Data**:\n",
        "   - The test pipeline still needs to fit the `preprocessor` (scaling, encoding) on the training data (`X_train`). This ensures that the transformations applied to the test data are consistent with those learned from the training data.\n",
        "\n",
        "3. **No SMOTE in Test Pipeline**:\n",
        "   - As expected, SMOTE is only applied to the training data and is excluded from the test pipeline. This ensures that the test set is evaluated as-is, without oversampling or resampling.\n",
        "\n",
        "### Final Thoughts:\n",
        "- By reusing the fitted transformers for the test pipeline, you ensure that the same transformations applied to the training data (such as risk ratios and WoE) are also applied to the test data, without re-fitting the transformer.\n",
        "- The pipeline now processes the test data correctly, and the columns `education_risk_ratio`, `marriage_risk_ratio`, `education_woe`, and `marriage_woe` are generated properly during inference.\n",
        "\n",
        "This solution ensures consistency between training and test data transformations and avoids issues with missing columns during inference."
      ],
      "metadata": {
        "id": "RvqqOrxvMqT7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Engineering Full Pipeline Check"
      ],
      "metadata": {
        "id": "rzB6c0f_UfIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder, FunctionTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "from imblearn.pipeline import Pipeline as IMBPipeline\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from loan_data_utils import ThresholdClassifier\n",
        "from feature_engineering_pipeline_utils import (\n",
        "    numeric_features,\n",
        "    pay_columns,\n",
        "    delay_columns,\n",
        "    bill_columns,\n",
        "    categorical_features,\n",
        "    ordinal_features,\n",
        "    boolean_features,\n",
        "    RollingAverageDelayTransformer,\n",
        "    HighRiskDelayFeatureTransformer,\n",
        "    SevereDelayCountTransformer,\n",
        "    CumulativeDelayTransformer,\n",
        "    CumulativeDelayBinningTransformer,\n",
        "    SevereDelayPaymentRatioTransformer,\n",
        "    AvgPaymentSevereDelaysTransformer,\n",
        "    DeferredAndDecreasingPaymentsTransformer,\n",
        "    SevereDeferredAndDecreasingPaymentsTransformer,\n",
        "    SevereDeferredAndAnyDecreasingPaymentsTransformer,\n",
        "    TotalPaymentsTransformer,\n",
        "    AvgPaymentTransformer,\n",
        "    PaymentStdTransformer,\n",
        "    WeightedPaymentSumTransformer,\n",
        "    TotalBillsTransformer,\n",
        "    DebtToLimitRatioTransformer,\n",
        "    EducationMarriageInteractionTransformer,\n",
        "    EducationMarriageUnknownFlagTransformer,\n",
        "    RiskRatioTransformer,\n",
        "    WoeIvTransformer,\n",
        "    WinsorRobustOutlierRemovalTransformer,\n",
        "    CombinedFeatureEngineeringTransformer\n",
        ")\n",
        "\n",
        "# Define the target\n",
        "target = 'default_payment_next_month'\n",
        "\n",
        "# Split the data into features and target\n",
        "X = df.drop(columns=[target])\n",
        "y = df[target]\n",
        "\n",
        "# Perform the train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
        "\n",
        "\n",
        "# Step 1: Feature Engineering and Preprocessing Pipeline\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='mean')),\n",
        "    ('scaler', StandardScaler())\n",
        "])\n",
        "\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
        "])\n",
        "\n",
        "ordinal_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "    ('ordinal', OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1))\n",
        "])\n",
        "\n",
        "bool_to_numeric_transformer = Pipeline(steps=[\n",
        "    ('convert_bool', FunctionTransformer(lambda x: x.astype(int))),\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent'))\n",
        "])\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_features),\n",
        "        ('cat', categorical_transformer, categorical_features),\n",
        "        ('ord', ordinal_transformer, ordinal_features),\n",
        "        ('bool', bool_to_numeric_transformer, boolean_features)\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Step 1: Define the full pipeline (with SMOTE) for the training data\n",
        "smote_preprocessing_pipeline = IMBPipeline(steps=[\n",
        "    ('outlier_removal', WinsorRobustOutlierRemovalTransformer(columns=pay_columns)),  # Outlier removal\n",
        "    ('feature_engineering', CombinedFeatureEngineeringTransformer(delay_columns, pay_columns, bill_columns)),  # Feature engineering\n",
        "    ('preprocessor', preprocessor),  # Preprocessing\n",
        "    ('smote', SMOTE())  # SMOTE for resampling\n",
        "])\n",
        "\n",
        "# Step 2: Define the preprocessing pipeline for the test data (without SMOTE)\n",
        "test_preprocessing_pipeline = Pipeline(steps=[\n",
        "    ('outlier_removal', WinsorRobustOutlierRemovalTransformer(columns=pay_columns)),  # Outlier removal\n",
        "    # Reuse the fitted CombinedFeatureEngineeringTransformer from the training pipeline\n",
        "    ('feature_engineering', smote_preprocessing_pipeline.named_steps['feature_engineering']),\n",
        "    ('preprocessor', preprocessor)  # Preprocessing (without SMOTE)\n",
        "])\n",
        "\n",
        "# Step 3: Fit the training pipeline and resample the training data\n",
        "X_train_processed, y_train_resampled = smote_preprocessing_pipeline.fit_resample(X_train, y_train)\n",
        "\n",
        "# Step 4: Fit the test preprocessing pipeline using the training data\n",
        "# Ensuring the test pipeline learns from X_train for scaling and encoding\n",
        "test_preprocessing_pipeline.fit(X_train)\n",
        "\n",
        "# Step 5: Transform the test data using the test preprocessing pipeline\n",
        "X_test_processed = test_preprocessing_pipeline.transform(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRjEYXzdI8x5",
        "outputId": "0541b78e-2878-4d28-d8a6-036fdcd02801"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data after All Feature Engineering Index(['limit_bal', 'sex', 'education', 'marriage', 'age', 'bill_amt_4_april',\n",
            "       'bill_amt_5_may', 'bill_amt_6_june', 'bill_amt_7_july',\n",
            "       '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',\n",
            "       'pay_amt_9_september', 'pay_delay_4_april', 'pay_delay_5_may',\n",
            "       'pay_delay_6_june', 'pay_delay_7_july', 'pay_delay_8_august',\n",
            "       'pay_delay_9_september', 'rolling_avg_delay', 'high_risk_delay',\n",
            "       'severe_delay_count', 'cumulative_delay', 'cumulative_delay_category',\n",
            "       'severe_delay_payment_ratio', 'avg_payment_severe_delays',\n",
            "       'deferred_and_decreasing_payments',\n",
            "       'severe_deferred_and_decreasing_payments',\n",
            "       'severe_deferred_and_any_decreasing_payments', 'total_payments',\n",
            "       'avg_payment', 'payment_std', 'weighted_payment_sum', 'total_bills',\n",
            "       'debt_to_limit_ratio', 'education_marriage',\n",
            "       'education_or_marriage_unknown_flag',\n",
            "       'education_and_marriage_unknown_flag', 'education_risk_ratio',\n",
            "       'marriage_risk_ratio', 'education_woe', 'marriage_woe'],\n",
            "      dtype='object')\n",
            "Data after All Feature Engineering Index(['limit_bal', 'sex', 'education', 'marriage', 'age', 'bill_amt_4_april',\n",
            "       'bill_amt_5_may', 'bill_amt_6_june', 'bill_amt_7_july',\n",
            "       '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',\n",
            "       'pay_amt_9_september', 'pay_delay_4_april', 'pay_delay_5_may',\n",
            "       'pay_delay_6_june', 'pay_delay_7_july', 'pay_delay_8_august',\n",
            "       'pay_delay_9_september', 'rolling_avg_delay', 'high_risk_delay',\n",
            "       'severe_delay_count', 'cumulative_delay', 'cumulative_delay_category',\n",
            "       'severe_delay_payment_ratio', 'avg_payment_severe_delays',\n",
            "       'deferred_and_decreasing_payments',\n",
            "       'severe_deferred_and_decreasing_payments',\n",
            "       'severe_deferred_and_any_decreasing_payments', 'total_payments',\n",
            "       'avg_payment', 'payment_std', 'weighted_payment_sum', 'total_bills',\n",
            "       'debt_to_limit_ratio', 'education_marriage',\n",
            "       'education_or_marriage_unknown_flag',\n",
            "       'education_and_marriage_unknown_flag', 'education_risk_ratio',\n",
            "       'marriage_risk_ratio', 'education_woe', 'marriage_woe'],\n",
            "      dtype='object')\n",
            "Data after All Feature Engineering Index(['limit_bal', 'sex', 'education', 'marriage', 'age', 'bill_amt_4_april',\n",
            "       'bill_amt_5_may', 'bill_amt_6_june', 'bill_amt_7_july',\n",
            "       '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',\n",
            "       'pay_amt_9_september', 'pay_delay_4_april', 'pay_delay_5_may',\n",
            "       'pay_delay_6_june', 'pay_delay_7_july', 'pay_delay_8_august',\n",
            "       'pay_delay_9_september', 'rolling_avg_delay', 'high_risk_delay',\n",
            "       'severe_delay_count', 'cumulative_delay', 'cumulative_delay_category',\n",
            "       'severe_delay_payment_ratio', 'avg_payment_severe_delays',\n",
            "       'deferred_and_decreasing_payments',\n",
            "       'severe_deferred_and_decreasing_payments',\n",
            "       'severe_deferred_and_any_decreasing_payments', 'total_payments',\n",
            "       'avg_payment', 'payment_std', 'weighted_payment_sum', 'total_bills',\n",
            "       'debt_to_limit_ratio', 'education_marriage',\n",
            "       'education_or_marriage_unknown_flag',\n",
            "       'education_and_marriage_unknown_flag', 'education_risk_ratio',\n",
            "       'marriage_risk_ratio', 'education_woe', 'marriage_woe'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pipeline Output Data Checks"
      ],
      "metadata": {
        "id": "UG6v7aiBfxQD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize an empty list to store feature names\n",
        "all_feature_names = []\n",
        "\n",
        "# Loop through each transformer in the preprocessor to capture feature names\n",
        "for name, transformer, columns in smote_preprocessing_pipeline.named_steps['preprocessor'].transformers_:\n",
        "    if name == 'num':  # Numeric features (imputation and scaling)\n",
        "        all_feature_names.extend(transformer.named_steps['scaler'].get_feature_names_out(columns))\n",
        "    elif name == 'cat':  # Categorical features (OneHotEncoder)\n",
        "        all_feature_names.extend(transformer.named_steps['onehot'].get_feature_names_out(columns))\n",
        "    elif name == 'ord':  # Ordinal features (OrdinalEncoder)\n",
        "        all_feature_names.extend(columns)  # Ordinal features don't expand, so just use the column names\n",
        "    elif name == 'bool':  # Boolean features (converted to int)\n",
        "        all_feature_names.extend(columns)\n",
        "\n",
        "# Step 7: Convert the processed training and test data to DataFrames with meaningful feature names\n",
        "X_train_processed_df = pd.DataFrame(X_train_processed, columns=all_feature_names)\n",
        "X_test_processed_df = pd.DataFrame(X_test_processed, columns=all_feature_names)\n",
        "\n",
        "# Step 8: Add the target variable to the training and test DataFrames\n",
        "X_train_processed_df['target'] = y_train_resampled.reset_index(drop=True)\n",
        "X_test_processed_df['target'] = y_test.reset_index(drop=True)  # We don't resample y_test\n",
        "\n",
        "# Step 9: Confirm data types after processing (Optional step)\n",
        "# print(\"Data types after processing (X_train):\")\n",
        "# print(X_train_processed_df.dtypes)\n",
        "# print(\"Data types after processing (X_test):\")\n",
        "# print(X_test_processed_df.dtypes)\n",
        "\n",
        "# Step 10: Inspect the processed DataFrames (Optional step)\n",
        "print(\"Processed training data:\")\n",
        "print(X_train_processed_df.info())\n",
        "print(\"Processed test data:\")\n",
        "print(X_test_processed_df.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJ9MFtfLgQsU",
        "outputId": "32c392da-14e5-43cb-a483-65aa1e96350d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed training data:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 37336 entries, 0 to 37335\n",
            "Data columns (total 65 columns):\n",
            " #   Column                                             Non-Null Count  Dtype  \n",
            "---  ------                                             --------------  -----  \n",
            " 0   age                                                37336 non-null  float64\n",
            " 1   limit_bal                                          37336 non-null  float64\n",
            " 2   bill_amt_4_april                                   37336 non-null  float64\n",
            " 3   bill_amt_5_may                                     37336 non-null  float64\n",
            " 4   bill_amt_6_june                                    37336 non-null  float64\n",
            " 5   bill_amt_7_july                                    37336 non-null  float64\n",
            " 6   bill_amt_8_august                                  37336 non-null  float64\n",
            " 7   bill_amt_9_september                               37336 non-null  float64\n",
            " 8   pay_amt_4_april                                    37336 non-null  float64\n",
            " 9   pay_amt_5_may                                      37336 non-null  float64\n",
            " 10  pay_amt_6_june                                     37336 non-null  float64\n",
            " 11  pay_amt_7_july                                     37336 non-null  float64\n",
            " 12  pay_amt_8_august                                   37336 non-null  float64\n",
            " 13  pay_amt_9_september                                37336 non-null  float64\n",
            " 14  severe_delay_count                                 37336 non-null  float64\n",
            " 15  cumulative_delay                                   37336 non-null  float64\n",
            " 16  severe_delay_payment_ratio                         37336 non-null  float64\n",
            " 17  avg_payment_severe_delays                          37336 non-null  float64\n",
            " 18  total_payments                                     37336 non-null  float64\n",
            " 19  avg_payment                                        37336 non-null  float64\n",
            " 20  payment_std                                        37336 non-null  float64\n",
            " 21  weighted_payment_sum                               37336 non-null  float64\n",
            " 22  total_bills                                        37336 non-null  float64\n",
            " 23  debt_to_limit_ratio                                37336 non-null  float64\n",
            " 24  rolling_avg_delay                                  37336 non-null  float64\n",
            " 25  education_risk_ratio                               37336 non-null  float64\n",
            " 26  marriage_risk_ratio                                37336 non-null  float64\n",
            " 27  education_woe                                      37336 non-null  float64\n",
            " 28  marriage_woe                                       37336 non-null  float64\n",
            " 29  sex_Female                                         37336 non-null  float64\n",
            " 30  sex_Male                                           37336 non-null  float64\n",
            " 31  marriage_Married                                   37336 non-null  float64\n",
            " 32  marriage_Single                                    37336 non-null  float64\n",
            " 33  marriage_Unknown/Others                            37336 non-null  float64\n",
            " 34  education_marriage_Graduate School_Married         37336 non-null  float64\n",
            " 35  education_marriage_Graduate School_Single          37336 non-null  float64\n",
            " 36  education_marriage_Graduate School_Unknown/Others  37336 non-null  float64\n",
            " 37  education_marriage_High School_Married             37336 non-null  float64\n",
            " 38  education_marriage_High School_Single              37336 non-null  float64\n",
            " 39  education_marriage_High School_Unknown/Others      37336 non-null  float64\n",
            " 40  education_marriage_Other/Unknown_Married           37336 non-null  float64\n",
            " 41  education_marriage_Other/Unknown_Single            37336 non-null  float64\n",
            " 42  education_marriage_Other/Unknown_Unknown/Others    37336 non-null  float64\n",
            " 43  education_marriage_University_Married              37336 non-null  float64\n",
            " 44  education_marriage_University_Single               37336 non-null  float64\n",
            " 45  education_marriage_University_Unknown/Others       37336 non-null  float64\n",
            " 46  cumulative_delay_category_High Delay               37336 non-null  float64\n",
            " 47  cumulative_delay_category_Low Delay                37336 non-null  float64\n",
            " 48  cumulative_delay_category_Moderate Delay           37336 non-null  float64\n",
            " 49  cumulative_delay_category_No Delay                 37336 non-null  float64\n",
            " 50  cumulative_delay_category_Severe Delay             37336 non-null  float64\n",
            " 51  pay_delay_4_april                                  37336 non-null  float64\n",
            " 52  pay_delay_5_may                                    37336 non-null  float64\n",
            " 53  pay_delay_6_june                                   37336 non-null  float64\n",
            " 54  pay_delay_7_july                                   37336 non-null  float64\n",
            " 55  pay_delay_8_august                                 37336 non-null  float64\n",
            " 56  pay_delay_9_september                              37336 non-null  float64\n",
            " 57  education                                          37336 non-null  float64\n",
            " 58  high_risk_delay                                    37336 non-null  float64\n",
            " 59  deferred_and_decreasing_payments                   37336 non-null  float64\n",
            " 60  severe_deferred_and_decreasing_payments            37336 non-null  float64\n",
            " 61  severe_deferred_and_any_decreasing_payments        37336 non-null  float64\n",
            " 62  education_or_marriage_unknown_flag                 37336 non-null  float64\n",
            " 63  education_and_marriage_unknown_flag                37336 non-null  float64\n",
            " 64  target                                             37336 non-null  int64  \n",
            "dtypes: float64(64), int64(1)\n",
            "memory usage: 18.5 MB\n",
            "None\n",
            "Processed test data:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5993 entries, 0 to 5992\n",
            "Data columns (total 65 columns):\n",
            " #   Column                                             Non-Null Count  Dtype  \n",
            "---  ------                                             --------------  -----  \n",
            " 0   age                                                5993 non-null   float64\n",
            " 1   limit_bal                                          5993 non-null   float64\n",
            " 2   bill_amt_4_april                                   5993 non-null   float64\n",
            " 3   bill_amt_5_may                                     5993 non-null   float64\n",
            " 4   bill_amt_6_june                                    5993 non-null   float64\n",
            " 5   bill_amt_7_july                                    5993 non-null   float64\n",
            " 6   bill_amt_8_august                                  5993 non-null   float64\n",
            " 7   bill_amt_9_september                               5993 non-null   float64\n",
            " 8   pay_amt_4_april                                    5993 non-null   float64\n",
            " 9   pay_amt_5_may                                      5993 non-null   float64\n",
            " 10  pay_amt_6_june                                     5993 non-null   float64\n",
            " 11  pay_amt_7_july                                     5993 non-null   float64\n",
            " 12  pay_amt_8_august                                   5993 non-null   float64\n",
            " 13  pay_amt_9_september                                5993 non-null   float64\n",
            " 14  severe_delay_count                                 5993 non-null   float64\n",
            " 15  cumulative_delay                                   5993 non-null   float64\n",
            " 16  severe_delay_payment_ratio                         5993 non-null   float64\n",
            " 17  avg_payment_severe_delays                          5993 non-null   float64\n",
            " 18  total_payments                                     5993 non-null   float64\n",
            " 19  avg_payment                                        5993 non-null   float64\n",
            " 20  payment_std                                        5993 non-null   float64\n",
            " 21  weighted_payment_sum                               5993 non-null   float64\n",
            " 22  total_bills                                        5993 non-null   float64\n",
            " 23  debt_to_limit_ratio                                5993 non-null   float64\n",
            " 24  rolling_avg_delay                                  5993 non-null   float64\n",
            " 25  education_risk_ratio                               5993 non-null   float64\n",
            " 26  marriage_risk_ratio                                5993 non-null   float64\n",
            " 27  education_woe                                      5993 non-null   float64\n",
            " 28  marriage_woe                                       5993 non-null   float64\n",
            " 29  sex_Female                                         5993 non-null   float64\n",
            " 30  sex_Male                                           5993 non-null   float64\n",
            " 31  marriage_Married                                   5993 non-null   float64\n",
            " 32  marriage_Single                                    5993 non-null   float64\n",
            " 33  marriage_Unknown/Others                            5993 non-null   float64\n",
            " 34  education_marriage_Graduate School_Married         5993 non-null   float64\n",
            " 35  education_marriage_Graduate School_Single          5993 non-null   float64\n",
            " 36  education_marriage_Graduate School_Unknown/Others  5993 non-null   float64\n",
            " 37  education_marriage_High School_Married             5993 non-null   float64\n",
            " 38  education_marriage_High School_Single              5993 non-null   float64\n",
            " 39  education_marriage_High School_Unknown/Others      5993 non-null   float64\n",
            " 40  education_marriage_Other/Unknown_Married           5993 non-null   float64\n",
            " 41  education_marriage_Other/Unknown_Single            5993 non-null   float64\n",
            " 42  education_marriage_Other/Unknown_Unknown/Others    5993 non-null   float64\n",
            " 43  education_marriage_University_Married              5993 non-null   float64\n",
            " 44  education_marriage_University_Single               5993 non-null   float64\n",
            " 45  education_marriage_University_Unknown/Others       5993 non-null   float64\n",
            " 46  cumulative_delay_category_High Delay               5993 non-null   float64\n",
            " 47  cumulative_delay_category_Low Delay                5993 non-null   float64\n",
            " 48  cumulative_delay_category_Moderate Delay           5993 non-null   float64\n",
            " 49  cumulative_delay_category_No Delay                 5993 non-null   float64\n",
            " 50  cumulative_delay_category_Severe Delay             5993 non-null   float64\n",
            " 51  pay_delay_4_april                                  5993 non-null   float64\n",
            " 52  pay_delay_5_may                                    5993 non-null   float64\n",
            " 53  pay_delay_6_june                                   5993 non-null   float64\n",
            " 54  pay_delay_7_july                                   5993 non-null   float64\n",
            " 55  pay_delay_8_august                                 5993 non-null   float64\n",
            " 56  pay_delay_9_september                              5993 non-null   float64\n",
            " 57  education                                          5993 non-null   float64\n",
            " 58  high_risk_delay                                    5993 non-null   float64\n",
            " 59  deferred_and_decreasing_payments                   5993 non-null   float64\n",
            " 60  severe_deferred_and_decreasing_payments            5993 non-null   float64\n",
            " 61  severe_deferred_and_any_decreasing_payments        5993 non-null   float64\n",
            " 62  education_or_marriage_unknown_flag                 5993 non-null   float64\n",
            " 63  education_and_marriage_unknown_flag                5993 non-null   float64\n",
            " 64  target                                             5993 non-null   int64  \n",
            "dtypes: float64(64), int64(1)\n",
            "memory usage: 3.0 MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_processed_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "QnjKk2c9ihQM",
        "outputId": "37329e49-fdd9-42c5-e358-0fb9bfd563e7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        age  limit_bal  bill_amt_4_april  bill_amt_5_may  bill_amt_6_june  \\\n",
              "0 -0.050264   1.022024         -0.652938       -0.665085        -0.674057   \n",
              "1  0.167213  -0.903020         -0.208968       -0.220084         0.072324   \n",
              "2 -1.355126  -0.672015         -0.645486       -0.650460        -0.660258   \n",
              "3 -1.028910  -0.903020         -0.639846       -0.665085        -0.667997   \n",
              "4 -0.593956   0.252006          1.516122        1.513376         1.244053   \n",
              "\n",
              "   bill_amt_7_july  bill_amt_8_august  bill_amt_9_september  pay_amt_4_april  \\\n",
              "0        -0.678531          -0.694464             -0.700030        -0.709630   \n",
              "1         0.032115          -0.005582             -0.049079        -0.446149   \n",
              "2        -0.672134          -0.688208             -0.693968        -0.481719   \n",
              "3        -0.672912          -0.665187             -0.694705        -0.709630   \n",
              "4        -0.321904           0.009043             -0.037938         0.245271   \n",
              "\n",
              "   pay_amt_5_may  ...  pay_delay_8_august  pay_delay_9_september  education  \\\n",
              "0      -0.746632  ...                 7.0                    8.0        3.0   \n",
              "1      -0.746632  ...                 1.0                    1.0        1.0   \n",
              "2      -0.746632  ...                 8.0                    9.0        0.0   \n",
              "3      -0.562644  ...                 8.0                    9.0        3.0   \n",
              "4       3.086462  ...                 7.0                    8.0        3.0   \n",
              "\n",
              "   high_risk_delay  deferred_and_decreasing_payments  \\\n",
              "0              0.0                               0.0   \n",
              "1              0.0                               0.0   \n",
              "2              0.0                               0.0   \n",
              "3              0.0                               0.0   \n",
              "4              0.0                               0.0   \n",
              "\n",
              "   severe_deferred_and_decreasing_payments  \\\n",
              "0                                      0.0   \n",
              "1                                      0.0   \n",
              "2                                      0.0   \n",
              "3                                      0.0   \n",
              "4                                      0.0   \n",
              "\n",
              "   severe_deferred_and_any_decreasing_payments  \\\n",
              "0                                          0.0   \n",
              "1                                          0.0   \n",
              "2                                          0.0   \n",
              "3                                          0.0   \n",
              "4                                          0.0   \n",
              "\n",
              "   education_or_marriage_unknown_flag  education_and_marriage_unknown_flag  \\\n",
              "0                                 0.0                                  0.0   \n",
              "1                                 1.0                                  0.0   \n",
              "2                                 0.0                                  0.0   \n",
              "3                                 0.0                                  0.0   \n",
              "4                                 0.0                                  0.0   \n",
              "\n",
              "   target  \n",
              "0       0  \n",
              "1       1  \n",
              "2       0  \n",
              "3       0  \n",
              "4       1  \n",
              "\n",
              "[5 rows x 65 columns]"
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-840cda85-4b5b-4ebf-9796-1bf9fb1c85e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train_processed_df"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_processed_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "VF2TMKAaj9mo",
        "outputId": "72c9eb11-de13-4e5d-b0a1-546bcc08f80c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  limit_bal  bill_amt_4_april  bill_amt_5_may  bill_amt_6_june  \\\n",
              "0  0.819644  -0.903020         -0.573376       -0.518437        -0.471461   \n",
              "1 -0.485218   0.560013          2.441981        2.312826         2.110130   \n",
              "2 -1.137649  -0.595013         -0.516340       -0.471876        -0.516570   \n",
              "3 -1.463864  -0.903020         -0.209404       -0.217548        -0.257163   \n",
              "4 -0.376479   0.483012          0.077742        0.034887        -0.076958   \n",
              "\n",
              "   bill_amt_7_july  bill_amt_8_august  bill_amt_9_september  pay_amt_4_april  \\\n",
              "0        -0.432596          -0.136452             -0.165121         0.168642   \n",
              "1         1.857348           1.740194              1.598916         0.827346   \n",
              "2        -0.551054          -0.589122             -0.616880        -0.490062   \n",
              "3        -0.270787          -0.265054             -0.281805        -0.458884   \n",
              "4        -0.252331          -0.029786             -0.089248         0.388210   \n",
              "\n",
              "   pay_amt_5_may  ...  pay_delay_8_august  pay_delay_9_september  education  \\\n",
              "0      -0.628691  ...                 9.0                   10.0        1.0   \n",
              "1       0.880958  ...                 9.0                   10.0        3.0   \n",
              "2      -0.392808  ...                 9.0                   10.0        0.0   \n",
              "3      -0.524195  ...                 9.0                   10.0        3.0   \n",
              "4       0.432781  ...                 9.0                   10.0        0.0   \n",
              "\n",
              "   high_risk_delay  deferred_and_decreasing_payments  \\\n",
              "0              0.0                               0.0   \n",
              "1              0.0                               0.0   \n",
              "2              0.0                               0.0   \n",
              "3              0.0                               0.0   \n",
              "4              0.0                               0.0   \n",
              "\n",
              "   severe_deferred_and_decreasing_payments  \\\n",
              "0                                      0.0   \n",
              "1                                      0.0   \n",
              "2                                      0.0   \n",
              "3                                      0.0   \n",
              "4                                      0.0   \n",
              "\n",
              "   severe_deferred_and_any_decreasing_payments  \\\n",
              "0                                          0.0   \n",
              "1                                          0.0   \n",
              "2                                          0.0   \n",
              "3                                          0.0   \n",
              "4                                          0.0   \n",
              "\n",
              "   education_or_marriage_unknown_flag  education_and_marriage_unknown_flag  \\\n",
              "0                                 0.0                                  0.0   \n",
              "1                                 0.0                                  0.0   \n",
              "2                                 0.0                                  0.0   \n",
              "3                                 0.0                                  0.0   \n",
              "4                                 0.0                                  0.0   \n",
              "\n",
              "   target  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
              "3       0  \n",
              "4       0  \n",
              "\n",
              "[5 rows x 65 columns]"
            ],
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>limit_bal</th>\n",
              "      <th>bill_amt_4_april</th>\n",
              "      <th>bill_amt_5_may</th>\n",
              "      <th>bill_amt_6_june</th>\n",
              "      <th>bill_amt_7_july</th>\n",
              "      <th>bill_amt_8_august</th>\n",
              "      <th>bill_amt_9_september</th>\n",
              "      <th>pay_amt_4_april</th>\n",
              "      <th>pay_amt_5_may</th>\n",
              "      <th>...</th>\n",
              "      <th>pay_delay_8_august</th>\n",
              "      <th>pay_delay_9_september</th>\n",
              "      <th>education</th>\n",
              "      <th>high_risk_delay</th>\n",
              "      <th>deferred_and_decreasing_payments</th>\n",
              "      <th>severe_deferred_and_decreasing_payments</th>\n",
              "      <th>severe_deferred_and_any_decreasing_payments</th>\n",
              "      <th>education_or_marriage_unknown_flag</th>\n",
              "      <th>education_and_marriage_unknown_flag</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.819644</td>\n",
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              "      <td>-0.573376</td>\n",
              "      <td>-0.518437</td>\n",
              "      <td>-0.471461</td>\n",
              "      <td>-0.432596</td>\n",
              "      <td>-0.136452</td>\n",
              "      <td>-0.165121</td>\n",
              "      <td>0.168642</td>\n",
              "      <td>-0.628691</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.485218</td>\n",
              "      <td>0.560013</td>\n",
              "      <td>2.441981</td>\n",
              "      <td>2.312826</td>\n",
              "      <td>2.110130</td>\n",
              "      <td>1.857348</td>\n",
              "      <td>1.740194</td>\n",
              "      <td>1.598916</td>\n",
              "      <td>0.827346</td>\n",
              "      <td>0.880958</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.137649</td>\n",
              "      <td>-0.595013</td>\n",
              "      <td>-0.516340</td>\n",
              "      <td>-0.471876</td>\n",
              "      <td>-0.516570</td>\n",
              "      <td>-0.551054</td>\n",
              "      <td>-0.589122</td>\n",
              "      <td>-0.616880</td>\n",
              "      <td>-0.490062</td>\n",
              "      <td>-0.392808</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.463864</td>\n",
              "      <td>-0.903020</td>\n",
              "      <td>-0.209404</td>\n",
              "      <td>-0.217548</td>\n",
              "      <td>-0.257163</td>\n",
              "      <td>-0.270787</td>\n",
              "      <td>-0.265054</td>\n",
              "      <td>-0.281805</td>\n",
              "      <td>-0.458884</td>\n",
              "      <td>-0.524195</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.376479</td>\n",
              "      <td>0.483012</td>\n",
              "      <td>0.077742</td>\n",
              "      <td>0.034887</td>\n",
              "      <td>-0.076958</td>\n",
              "      <td>-0.252331</td>\n",
              "      <td>-0.029786</td>\n",
              "      <td>-0.089248</td>\n",
              "      <td>0.388210</td>\n",
              "      <td>0.432781</td>\n",
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              "      <td>9.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 65 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a4a1bd6-5f70-4da9-a937-5958d9868542')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6a4a1bd6-5f70-4da9-a937-5958d9868542 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6a4a1bd6-5f70-4da9-a937-5958d9868542');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0cad082b-1f11-4261-8483-e7d15d7d1d54 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test_processed_df"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train_resampled.value_counts())\n",
        "print(y_test.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EMRg7d-kJWx",
        "outputId": "86bb27cb-d8a4-4103-9b9c-5ac11da559ef"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "default_payment_next_month\n",
            "0    18668\n",
            "1    18668\n",
            "Name: count, dtype: int64\n",
            "default_payment_next_month\n",
            "0    4667\n",
            "1    1326\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "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": "cf6d1fcc-91d2-4673-cebc-e96a9931eb4b"
      },
      "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",
        "target = 'default_payment_next_month'\n",
        "# Define primary, bill, and pay columns\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",
        "ordinal_columns = ['education', 'pay_delay_9_september', 'pay_delay_8_august', 'pay_delay_7_july', 'pay_delay_6_june', 'pay_delay_5_may', 'pay_delay_4_april']\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",
        "categorical_columns = [\n",
        "    'default_payment_next_month', 'sex', 'education', 'marriage'\n",
        "]\n",
        "\n",
        "ordinal_columns = [\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_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",
        "\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(ordinal_columns, 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",
        "#--------   Plot Selected Features  --------#\n",
        "\n",
        "def plot_selected_features(selected_features, model_name, figsize=(16,8)):\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('Feature')\n",
        "    plt.show()\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": "41bfb946-1365-4295-ff56-5c52cf12447e"
      },
      "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": "9e65e9a7-8be9-4d89-f107-866b073fbdf7"
      },
      "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 Pipeline Script"
      ],
      "metadata": {
        "id": "jTdy0ysdGptt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9TfChdMGhTF",
        "outputId": "dcfd4f2e-f9f2-493e-cc8b-733e5fc62163"
      },
      "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 ------------#\n",
        "\n",
        "# Pay Columns\n",
        "pay_columns = ['pay_amt_4_april', 'pay_amt_5_may',\n",
        "               'pay_amt_6_june', 'pay_amt_7_july',\n",
        "               'pay_amt_8_august', 'pay_amt_9_september']\n",
        "\n",
        "# Bill Columns\n",
        "bill_columns = ['bill_amt_4_april', 'bill_amt_5_may',\n",
        "               'bill_amt_6_june', 'bill_amt_7_july',\n",
        "               'bill_amt_8_august', 'bill_amt_9_september']\n",
        "\n",
        "delay_columns = [\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",
        "# Numeric Features (New numeric features added)\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",
        "# ------- 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, though this should be rare due to the epsilon\n",
        "        X['debt_to_limit_ratio'].replace([float('inf'), -float('inf')], 0, inplace=True)\n",
        "\n",
        "        return X\n",
        "\n",
        "# ------- Education & Marriage Interaction Transformer -------#\n",
        "\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",
        "        # 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",
        "\n",
        "# ------- Education & Marriage Unknown Flag Transformer -------#\n",
        "\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",
        "        # 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",
        "\n",
        "# ------- Education & Marriage Unknown Flag Transformer -------#\n",
        "\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",
        "        # 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",
        "# ------- 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",
        "'''\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": [
        "#### END"
      ],
      "metadata": {
        "id": "5IdS9UxDiBIt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cU9U02Y5CpJ7"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNQo73xAwStjFOQM5W9APwH",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}