combined_models_version_predicting_NB_Claim.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyO1hNh0EMn0gc1deVfVViR5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fjadidi2001/DataScienceJourney/blob/master/combined_models_version_predicting_NB_Claim.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.pipeline import Pipeline as ImbPipeline\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "from sklearn.metrics import (\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    matthews_corrcoef,  # Added for Matthews Correlation\n",
        "    roc_auc_score,      # Added for AUC score\n",
        "    accuracy_score      # Added for test accuracy\n",
        ")\n"
      ],
      "metadata": {
        "id": "zyxePO1KjKly"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Imports necessary libraries for data manipulation (pandas, numpy)\n",
        "- Machine learning tools from scikit-learn\n",
        "- SMOTE for handling imbalanced data"
      ],
      "metadata": {
        "id": "2yE3wMl9XnQB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Load the dataset\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Specify file path\n",
        "file_path = '/content/drive/My Drive/telematics_syn.csv'\n",
        "\n",
        "# Import pandas (assuming you want to use it to read the CSV)\n",
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Step 2: Explore the data\n",
        "print(data.head())\n",
        "print(data.info())\n",
        "\n",
        "# Check for missing values\n",
        "print(data.isnull().sum())\n",
        "\n",
        "# Display basic statistics\n",
        "print(data.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EozoDZ6xjI0g",
        "outputId": "16169fbc-544e-4091-c1dd-ad0517a0de8c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "   Duration  Insured.age Insured.sex  Car.age  Marital  Car.use  Credit.score  \\\n",
            "0       366           45        Male       -1  Married  Commute         609.0   \n",
            "1       182           44      Female        3  Married  Commute         575.0   \n",
            "2       184           48      Female        6  Married  Commute         847.0   \n",
            "3       183           71        Male        6  Married  Private         842.0   \n",
            "4       183           84        Male       10  Married  Private         856.0   \n",
            "\n",
            "  Region  Annual.miles.drive  Years.noclaims  ...  Left.turn.intensity10  \\\n",
            "0  Urban             6213.71              25  ...                    1.0   \n",
            "1  Urban            12427.42              20  ...                   58.0   \n",
            "2  Urban            12427.42              14  ...                    0.0   \n",
            "3  Urban             6213.71              43  ...                    0.0   \n",
            "4  Urban             6213.71              65  ...                    2.0   \n",
            "\n",
            "   Left.turn.intensity11  Left.turn.intensity12  Right.turn.intensity08  \\\n",
            "0                    0.0                    0.0                     3.0   \n",
            "1                   24.0                   11.0                  1099.0   \n",
            "2                    0.0                    0.0                     0.0   \n",
            "3                    0.0                    0.0                     0.0   \n",
            "4                    0.0                    0.0                   325.0   \n",
            "\n",
            "   Right.turn.intensity09  Right.turn.intensity10  Right.turn.intensity11  \\\n",
            "0                     1.0                     0.0                     0.0   \n",
            "1                   615.0                   219.0                   101.0   \n",
            "2                     0.0                     0.0                     0.0   \n",
            "3                     0.0                     0.0                     0.0   \n",
            "4                   111.0                    18.0                     4.0   \n",
            "\n",
            "   Right.turn.intensity12  NB_Claim    AMT_Claim  \n",
            "0                     0.0         1  5100.171753  \n",
            "1                    40.0         1   883.554840  \n",
            "2                     0.0         0     0.000000  \n",
            "3                     0.0         0     0.000000  \n",
            "4                     2.0         0     0.000000  \n",
            "\n",
            "[5 rows x 52 columns]\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 52 columns):\n",
            " #   Column                  Non-Null Count   Dtype  \n",
            "---  ------                  --------------   -----  \n",
            " 0   Duration                100000 non-null  int64  \n",
            " 1   Insured.age             100000 non-null  int64  \n",
            " 2   Insured.sex             100000 non-null  object \n",
            " 3   Car.age                 100000 non-null  int64  \n",
            " 4   Marital                 100000 non-null  object \n",
            " 5   Car.use                 100000 non-null  object \n",
            " 6   Credit.score            100000 non-null  float64\n",
            " 7   Region                  100000 non-null  object \n",
            " 8   Annual.miles.drive      100000 non-null  float64\n",
            " 9   Years.noclaims          100000 non-null  int64  \n",
            " 10  Territory               100000 non-null  int64  \n",
            " 11  Annual.pct.driven       100000 non-null  float64\n",
            " 12  Total.miles.driven      100000 non-null  float64\n",
            " 13  Pct.drive.mon           100000 non-null  float64\n",
            " 14  Pct.drive.tue           100000 non-null  float64\n",
            " 15  Pct.drive.wed           100000 non-null  float64\n",
            " 16  Pct.drive.thr           100000 non-null  float64\n",
            " 17  Pct.drive.fri           100000 non-null  float64\n",
            " 18  Pct.drive.sat           100000 non-null  float64\n",
            " 19  Pct.drive.sun           100000 non-null  float64\n",
            " 20  Pct.drive.2hrs          100000 non-null  float64\n",
            " 21  Pct.drive.3hrs          100000 non-null  float64\n",
            " 22  Pct.drive.4hrs          100000 non-null  float64\n",
            " 23  Pct.drive.wkday         100000 non-null  float64\n",
            " 24  Pct.drive.wkend         100000 non-null  float64\n",
            " 25  Pct.drive.rush am       100000 non-null  float64\n",
            " 26  Pct.drive.rush pm       100000 non-null  float64\n",
            " 27  Avgdays.week            100000 non-null  float64\n",
            " 28  Accel.06miles           100000 non-null  float64\n",
            " 29  Accel.08miles           100000 non-null  float64\n",
            " 30  Accel.09miles           100000 non-null  float64\n",
            " 31  Accel.11miles           100000 non-null  float64\n",
            " 32  Accel.12miles           100000 non-null  float64\n",
            " 33  Accel.14miles           100000 non-null  float64\n",
            " 34  Brake.06miles           100000 non-null  float64\n",
            " 35  Brake.08miles           100000 non-null  float64\n",
            " 36  Brake.09miles           100000 non-null  float64\n",
            " 37  Brake.11miles           100000 non-null  float64\n",
            " 38  Brake.12miles           100000 non-null  float64\n",
            " 39  Brake.14miles           100000 non-null  float64\n",
            " 40  Left.turn.intensity08   100000 non-null  float64\n",
            " 41  Left.turn.intensity09   100000 non-null  float64\n",
            " 42  Left.turn.intensity10   100000 non-null  float64\n",
            " 43  Left.turn.intensity11   100000 non-null  float64\n",
            " 44  Left.turn.intensity12   100000 non-null  float64\n",
            " 45  Right.turn.intensity08  100000 non-null  float64\n",
            " 46  Right.turn.intensity09  100000 non-null  float64\n",
            " 47  Right.turn.intensity10  100000 non-null  float64\n",
            " 48  Right.turn.intensity11  100000 non-null  float64\n",
            " 49  Right.turn.intensity12  100000 non-null  float64\n",
            " 50  NB_Claim                100000 non-null  int64  \n",
            " 51  AMT_Claim               100000 non-null  float64\n",
            "dtypes: float64(42), int64(6), object(4)\n",
            "memory usage: 39.7+ MB\n",
            "None\n",
            "Duration                  0\n",
            "Insured.age               0\n",
            "Insured.sex               0\n",
            "Car.age                   0\n",
            "Marital                   0\n",
            "Car.use                   0\n",
            "Credit.score              0\n",
            "Region                    0\n",
            "Annual.miles.drive        0\n",
            "Years.noclaims            0\n",
            "Territory                 0\n",
            "Annual.pct.driven         0\n",
            "Total.miles.driven        0\n",
            "Pct.drive.mon             0\n",
            "Pct.drive.tue             0\n",
            "Pct.drive.wed             0\n",
            "Pct.drive.thr             0\n",
            "Pct.drive.fri             0\n",
            "Pct.drive.sat             0\n",
            "Pct.drive.sun             0\n",
            "Pct.drive.2hrs            0\n",
            "Pct.drive.3hrs            0\n",
            "Pct.drive.4hrs            0\n",
            "Pct.drive.wkday           0\n",
            "Pct.drive.wkend           0\n",
            "Pct.drive.rush am         0\n",
            "Pct.drive.rush pm         0\n",
            "Avgdays.week              0\n",
            "Accel.06miles             0\n",
            "Accel.08miles             0\n",
            "Accel.09miles             0\n",
            "Accel.11miles             0\n",
            "Accel.12miles             0\n",
            "Accel.14miles             0\n",
            "Brake.06miles             0\n",
            "Brake.08miles             0\n",
            "Brake.09miles             0\n",
            "Brake.11miles             0\n",
            "Brake.12miles             0\n",
            "Brake.14miles             0\n",
            "Left.turn.intensity08     0\n",
            "Left.turn.intensity09     0\n",
            "Left.turn.intensity10     0\n",
            "Left.turn.intensity11     0\n",
            "Left.turn.intensity12     0\n",
            "Right.turn.intensity08    0\n",
            "Right.turn.intensity09    0\n",
            "Right.turn.intensity10    0\n",
            "Right.turn.intensity11    0\n",
            "Right.turn.intensity12    0\n",
            "NB_Claim                  0\n",
            "AMT_Claim                 0\n",
            "dtype: int64\n",
            "            Duration    Insured.age        Car.age   Credit.score  \\\n",
            "count  100000.000000  100000.000000  100000.000000  100000.000000   \n",
            "mean      314.204060      51.378950       5.639720     800.888870   \n",
            "std        79.746222      15.467075       4.062135      83.382316   \n",
            "min        27.000000      16.000000      -2.000000     422.000000   \n",
            "25%       200.000000      39.000000       2.000000     766.000000   \n",
            "50%       365.000000      51.000000       5.000000     825.000000   \n",
            "75%       366.000000      63.000000       8.000000     856.000000   \n",
            "max       366.000000     103.000000      20.000000     900.000000   \n",
            "\n",
            "       Annual.miles.drive  Years.noclaims      Territory  Annual.pct.driven  \\\n",
            "count       100000.000000   100000.000000  100000.000000      100000.000000   \n",
            "mean          9124.122908       28.839960      56.531390           0.502294   \n",
            "std           3826.144730       16.123717      24.036518           0.299189   \n",
            "min              0.000000        0.000000      11.000000           0.002740   \n",
            "25%           6213.710000       15.000000      35.000000           0.249315   \n",
            "50%           7456.452000       29.000000      62.000000           0.490411   \n",
            "75%          12427.420000       41.000000      78.000000           0.753425   \n",
            "max          56731.172300       79.000000      91.000000           1.000000   \n",
            "\n",
            "       Total.miles.driven  Pct.drive.mon  ...  Left.turn.intensity10  \\\n",
            "count       100000.000000  100000.000000  ...          100000.000000   \n",
            "mean          4833.575303       0.139365  ...             551.574010   \n",
            "std           4545.943016       0.042807  ...           14687.929802   \n",
            "min              0.095298       0.000000  ...               0.000000   \n",
            "25%           1529.897500       0.120894  ...               0.000000   \n",
            "50%           3468.287765       0.137909  ...               3.000000   \n",
            "75%           6779.876842       0.155203  ...              30.000000   \n",
            "max          47282.603936       0.998172  ...          794380.000000   \n",
            "\n",
            "       Left.turn.intensity11  Left.turn.intensity12  Right.turn.intensity08  \\\n",
            "count          100000.000000          100000.000000           100000.000000   \n",
            "mean              487.340690             447.758420              843.461830   \n",
            "std             14198.331308           13719.790281            11630.185503   \n",
            "min                 0.000000               0.000000                0.000000   \n",
            "25%                 0.000000               0.000000               11.000000   \n",
            "50%                 1.000000               0.000000              122.000000   \n",
            "75%                 9.000000               2.000000              680.000000   \n",
            "max            793926.000000          793170.000000           841210.000000   \n",
            "\n",
            "       Right.turn.intensity09  Right.turn.intensity10  Right.turn.intensity11  \\\n",
            "count           100000.000000           100000.000000           100000.000000   \n",
            "mean               565.056100              326.654840              246.713120   \n",
            "std              10657.402935             9460.244357             8977.569994   \n",
            "min                  0.000000                0.000000                0.000000   \n",
            "25%                  3.000000                0.000000                0.000000   \n",
            "50%                 43.000000                7.000000                2.000000   \n",
            "75%                321.000000               81.000000               27.000000   \n",
            "max             841207.000000           841200.000000           841176.000000   \n",
            "\n",
            "       Right.turn.intensity12      NB_Claim      AMT_Claim  \n",
            "count           100000.000000  100000.00000  100000.000000  \n",
            "mean               198.753690       0.04494     137.602253  \n",
            "std               8585.177049       0.21813    1264.320056  \n",
            "min                  0.000000       0.00000       0.000000  \n",
            "25%                  0.000000       0.00000       0.000000  \n",
            "50%                  0.000000       0.00000       0.000000  \n",
            "75%                  9.000000       0.00000       0.000000  \n",
            "max             841144.000000       3.00000  104074.886700  \n",
            "\n",
            "[8 rows x 48 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Data Loading:\n",
        "\n",
        "    - Mounts Google Drive\n",
        "    - Reads the telematics dataset from CSV\n",
        "\n",
        "\n",
        "- Data Exploration:\n",
        "\n",
        "    - Shows first few rows with head()\n",
        "    - Displays dataset info and statistics\n",
        "    - Checks for missing values"
      ],
      "metadata": {
        "id": "n8G81L1PX5kL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and target\n",
        "X = data.drop(['NB_Claim', 'AMT_Claim'], axis=1)\n",
        "y = (data['NB_Claim'] > 0).astype(int)  # Convert to binary (0 for no claim, 1 for claim)"
      ],
      "metadata": {
        "id": "BAW6-84xjgXz"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check class balance\n",
        "class_counts = y.value_counts()\n",
        "print(\"Class distribution:\")\n",
        "print(class_counts)\n",
        "print(f\"Class balance ratio: {class_counts[0] / class_counts[1]:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eX-VMoCloHAF",
        "outputId": "a7fad83e-646b-4e9e-943f-ebb9a909b683"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution:\n",
            "NB_Claim\n",
            "0    95728\n",
            "1     4272\n",
            "Name: count, dtype: int64\n",
            "Class balance ratio: 22.41\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Separates features (X) and target (y)\n",
        "- Converts claims to binary classification (0 for no claim, 1 for claim)\n",
        "- Checks class distribution and imbalance"
      ],
      "metadata": {
        "id": "E6e6pQZ2YKB0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "cc7Rq-zSoEaY"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define numeric and categorical columns\n",
        "numeric_features = X.select_dtypes(include=['int64', 'float64']).columns\n",
        "categorical_features = ['Insured.sex', 'Marital', 'Car.use', 'Region']\n",
        "\n",
        "# Create preprocessor\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', StandardScaler(), numeric_features),\n",
        "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)\n",
        "    ])"
      ],
      "metadata": {
        "id": "-aAec1jJjjkA"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Splits data into training and test sets\n",
        "- Creates preprocessor for both numeric and categorical features\n",
        "- Standardizes numeric features\n",
        "- One-hot encodes categorical features"
      ],
      "metadata": {
        "id": "LyXgwx00YZox"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute class weights\n",
        "class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
        "class_weight_dict = dict(zip(np.unique(y_train), class_weights))"
      ],
      "metadata": {
        "id": "Xy8BhtjmDv9h"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Random Forest classifier with balanced class weights\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=42, n_jobs=-1)\n"
      ],
      "metadata": {
        "id": "_9jK2mk9lAn7"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the full pipeline\n",
        "pipeline = Pipeline([\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('classifier', rf_classifier)\n",
        "])"
      ],
      "metadata": {
        "id": "WyJgyO49lFBb"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "n_AMdYd4hunO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "8dcf8acd-80a4-478b-8205-1f2a1be930e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
              "                                                  Index(['Duration', 'Insured.age', 'Car.age', 'Credit.score',\n",
              "       'Annual.miles.drive', 'Years.noclaims', 'Territory',\n",
              "       'Annual.pct.driven', 'Total.miles.driven', 'Pct.drive.mon',\n",
              "       'Pct.drive.tue', 'Pct.drive.wed', 'Pct.drive.thr', 'Pct.drive.fri',\n",
              "       'Pct.drive.sat', 'Pct.drive.sun', 'P...\n",
              "       'Left.turn.intensity12', 'Right.turn.intensity08',\n",
              "       'Right.turn.intensity09', 'Right.turn.intensity10',\n",
              "       'Right.turn.intensity11', 'Right.turn.intensity12'],\n",
              "      dtype='object')),\n",
              "                                                 ('cat',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
              "                                                  ['Insured.sex', 'Marital',\n",
              "                                                   'Car.use', 'Region'])])),\n",
              "                ('classifier',\n",
              "                 RandomForestClassifier(class_weight='balanced', n_jobs=-1,\n",
              "                                        random_state=42))])"
            ],
            "text/html": [
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              "  --sklearn-color-text: black;\n",
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              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
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              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                                  Index([&#x27;Duration&#x27;, &#x27;Insured.age&#x27;, &#x27;Car.age&#x27;, &#x27;Credit.score&#x27;,\n",
              "       &#x27;Annual.miles.drive&#x27;, &#x27;Years.noclaims&#x27;, &#x27;Territory&#x27;,\n",
              "       &#x27;Annual.pct.driven&#x27;, &#x27;Total.miles.driven&#x27;, &#x27;Pct.drive.mon&#x27;,\n",
              "       &#x27;Pct.drive.tue&#x27;, &#x27;Pct.drive.wed&#x27;, &#x27;Pct.drive.thr&#x27;, &#x27;Pct.drive.fri&#x27;,\n",
              "       &#x27;Pct.drive.sat&#x27;, &#x27;Pct.drive.sun&#x27;, &#x27;P...\n",
              "       &#x27;Left.turn.intensity12&#x27;, &#x27;Right.turn.intensity08&#x27;,\n",
              "       &#x27;Right.turn.intensity09&#x27;, &#x27;Right.turn.intensity10&#x27;,\n",
              "       &#x27;Right.turn.intensity11&#x27;, &#x27;Right.turn.intensity12&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                                  [&#x27;Insured.sex&#x27;, &#x27;Marital&#x27;,\n",
              "                                                   &#x27;Car.use&#x27;, &#x27;Region&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=-1,\n",
              "                                        random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                                  Index([&#x27;Duration&#x27;, &#x27;Insured.age&#x27;, &#x27;Car.age&#x27;, &#x27;Credit.score&#x27;,\n",
              "       &#x27;Annual.miles.drive&#x27;, &#x27;Years.noclaims&#x27;, &#x27;Territory&#x27;,\n",
              "       &#x27;Annual.pct.driven&#x27;, &#x27;Total.miles.driven&#x27;, &#x27;Pct.drive.mon&#x27;,\n",
              "       &#x27;Pct.drive.tue&#x27;, &#x27;Pct.drive.wed&#x27;, &#x27;Pct.drive.thr&#x27;, &#x27;Pct.drive.fri&#x27;,\n",
              "       &#x27;Pct.drive.sat&#x27;, &#x27;Pct.drive.sun&#x27;, &#x27;P...\n",
              "       &#x27;Left.turn.intensity12&#x27;, &#x27;Right.turn.intensity08&#x27;,\n",
              "       &#x27;Right.turn.intensity09&#x27;, &#x27;Right.turn.intensity10&#x27;,\n",
              "       &#x27;Right.turn.intensity11&#x27;, &#x27;Right.turn.intensity12&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                                  [&#x27;Insured.sex&#x27;, &#x27;Marital&#x27;,\n",
              "                                                   &#x27;Car.use&#x27;, &#x27;Region&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=-1,\n",
              "                                        random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                 Index([&#x27;Duration&#x27;, &#x27;Insured.age&#x27;, &#x27;Car.age&#x27;, &#x27;Credit.score&#x27;,\n",
              "       &#x27;Annual.miles.drive&#x27;, &#x27;Years.noclaims&#x27;, &#x27;Territory&#x27;,\n",
              "       &#x27;Annual.pct.driven&#x27;, &#x27;Total.miles.driven&#x27;, &#x27;Pct.drive.mon&#x27;,\n",
              "       &#x27;Pct.drive.tue&#x27;, &#x27;Pct.drive.wed&#x27;, &#x27;Pct.drive.thr&#x27;, &#x27;Pct.drive.fri&#x27;,\n",
              "       &#x27;Pct.drive.sat&#x27;, &#x27;Pct.drive.sun&#x27;, &#x27;Pct.drive.2hrs&#x27;, &#x27;Pct.drive.3hrs&#x27;,...\n",
              "       &#x27;Left.turn.intensity08&#x27;, &#x27;Left.turn.intensity09&#x27;,\n",
              "       &#x27;Left.turn.intensity10&#x27;, &#x27;Left.turn.intensity11&#x27;,\n",
              "       &#x27;Left.turn.intensity12&#x27;, &#x27;Right.turn.intensity08&#x27;,\n",
              "       &#x27;Right.turn.intensity09&#x27;, &#x27;Right.turn.intensity10&#x27;,\n",
              "       &#x27;Right.turn.intensity11&#x27;, &#x27;Right.turn.intensity12&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
              "                                 [&#x27;Insured.sex&#x27;, &#x27;Marital&#x27;, &#x27;Car.use&#x27;,\n",
              "                                  &#x27;Region&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;Duration&#x27;, &#x27;Insured.age&#x27;, &#x27;Car.age&#x27;, &#x27;Credit.score&#x27;,\n",
              "       &#x27;Annual.miles.drive&#x27;, &#x27;Years.noclaims&#x27;, &#x27;Territory&#x27;,\n",
              "       &#x27;Annual.pct.driven&#x27;, &#x27;Total.miles.driven&#x27;, &#x27;Pct.drive.mon&#x27;,\n",
              "       &#x27;Pct.drive.tue&#x27;, &#x27;Pct.drive.wed&#x27;, &#x27;Pct.drive.thr&#x27;, &#x27;Pct.drive.fri&#x27;,\n",
              "       &#x27;Pct.drive.sat&#x27;, &#x27;Pct.drive.sun&#x27;, &#x27;Pct.drive.2hrs&#x27;, &#x27;Pct.drive.3hrs&#x27;,\n",
              "       &#x27;Pct.drive.4hrs&#x27;, &#x27;Pct.drive.wkday&#x27;, &#x27;Pct.drive.wkend&#x27;,\n",
              "       &#x27;Pct.drive.rush am&#x27;, &#x27;Pct.drive.rush pm&#x27;, &#x27;Avgdays.week&#x27;,\n",
              "       &#x27;Accel.06miles&#x27;, &#x27;Accel.08miles&#x27;, &#x27;Accel.09miles&#x27;, &#x27;Accel.11miles&#x27;,\n",
              "       &#x27;Accel.12miles&#x27;, &#x27;Accel.14miles&#x27;, &#x27;Brake.06miles&#x27;, &#x27;Brake.08miles&#x27;,\n",
              "       &#x27;Brake.09miles&#x27;, &#x27;Brake.11miles&#x27;, &#x27;Brake.12miles&#x27;, &#x27;Brake.14miles&#x27;,\n",
              "       &#x27;Left.turn.intensity08&#x27;, &#x27;Left.turn.intensity09&#x27;,\n",
              "       &#x27;Left.turn.intensity10&#x27;, &#x27;Left.turn.intensity11&#x27;,\n",
              "       &#x27;Left.turn.intensity12&#x27;, &#x27;Right.turn.intensity08&#x27;,\n",
              "       &#x27;Right.turn.intensity09&#x27;, &#x27;Right.turn.intensity10&#x27;,\n",
              "       &#x27;Right.turn.intensity11&#x27;, &#x27;Right.turn.intensity12&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Insured.sex&#x27;, &#x27;Marital&#x27;, &#x27;Car.use&#x27;, &#x27;Region&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, n_jobs=-1, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Fit the pipeline\n",
        "pipeline.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Computes class weights to handle imbalanced data\n",
        "- Creates Random Forest classifier with balanced weights\n",
        "- Builds pipeline combining preprocessor and classifier\n",
        "- Trains the model"
      ],
      "metadata": {
        "id": "NSJ6lRyqYptA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "y_pred = pipeline.predict(X_test)\n",
        "\n",
        "# Calculate probabilities for AUC score\n",
        "y_pred_proba = pipeline.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Calculate and print all metrics\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "# New metrics\n",
        "mcc = matthews_corrcoef(y_test, y_pred)\n",
        "auc = roc_auc_score(y_test, y_pred_proba)\n",
        "test_accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "print(f\"\\nAdditional Metrics:\")\n",
        "print(f\"Matthews Correlation Coefficient: {mcc:.3f}\")\n",
        "print(f\"AUC Score: {auc:.3f}\")\n",
        "print(f\"Test Accuracy: {test_accuracy:.3f}\")\n",
        "\n",
        "# Cross-validation\n",
        "cv_scores = cross_val_score(pipeline, X, y, cv=5)\n",
        "print(f\"\\nCross-validation scores: {cv_scores}\")\n",
        "print(f\"Mean CV score: {cv_scores.mean():.3f} (+/- {cv_scores.std() * 2:.3f})\")\n",
        "\n",
        "# Feature importance analysis\n",
        "feature_importance = pipeline.named_steps['classifier'].feature_importances_\n",
        "feature_names = (preprocessor.named_transformers_['num'].get_feature_names_out().tolist() +\n",
        "                preprocessor.named_transformers_['cat'].get_feature_names_out().tolist())\n",
        "\n",
        "indices = np.argsort(feature_importance)[::-1]\n",
        "\n",
        "print(\"\\nTop 20 Feature ranking:\")\n",
        "for f in range(min(20, len(feature_names))):\n",
        "    print(\"%d. %s (%f)\" % (f + 1, feature_names[indices[f]], feature_importance[indices[f]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEqXr63alJas",
        "outputId": "0e45d02d-118b-46e6-834c-d15d7c549a54"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.98     19130\n",
            "           1       0.97      0.26      0.41       870\n",
            "\n",
            "    accuracy                           0.97     20000\n",
            "   macro avg       0.97      0.63      0.70     20000\n",
            "weighted avg       0.97      0.97      0.96     20000\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[19122     8]\n",
            " [  641   229]]\n",
            "\n",
            "Additional Metrics:\n",
            "Matthews Correlation Coefficient: 0.495\n",
            "AUC Score: 0.938\n",
            "Test Accuracy: 0.968\n",
            "\n",
            "Cross-validation scores: [0.968   0.9677  0.96725 0.96695 0.9692 ]\n",
            "Mean CV score: 0.968 (+/- 0.002)\n",
            "\n",
            "Top 20 Feature ranking:\n",
            "1. Total.miles.driven (0.103526)\n",
            "2. Annual.pct.driven (0.092099)\n",
            "3. Duration (0.033678)\n",
            "4. Credit.score (0.032826)\n",
            "5. Pct.drive.2hrs (0.030017)\n",
            "6. Avgdays.week (0.030016)\n",
            "7. Pct.drive.thr (0.028129)\n",
            "8. Pct.drive.rush am (0.027172)\n",
            "9. Pct.drive.rush pm (0.024612)\n",
            "10. Pct.drive.wed (0.024558)\n",
            "11. Pct.drive.mon (0.024377)\n",
            "12. Pct.drive.tue (0.023702)\n",
            "13. Pct.drive.fri (0.023537)\n",
            "14. Brake.06miles (0.022965)\n",
            "15. Pct.drive.sat (0.022950)\n",
            "16. Pct.drive.sun (0.022392)\n",
            "17. Car.age (0.021313)\n",
            "18. Accel.06miles (0.020572)\n",
            "19. Years.noclaims (0.020419)\n",
            "20. Left.turn.intensity09 (0.020307)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation:\n",
        "\n",
        "- Makes predictions on test set\n",
        "- Calculates various metrics:\n",
        "\n",
        "    - Classification report (precision, recall, F1-score)\n",
        "    - Confusion matrix\n",
        "\n",
        "    - Matthews Correlation Coefficient (MCC): Measures correlation between predicted and actual values\n",
        "    - AUC Score: Area Under the ROC Curve\n",
        "    - Test Accuracy: Simple accuracy score\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "- Performs cross-validation\n",
        "- Analyzes feature importance"
      ],
      "metadata": {
        "id": "3OUcn441Zs13"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch_tabnet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9T_v7lk2IUB3",
        "outputId": "18bf60ff-4637-4218-d509-4c9c9c3bb7eb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pytorch_tabnet\n",
            "  Downloading pytorch_tabnet-4.1.0-py3-none-any.whl.metadata (15 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (1.26.4)\n",
            "Requirement already satisfied: scikit_learn>0.21 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (1.5.2)\n",
            "Requirement already satisfied: scipy>1.4 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (1.13.1)\n",
            "Requirement already satisfied: torch>=1.3 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (2.4.1+cu121)\n",
            "Requirement already satisfied: tqdm>=4.36 in /usr/local/lib/python3.10/dist-packages (from pytorch_tabnet) (4.66.5)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn>0.21->pytorch_tabnet) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn>0.21->pytorch_tabnet) (3.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (1.13.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.3->pytorch_tabnet) (2024.6.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.3->pytorch_tabnet) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.3->pytorch_tabnet) (1.3.0)\n",
            "Downloading pytorch_tabnet-4.1.0-py3-none-any.whl (44 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.5/44.5 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pytorch_tabnet\n",
            "Successfully installed pytorch_tabnet-4.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score\n",
        "from pytorch_tabnet.tab_model import TabNetClassifier\n",
        "import torch"
      ],
      "metadata": {
        "id": "J3vBsDChIfRB"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select top 20 features based on the provided ranking\n",
        "top_features = [\n",
        "    'Total.miles.driven', 'Annual.pct.driven', 'Duration', 'Credit.score',\n",
        "    'Pct.drive.2hrs', 'Avgdays.week', 'Pct.drive.thr', 'Pct.drive.rush am',\n",
        "    'Pct.drive.rush pm', 'Pct.drive.wed', 'Pct.drive.mon', 'Pct.drive.tue',\n",
        "    'Pct.drive.fri', 'Brake.06miles', 'Pct.drive.sat', 'Pct.drive.sun',\n",
        "    'Car.age', 'Accel.06miles', 'Years.noclaims', 'Left.turn.intensity09'\n",
        "]"
      ],
      "metadata": {
        "id": "Js4C4zjbIiaq"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the data\n",
        "X = data[top_features]\n",
        "y = (data['NB_Claim'] > 0).astype(int)\n",
        "\n",
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n"
      ],
      "metadata": {
        "id": "UQOt6WJxInVX"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "3sQKq2sfIqDz"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to PyTorch tensors\n",
        "X_train_tensor = torch.FloatTensor(X_train_scaled)\n",
        "y_train_tensor = torch.LongTensor(y_train.values)\n",
        "X_test_tensor = torch.FloatTensor(X_test_scaled)\n",
        "y_test_tensor = torch.LongTensor(y_test.values)"
      ],
      "metadata": {
        "id": "cprfpj6wIsfd"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize and train TabNet\n",
        "tabnet_model = TabNetClassifier(\n",
        "    n_d=8, n_a=8, n_steps=3,\n",
        "    gamma=1.3, n_independent=2, n_shared=2,\n",
        "    cat_idxs=[], cat_dims=[],\n",
        "    cat_emb_dim=1, optimizer_fn=torch.optim.Adam,\n",
        "    optimizer_params=dict(lr=2e-2),\n",
        "    mask_type='entmax',\n",
        "    lambda_sparse=1e-3, momentum=0.3, clip_value=2.,\n",
        "    device_name='auto'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wBkbtIZIvIo",
        "outputId": "2dbfc025-76c9-4740-ec61-69bc1a067715"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
            "  warnings.warn(f\"Device used : {self.device}\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabnet_model.fit(\n",
        "    X_train_tensor.numpy(), y_train_tensor.numpy(),\n",
        "    eval_set=[(X_test_tensor.numpy(), y_test_tensor.numpy())],\n",
        "    max_epochs=100,\n",
        "    patience=10,\n",
        "    batch_size=1024,\n",
        "    virtual_batch_size=128,\n",
        "    num_workers=0,\n",
        "    drop_last=False\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmVNCFkGISKq",
        "outputId": "260bf663-0193-4c5b-e87c-eb23e917216d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 0  | loss: 0.18476 | val_0_auc: 0.76645 |  0:00:05s\n",
            "epoch 1  | loss: 0.15592 | val_0_auc: 0.77502 |  0:00:09s\n",
            "epoch 2  | loss: 0.15475 | val_0_auc: 0.78037 |  0:00:12s\n",
            "epoch 3  | loss: 0.15396 | val_0_auc: 0.78723 |  0:00:15s\n",
            "epoch 4  | loss: 0.15322 | val_0_auc: 0.78997 |  0:00:20s\n",
            "epoch 5  | loss: 0.15155 | val_0_auc: 0.79364 |  0:00:23s\n",
            "epoch 6  | loss: 0.15093 | val_0_auc: 0.79293 |  0:00:27s\n",
            "epoch 7  | loss: 0.14993 | val_0_auc: 0.79093 |  0:00:30s\n",
            "epoch 8  | loss: 0.1509  | val_0_auc: 0.78905 |  0:00:34s\n",
            "epoch 9  | loss: 0.15087 | val_0_auc: 0.78983 |  0:00:38s\n",
            "epoch 10 | loss: 0.1501  | val_0_auc: 0.79775 |  0:00:41s\n",
            "epoch 11 | loss: 0.14953 | val_0_auc: 0.79415 |  0:00:46s\n",
            "epoch 12 | loss: 0.14887 | val_0_auc: 0.79751 |  0:00:49s\n",
            "epoch 13 | loss: 0.14989 | val_0_auc: 0.79007 |  0:00:53s\n",
            "epoch 14 | loss: 0.14928 | val_0_auc: 0.79409 |  0:00:58s\n",
            "epoch 15 | loss: 0.14901 | val_0_auc: 0.79624 |  0:01:02s\n",
            "epoch 16 | loss: 0.14865 | val_0_auc: 0.80019 |  0:01:06s\n",
            "epoch 17 | loss: 0.14825 | val_0_auc: 0.80078 |  0:01:09s\n",
            "epoch 18 | loss: 0.14776 | val_0_auc: 0.80028 |  0:01:14s\n",
            "epoch 19 | loss: 0.14767 | val_0_auc: 0.80206 |  0:01:17s\n",
            "epoch 20 | loss: 0.14673 | val_0_auc: 0.79949 |  0:01:21s\n",
            "epoch 21 | loss: 0.14823 | val_0_auc: 0.80005 |  0:01:25s\n",
            "epoch 22 | loss: 0.14824 | val_0_auc: 0.79916 |  0:01:29s\n",
            "epoch 23 | loss: 0.1472  | val_0_auc: 0.80151 |  0:01:32s\n",
            "epoch 24 | loss: 0.14649 | val_0_auc: 0.80475 |  0:01:36s\n",
            "epoch 25 | loss: 0.14647 | val_0_auc: 0.79834 |  0:01:41s\n",
            "epoch 26 | loss: 0.14739 | val_0_auc: 0.80159 |  0:01:45s\n",
            "epoch 27 | loss: 0.14614 | val_0_auc: 0.79708 |  0:01:48s\n",
            "epoch 28 | loss: 0.14653 | val_0_auc: 0.80743 |  0:01:52s\n",
            "epoch 29 | loss: 0.14607 | val_0_auc: 0.80815 |  0:01:56s\n",
            "epoch 30 | loss: 0.14524 | val_0_auc: 0.8089  |  0:02:00s\n",
            "epoch 31 | loss: 0.1455  | val_0_auc: 0.8045  |  0:02:03s\n",
            "epoch 32 | loss: 0.14483 | val_0_auc: 0.80882 |  0:02:08s\n",
            "epoch 33 | loss: 0.14486 | val_0_auc: 0.80754 |  0:02:11s\n",
            "epoch 34 | loss: 0.14457 | val_0_auc: 0.81013 |  0:02:15s\n",
            "epoch 35 | loss: 0.14455 | val_0_auc: 0.80767 |  0:02:19s\n",
            "epoch 36 | loss: 0.14386 | val_0_auc: 0.80298 |  0:02:23s\n",
            "epoch 37 | loss: 0.14317 | val_0_auc: 0.81415 |  0:02:26s\n",
            "epoch 38 | loss: 0.14328 | val_0_auc: 0.80455 |  0:02:30s\n",
            "epoch 39 | loss: 0.14267 | val_0_auc: 0.81048 |  0:02:34s\n",
            "epoch 40 | loss: 0.14263 | val_0_auc: 0.81623 |  0:02:38s\n",
            "epoch 41 | loss: 0.14182 | val_0_auc: 0.81401 |  0:02:41s\n",
            "epoch 42 | loss: 0.14151 | val_0_auc: 0.81659 |  0:02:45s\n",
            "epoch 43 | loss: 0.14216 | val_0_auc: 0.81819 |  0:02:49s\n",
            "epoch 44 | loss: 0.14238 | val_0_auc: 0.8167  |  0:02:53s\n",
            "epoch 45 | loss: 0.14187 | val_0_auc: 0.81678 |  0:02:56s\n",
            "epoch 46 | loss: 0.14057 | val_0_auc: 0.8177  |  0:03:00s\n",
            "epoch 47 | loss: 0.14222 | val_0_auc: 0.81146 |  0:03:04s\n",
            "epoch 48 | loss: 0.14125 | val_0_auc: 0.81201 |  0:03:08s\n",
            "epoch 49 | loss: 0.14033 | val_0_auc: 0.81767 |  0:03:11s\n",
            "epoch 50 | loss: 0.14054 | val_0_auc: 0.81844 |  0:03:16s\n",
            "epoch 51 | loss: 0.13963 | val_0_auc: 0.81814 |  0:03:19s\n",
            "epoch 52 | loss: 0.13978 | val_0_auc: 0.81636 |  0:03:23s\n",
            "epoch 53 | loss: 0.13945 | val_0_auc: 0.81724 |  0:03:27s\n",
            "epoch 54 | loss: 0.14015 | val_0_auc: 0.81592 |  0:03:31s\n",
            "epoch 55 | loss: 0.13936 | val_0_auc: 0.81843 |  0:03:34s\n",
            "epoch 56 | loss: 0.14005 | val_0_auc: 0.81545 |  0:03:37s\n",
            "epoch 57 | loss: 0.13834 | val_0_auc: 0.82027 |  0:03:42s\n",
            "epoch 58 | loss: 0.13739 | val_0_auc: 0.81704 |  0:03:46s\n",
            "epoch 59 | loss: 0.13741 | val_0_auc: 0.81711 |  0:03:49s\n",
            "epoch 60 | loss: 0.13795 | val_0_auc: 0.81697 |  0:03:53s\n",
            "epoch 61 | loss: 0.14054 | val_0_auc: 0.81156 |  0:03:57s\n",
            "epoch 62 | loss: 0.13926 | val_0_auc: 0.81645 |  0:04:00s\n",
            "epoch 63 | loss: 0.13707 | val_0_auc: 0.82179 |  0:04:04s\n",
            "epoch 64 | loss: 0.13965 | val_0_auc: 0.81591 |  0:04:08s\n",
            "epoch 65 | loss: 0.13854 | val_0_auc: 0.8199  |  0:04:12s\n",
            "epoch 66 | loss: 0.13868 | val_0_auc: 0.81878 |  0:04:15s\n",
            "epoch 67 | loss: 0.13635 | val_0_auc: 0.8212  |  0:04:19s\n",
            "epoch 68 | loss: 0.13821 | val_0_auc: 0.82181 |  0:04:23s\n",
            "epoch 69 | loss: 0.13863 | val_0_auc: 0.81855 |  0:04:27s\n",
            "epoch 70 | loss: 0.13905 | val_0_auc: 0.81613 |  0:04:30s\n",
            "epoch 71 | loss: 0.13809 | val_0_auc: 0.8204  |  0:04:35s\n",
            "epoch 72 | loss: 0.13645 | val_0_auc: 0.82041 |  0:04:38s\n",
            "epoch 73 | loss: 0.13578 | val_0_auc: 0.82164 |  0:04:42s\n",
            "epoch 74 | loss: 0.1362  | val_0_auc: 0.82229 |  0:04:45s\n",
            "epoch 75 | loss: 0.13633 | val_0_auc: 0.8228  |  0:04:50s\n",
            "epoch 76 | loss: 0.13623 | val_0_auc: 0.8206  |  0:04:53s\n",
            "epoch 77 | loss: 0.13566 | val_0_auc: 0.81507 |  0:04:57s\n",
            "epoch 78 | loss: 0.13674 | val_0_auc: 0.82215 |  0:05:01s\n",
            "epoch 79 | loss: 0.13532 | val_0_auc: 0.82203 |  0:05:05s\n",
            "epoch 80 | loss: 0.13522 | val_0_auc: 0.8172  |  0:05:08s\n",
            "epoch 81 | loss: 0.1348  | val_0_auc: 0.82592 |  0:05:12s\n",
            "epoch 82 | loss: 0.13428 | val_0_auc: 0.82393 |  0:05:16s\n",
            "epoch 83 | loss: 0.13407 | val_0_auc: 0.81678 |  0:05:20s\n",
            "epoch 84 | loss: 0.13517 | val_0_auc: 0.82453 |  0:05:23s\n",
            "epoch 85 | loss: 0.13406 | val_0_auc: 0.81972 |  0:05:28s\n",
            "epoch 86 | loss: 0.1331  | val_0_auc: 0.81935 |  0:05:31s\n",
            "epoch 87 | loss: 0.13314 | val_0_auc: 0.82615 |  0:05:35s\n",
            "epoch 88 | loss: 0.1332  | val_0_auc: 0.82647 |  0:05:38s\n",
            "epoch 89 | loss: 0.13238 | val_0_auc: 0.83142 |  0:05:43s\n",
            "epoch 90 | loss: 0.13262 | val_0_auc: 0.82643 |  0:05:47s\n",
            "epoch 91 | loss: 0.13212 | val_0_auc: 0.8254  |  0:05:50s\n",
            "epoch 92 | loss: 0.13657 | val_0_auc: 0.82465 |  0:05:54s\n",
            "epoch 93 | loss: 0.13428 | val_0_auc: 0.83063 |  0:05:58s\n",
            "epoch 94 | loss: 0.13219 | val_0_auc: 0.82623 |  0:06:02s\n",
            "epoch 95 | loss: 0.13173 | val_0_auc: 0.81902 |  0:06:05s\n",
            "epoch 96 | loss: 0.13373 | val_0_auc: 0.82579 |  0:06:10s\n",
            "epoch 97 | loss: 0.13311 | val_0_auc: 0.82149 |  0:06:13s\n",
            "epoch 98 | loss: 0.13295 | val_0_auc: 0.82577 |  0:06:17s\n",
            "epoch 99 | loss: 0.13196 | val_0_auc: 0.82994 |  0:06:21s\n",
            "\n",
            "Early stopping occurred at epoch 99 with best_epoch = 89 and best_val_0_auc = 0.83142\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
            "  warnings.warn(wrn_msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Automatically detect device (GPU or CPU)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "#The TabNetClassifier does not have a to() method, instead it sets the device during initialization\n",
        "#Remove or comment out the following line:\n",
        "#tabnet_model.to(device)\n",
        "\n",
        "# Instead, ensure you specified the correct device when you initialized the model\n",
        "#If you are still encountering issues and your device is set to 'auto', you can set it using the device param\n",
        "#tabnet_model = TabNetClassifier(device_name=device) # Replace device with 'cuda' or 'cpu' as needed\n",
        "\n",
        "# Convert data to PyTorch tensors and move to the same device as the model\n",
        "X_train_tensor = torch.tensor(X_train_scaled, dtype=torch.float32).to(device)\n",
        "X_test_tensor = torch.tensor(X_test_scaled, dtype=torch.float32).to(device)\n",
        "\n",
        "# Extract learned features using the model's forward function to get embeddings\n",
        "with torch.no_grad():\n",
        "    # Make sure network method takes device into account\n",
        "    # Assuming the first element of the tuple is the desired output\n",
        "    output = tabnet_model.network(X_train_tensor.to(tabnet_model.device))\n",
        "    train_features = output[0].cpu().numpy()  # Access the first element and move to CPU for numpy\n",
        "\n",
        "    output = tabnet_model.network(X_test_tensor.to(tabnet_model.device))\n",
        "    test_features = output[0].cpu().numpy() # Access the first element and move to CPU for numpy\n",
        "\n",
        "\n",
        "# Train SVM on the extracted features\n",
        "svm_model = SVC(kernel='rbf', class_weight='balanced', probability=True, random_state=42)\n",
        "svm_model.fit(train_features, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = svm_model.predict(test_features)\n",
        "y_pred_proba = svm_model.predict_proba(test_features)[:, 1]\n",
        "\n",
        "# Print the classification report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Print the confusion matrix\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "# Calculate ROC AUC score\n",
        "roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
        "print(f\"\\nROC AUC Score: {roc_auc:.3f}\")\n",
        "\n",
        "# Feature importance from TabNet\n",
        "feat_importances = tabnet_model.feature_importances_\n",
        "feat_importance_dict = dict(zip(top_features, feat_importances))\n",
        "sorted_feat_importances = sorted(feat_importance_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "print(\"\\nTabNet Feature Importances:\")\n",
        "for feat, importance in sorted_feat_importances:\n",
        "    print(f\"{feat}: {importance:.6f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "djDbzv6_Ll86",
        "outputId": "8890c241-8e30-4c15-ad93-529973b758bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.70      0.82     19146\n",
            "           1       0.10      0.78      0.18       854\n",
            "\n",
            "    accuracy                           0.70     20000\n",
            "   macro avg       0.54      0.74      0.50     20000\n",
            "weighted avg       0.95      0.70      0.79     20000\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[13362  5784]\n",
            " [  187   667]]\n",
            "\n",
            "ROC AUC Score: 0.808\n",
            "\n",
            "TabNet Feature Importances:\n",
            "Duration: 0.285232\n",
            "Total.miles.driven: 0.184323\n",
            "Avgdays.week: 0.164837\n",
            "Annual.pct.driven: 0.113711\n",
            "Brake.06miles: 0.065162\n",
            "Pct.drive.sat: 0.038803\n",
            "Years.noclaims: 0.033412\n",
            "Credit.score: 0.031820\n",
            "Pct.drive.2hrs: 0.023103\n",
            "Pct.drive.sun: 0.021995\n",
            "Pct.drive.tue: 0.014535\n",
            "Pct.drive.rush am: 0.013778\n",
            "Car.age: 0.006937\n",
            "Pct.drive.mon: 0.002280\n",
            "Accel.06miles: 0.000035\n",
            "Pct.drive.rush pm: 0.000033\n",
            "Pct.drive.thr: 0.000003\n",
            "Pct.drive.fri: 0.000001\n",
            "Pct.drive.wed: 0.000001\n",
            "Left.turn.intensity09: 0.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import (\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    roc_auc_score,\n",
        "    matthews_corrcoef,\n",
        "    accuracy_score,\n",
        "    roc_curve\n",
        ")\n",
        "from pytorch_tabnet.tab_model import TabNetClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Select top 20 features\n",
        "top_features = [\n",
        "    'Total.miles.driven', 'Annual.pct.driven', 'Duration', 'Credit.score',\n",
        "    'Pct.drive.2hrs', 'Avgdays.week', 'Pct.drive.thr', 'Pct.drive.rush am',\n",
        "    'Pct.drive.rush pm', 'Pct.drive.wed', 'Pct.drive.mon', 'Pct.drive.tue',\n",
        "    'Pct.drive.fri', 'Brake.06miles', 'Pct.drive.sat', 'Pct.drive.sun',\n",
        "    'Car.age', 'Accel.06miles', 'Years.noclaims', 'Left.turn.intensity09'\n",
        "]\n",
        "\n",
        "# Prepare the data\n",
        "X = data[top_features]\n",
        "y = (data['NB_Claim'] > 0).astype(int)\n",
        "\n",
        "# Split the data\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",
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Convert to PyTorch tensors\n",
        "X_train_tensor = torch.FloatTensor(X_train_scaled)\n",
        "y_train_tensor = torch.LongTensor(y_train.values)\n",
        "X_test_tensor = torch.FloatTensor(X_test_scaled)\n",
        "y_test_tensor = torch.LongTensor(y_test.values)\n",
        "\n",
        "# Initialize and train TabNet\n",
        "tabnet_model = TabNetClassifier(\n",
        "    n_d=8, n_a=8, n_steps=3,\n",
        "    gamma=1.3, n_independent=2, n_shared=2,\n",
        "    cat_idxs=[], cat_dims=[],\n",
        "    cat_emb_dim=1, optimizer_fn=torch.optim.Adam,\n",
        "    optimizer_params=dict(lr=2e-2),\n",
        "    mask_type='entmax',\n",
        "    lambda_sparse=1e-3, momentum=0.3, clip_value=2.,\n",
        "    device_name='auto'\n",
        ")\n",
        "\n",
        "# Train TabNet\n",
        "tabnet_model.fit(\n",
        "    X_train_tensor.numpy(), y_train_tensor.numpy(),\n",
        "    eval_set=[(X_test_tensor.numpy(), y_test_tensor.numpy())],\n",
        "    max_epochs=100,\n",
        "    patience=10,\n",
        "    batch_size=1024,\n",
        "    virtual_batch_size=128,\n",
        "    num_workers=0,\n",
        "    drop_last=False\n",
        ")\n",
        "\n",
        "# Device configuration\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Convert data to PyTorch tensors\n",
        "X_train_tensor = torch.tensor(X_train_scaled, dtype=torch.float32).to(device)\n",
        "X_test_tensor = torch.tensor(X_test_scaled, dtype=torch.float32).to(device)\n",
        "\n",
        "# Extract learned features\n",
        "with torch.no_grad():\n",
        "    output = tabnet_model.network(X_train_tensor.to(tabnet_model.device))\n",
        "    train_features = output[0].cpu().numpy()\n",
        "\n",
        "    output = tabnet_model.network(X_test_tensor.to(tabnet_model.device))\n",
        "    test_features = output[0].cpu().numpy()\n",
        "\n",
        "# Train SVM\n",
        "svm_model = SVC(kernel='rbf', class_weight='balanced', probability=True, random_state=42)\n",
        "svm_model.fit(train_features, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = svm_model.predict(test_features)\n",
        "y_pred_proba = svm_model.predict_proba(test_features)[:, 1]\n",
        "\n",
        "# Calculate metrics\n",
        "mcc = matthews_corrcoef(y_test, y_pred)\n",
        "auc = roc_auc_score(y_test, y_pred_proba)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Print all metrics\n",
        "print(\"\\nModel Performance Metrics:\")\n",
        "print(\"-\" * 50)\n",
        "print(f\"Test Accuracy: {accuracy:.3f}\")\n",
        "print(f\"Matthews Correlation Coefficient: {mcc:.3f}\")\n",
        "print(f\"ROC AUC Score: {auc:.3f}\")\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Visualizations\n",
        "plt.figure(figsize=(15, 10))\n",
        "\n",
        "# 1. Confusion Matrix Heatmap\n",
        "plt.subplot(2, 2, 1)\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "\n",
        "# 2. ROC Curve\n",
        "plt.subplot(2, 2, 2)\n",
        "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
        "plt.plot(fpr, tpr)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.title(f'ROC Curve (AUC = {auc:.3f})')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "\n",
        "# 3. Feature Importance Plot\n",
        "plt.subplot(2, 2, 3)\n",
        "feat_importances = tabnet_model.feature_importances_\n",
        "importance_df = pd.DataFrame({\n",
        "    'Feature': top_features,\n",
        "    'Importance': feat_importances\n",
        "}).sort_values('Importance', ascending=True)\n",
        "\n",
        "plt.barh(range(len(importance_df)), importance_df['Importance'])\n",
        "plt.yticks(range(len(importance_df)), importance_df['Feature'])\n",
        "plt.title('TabNet Feature Importance')\n",
        "plt.xlabel('Importance Score')\n",
        "\n",
        "# 4. Prediction Distribution\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.histplot(data=pd.DataFrame({\n",
        "    'Probability': y_pred_proba,\n",
        "    'Actual': y_test\n",
        "}), x='Probability', hue='Actual', bins=50)\n",
        "plt.title('Prediction Probability Distribution')\n",
        "plt.xlabel('Predicted Probability of Positive Class')\n",
        "plt.ylabel('Count')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Save feature importances to DataFrame\n",
        "feat_importance_df = pd.DataFrame({\n",
        "    'Feature': top_features,\n",
        "    'Importance': feat_importances\n",
        "}).sort_values('Importance', ascending=False)\n",
        "\n",
        "print(\"\\nTabNet Feature Importances:\")\n",
        "print(feat_importance_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "i_ByxlMDcZeZ",
        "outputId": "ff080e75-b529-49db-bed6-002cb64459a8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
            "  warnings.warn(f\"Device used : {self.device}\")\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 0  | loss: 0.18476 | val_0_auc: 0.76645 |  0:00:03s\n",
            "epoch 1  | loss: 0.15592 | val_0_auc: 0.77502 |  0:00:07s\n",
            "epoch 2  | loss: 0.15475 | val_0_auc: 0.78037 |  0:00:11s\n",
            "epoch 3  | loss: 0.15396 | val_0_auc: 0.78723 |  0:00:14s\n",
            "epoch 4  | loss: 0.15322 | val_0_auc: 0.78997 |  0:00:18s\n",
            "epoch 5  | loss: 0.15155 | val_0_auc: 0.79364 |  0:00:22s\n",
            "epoch 6  | loss: 0.15093 | val_0_auc: 0.79293 |  0:00:26s\n",
            "epoch 7  | loss: 0.14993 | val_0_auc: 0.79093 |  0:00:29s\n",
            "epoch 8  | loss: 0.1509  | val_0_auc: 0.78905 |  0:00:33s\n",
            "epoch 9  | loss: 0.15087 | val_0_auc: 0.78983 |  0:00:37s\n",
            "epoch 10 | loss: 0.1501  | val_0_auc: 0.79775 |  0:00:40s\n",
            "epoch 11 | loss: 0.14953 | val_0_auc: 0.79415 |  0:00:44s\n",
            "epoch 12 | loss: 0.14887 | val_0_auc: 0.79751 |  0:00:48s\n",
            "epoch 13 | loss: 0.14989 | val_0_auc: 0.79007 |  0:00:51s\n",
            "epoch 14 | loss: 0.14928 | val_0_auc: 0.79409 |  0:00:55s\n",
            "epoch 15 | loss: 0.14901 | val_0_auc: 0.79624 |  0:00:58s\n",
            "epoch 16 | loss: 0.14865 | val_0_auc: 0.80019 |  0:01:03s\n",
            "epoch 17 | loss: 0.14825 | val_0_auc: 0.80078 |  0:01:06s\n",
            "epoch 18 | loss: 0.14776 | val_0_auc: 0.80028 |  0:01:09s\n",
            "epoch 19 | loss: 0.14767 | val_0_auc: 0.80206 |  0:01:13s\n",
            "epoch 20 | loss: 0.14673 | val_0_auc: 0.79949 |  0:01:17s\n",
            "epoch 21 | loss: 0.14823 | val_0_auc: 0.80005 |  0:01:20s\n",
            "epoch 22 | loss: 0.14824 | val_0_auc: 0.79916 |  0:01:24s\n",
            "epoch 23 | loss: 0.1472  | val_0_auc: 0.80151 |  0:01:28s\n",
            "epoch 24 | loss: 0.14649 | val_0_auc: 0.80475 |  0:01:32s\n",
            "epoch 25 | loss: 0.14647 | val_0_auc: 0.79834 |  0:01:35s\n",
            "epoch 26 | loss: 0.14739 | val_0_auc: 0.80159 |  0:01:38s\n",
            "epoch 27 | loss: 0.14614 | val_0_auc: 0.79708 |  0:01:43s\n",
            "epoch 28 | loss: 0.14653 | val_0_auc: 0.80743 |  0:01:46s\n",
            "epoch 29 | loss: 0.14607 | val_0_auc: 0.80815 |  0:01:50s\n",
            "epoch 30 | loss: 0.14524 | val_0_auc: 0.8089  |  0:01:54s\n",
            "epoch 31 | loss: 0.1455  | val_0_auc: 0.8045  |  0:01:58s\n",
            "epoch 32 | loss: 0.14483 | val_0_auc: 0.80882 |  0:02:01s\n",
            "epoch 33 | loss: 0.14486 | val_0_auc: 0.80754 |  0:02:05s\n",
            "epoch 34 | loss: 0.14457 | val_0_auc: 0.81013 |  0:02:09s\n",
            "epoch 35 | loss: 0.14455 | val_0_auc: 0.80767 |  0:02:12s\n",
            "epoch 36 | loss: 0.14386 | val_0_auc: 0.80298 |  0:02:16s\n",
            "epoch 37 | loss: 0.14317 | val_0_auc: 0.81415 |  0:02:20s\n",
            "epoch 38 | loss: 0.14328 | val_0_auc: 0.80455 |  0:02:24s\n",
            "epoch 39 | loss: 0.14267 | val_0_auc: 0.81048 |  0:02:27s\n",
            "epoch 40 | loss: 0.14263 | val_0_auc: 0.81623 |  0:02:31s\n",
            "epoch 41 | loss: 0.14182 | val_0_auc: 0.81401 |  0:02:35s\n",
            "epoch 42 | loss: 0.14151 | val_0_auc: 0.81659 |  0:02:38s\n",
            "epoch 43 | loss: 0.14216 | val_0_auc: 0.81819 |  0:02:42s\n",
            "epoch 44 | loss: 0.14238 | val_0_auc: 0.8167  |  0:02:46s\n",
            "epoch 45 | loss: 0.14187 | val_0_auc: 0.81678 |  0:02:50s\n",
            "epoch 46 | loss: 0.14057 | val_0_auc: 0.8177  |  0:02:53s\n",
            "epoch 47 | loss: 0.14222 | val_0_auc: 0.81146 |  0:02:56s\n",
            "epoch 48 | loss: 0.14125 | val_0_auc: 0.81201 |  0:03:01s\n",
            "epoch 49 | loss: 0.14033 | val_0_auc: 0.81767 |  0:03:04s\n",
            "epoch 50 | loss: 0.14054 | val_0_auc: 0.81844 |  0:03:08s\n",
            "epoch 51 | loss: 0.13963 | val_0_auc: 0.81814 |  0:03:11s\n",
            "epoch 52 | loss: 0.13978 | val_0_auc: 0.81636 |  0:03:16s\n",
            "epoch 53 | loss: 0.13945 | val_0_auc: 0.81724 |  0:03:19s\n",
            "epoch 54 | loss: 0.14015 | val_0_auc: 0.81592 |  0:03:22s\n",
            "epoch 55 | loss: 0.13936 | val_0_auc: 0.81843 |  0:03:26s\n",
            "epoch 56 | loss: 0.14005 | val_0_auc: 0.81545 |  0:03:30s\n",
            "epoch 57 | loss: 0.13834 | val_0_auc: 0.82027 |  0:03:34s\n",
            "epoch 58 | loss: 0.13739 | val_0_auc: 0.81704 |  0:03:37s\n",
            "epoch 59 | loss: 0.13741 | val_0_auc: 0.81711 |  0:03:42s\n",
            "epoch 60 | loss: 0.13795 | val_0_auc: 0.81697 |  0:03:45s\n",
            "epoch 61 | loss: 0.14054 | val_0_auc: 0.81156 |  0:03:49s\n",
            "epoch 62 | loss: 0.13926 | val_0_auc: 0.81645 |  0:03:52s\n",
            "epoch 63 | loss: 0.13707 | val_0_auc: 0.82179 |  0:03:57s\n",
            "epoch 64 | loss: 0.13965 | val_0_auc: 0.81591 |  0:04:00s\n",
            "epoch 65 | loss: 0.13854 | val_0_auc: 0.8199  |  0:04:03s\n",
            "epoch 66 | loss: 0.13868 | val_0_auc: 0.81878 |  0:04:08s\n",
            "epoch 67 | loss: 0.13635 | val_0_auc: 0.8212  |  0:04:11s\n",
            "epoch 68 | loss: 0.13821 | val_0_auc: 0.82181 |  0:04:15s\n",
            "epoch 69 | loss: 0.13863 | val_0_auc: 0.81855 |  0:04:18s\n",
            "epoch 70 | loss: 0.13905 | val_0_auc: 0.81613 |  0:04:23s\n",
            "epoch 71 | loss: 0.13809 | val_0_auc: 0.8204  |  0:04:26s\n",
            "epoch 72 | loss: 0.13645 | val_0_auc: 0.82041 |  0:04:29s\n",
            "epoch 73 | loss: 0.13578 | val_0_auc: 0.82164 |  0:04:33s\n",
            "epoch 74 | loss: 0.1362  | val_0_auc: 0.82229 |  0:04:37s\n",
            "epoch 75 | loss: 0.13633 | val_0_auc: 0.8228  |  0:04:41s\n",
            "epoch 76 | loss: 0.13623 | val_0_auc: 0.8206  |  0:04:44s\n",
            "epoch 77 | loss: 0.13566 | val_0_auc: 0.81507 |  0:04:49s\n",
            "epoch 78 | loss: 0.13674 | val_0_auc: 0.82215 |  0:04:52s\n",
            "epoch 79 | loss: 0.13532 | val_0_auc: 0.82203 |  0:04:56s\n",
            "epoch 80 | loss: 0.13522 | val_0_auc: 0.8172  |  0:04:59s\n",
            "epoch 81 | loss: 0.1348  | val_0_auc: 0.82592 |  0:05:03s\n",
            "epoch 82 | loss: 0.13428 | val_0_auc: 0.82393 |  0:05:07s\n",
            "epoch 83 | loss: 0.13407 | val_0_auc: 0.81678 |  0:05:10s\n",
            "epoch 84 | loss: 0.13517 | val_0_auc: 0.82453 |  0:05:14s\n",
            "epoch 85 | loss: 0.13406 | val_0_auc: 0.81972 |  0:05:18s\n",
            "epoch 86 | loss: 0.1331  | val_0_auc: 0.81935 |  0:05:21s\n",
            "epoch 87 | loss: 0.13314 | val_0_auc: 0.82615 |  0:05:25s\n",
            "epoch 88 | loss: 0.1332  | val_0_auc: 0.82647 |  0:05:29s\n",
            "epoch 89 | loss: 0.13238 | val_0_auc: 0.83142 |  0:05:33s\n",
            "epoch 90 | loss: 0.13262 | val_0_auc: 0.82643 |  0:05:36s\n",
            "epoch 91 | loss: 0.13212 | val_0_auc: 0.8254  |  0:05:40s\n",
            "epoch 92 | loss: 0.13657 | val_0_auc: 0.82465 |  0:05:44s\n",
            "epoch 93 | loss: 0.13428 | val_0_auc: 0.83063 |  0:05:47s\n",
            "epoch 94 | loss: 0.13219 | val_0_auc: 0.82623 |  0:05:51s\n",
            "epoch 95 | loss: 0.13173 | val_0_auc: 0.81902 |  0:05:55s\n",
            "epoch 96 | loss: 0.13373 | val_0_auc: 0.82579 |  0:05:59s\n",
            "epoch 97 | loss: 0.13311 | val_0_auc: 0.82149 |  0:06:02s\n",
            "epoch 98 | loss: 0.13295 | val_0_auc: 0.82577 |  0:06:05s\n",
            "epoch 99 | loss: 0.13196 | val_0_auc: 0.82994 |  0:06:10s\n",
            "\n",
            "Early stopping occurred at epoch 99 with best_epoch = 89 and best_val_0_auc = 0.83142\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
            "  warnings.warn(wrn_msg)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model Performance Metrics:\n",
            "--------------------------------------------------\n",
            "Test Accuracy: 0.773\n",
            "Matthews Correlation Coefficient: 0.242\n",
            "ROC AUC Score: 0.816\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.77      0.87     19146\n",
            "           1       0.13      0.74      0.22       854\n",
            "\n",
            "    accuracy                           0.77     20000\n",
            "   macro avg       0.56      0.76      0.54     20000\n",
            "weighted avg       0.95      0.77      0.84     20000\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[14837  4309]\n",
            " [  221   633]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TabNet Feature Importances:\n",
            "                  Feature    Importance\n",
            "2                Duration  2.293648e-01\n",
            "0      Total.miles.driven  2.114111e-01\n",
            "1       Annual.pct.driven  1.143686e-01\n",
            "5            Avgdays.week  1.113107e-01\n",
            "3            Credit.score  6.707104e-02\n",
            "16                Car.age  5.853172e-02\n",
            "13          Brake.06miles  3.307807e-02\n",
            "18         Years.noclaims  2.677336e-02\n",
            "15          Pct.drive.sun  2.517574e-02\n",
            "14          Pct.drive.sat  2.350950e-02\n",
            "17          Accel.06miles  2.089833e-02\n",
            "7       Pct.drive.rush am  2.033423e-02\n",
            "4          Pct.drive.2hrs  1.977351e-02\n",
            "10          Pct.drive.mon  1.941784e-02\n",
            "12          Pct.drive.fri  1.898156e-02\n",
            "11          Pct.drive.tue  8.525227e-11\n",
            "8       Pct.drive.rush pm  0.000000e+00\n",
            "9           Pct.drive.wed  0.000000e+00\n",
            "6           Pct.drive.thr  0.000000e+00\n",
            "19  Left.turn.intensity09  0.000000e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import (\n",
        "    precision_score, recall_score, f1_score,\n",
        "    roc_auc_score, accuracy_score, matthews_corrcoef,\n",
        "    confusion_matrix, classification_report,\n",
        "    precision_recall_curve, average_precision_score,\n",
        "    roc_curve\n",
        ")\n",
        "from pytorch_tabnet.tab_model import TabNetClassifier\n",
        "import xgboost as xgb\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Select features (you can modify this list based on your needs)\n",
        "features = [\n",
        "    'Total.miles.driven', 'Annual.pct.driven', 'Duration', 'Credit.score',\n",
        "    'Pct.drive.2hrs', 'Avgdays.week', 'Pct.drive.thr', 'Pct.drive.rush am',\n",
        "    'Pct.drive.rush pm', 'Pct.drive.wed', 'Pct.drive.mon', 'Pct.drive.tue',\n",
        "    'Pct.drive.fri', 'Brake.06miles', 'Pct.drive.sat', 'Pct.drive.sun',\n",
        "    'Car.age', 'Accel.06miles', 'Years.noclaims', 'Left.turn.intensity09'\n",
        "]\n",
        "\n",
        "# Prepare the data\n",
        "X = data[features]\n",
        "y = (data['NB_Claim'] > 0).astype(int)\n",
        "\n",
        "# Split the data\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",
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Convert to PyTorch tensors for TabNet\n",
        "X_train_tensor = torch.FloatTensor(X_train_scaled)\n",
        "y_train_tensor = torch.LongTensor(y_train.values)\n",
        "X_test_tensor = torch.FloatTensor(X_test_scaled)\n",
        "y_test_tensor = torch.LongTensor(y_test.values)\n",
        "\n",
        "# Initialize and train TabNet\n",
        "tabnet_model = TabNetClassifier(\n",
        "    n_d=16, n_a=16, n_steps=5,\n",
        "    gamma=1.5, n_independent=2, n_shared=2,\n",
        "    cat_idxs=[], cat_dims=[],\n",
        "    cat_emb_dim=1, optimizer_fn=torch.optim.Adam,\n",
        "    optimizer_params=dict(lr=2e-2),\n",
        "    mask_type='entmax',\n",
        "    lambda_sparse=1e-3, momentum=0.3, clip_value=2.,\n",
        "    device_name='auto'\n",
        ")\n",
        "\n",
        "# Train TabNet\n",
        "tabnet_model.fit(\n",
        "    X_train_tensor.numpy(), y_train_tensor.numpy(),\n",
        "    eval_set=[(X_test_tensor.numpy(), y_test_tensor.numpy())],\n",
        "    max_epochs=150,\n",
        "    patience=15,\n",
        "    batch_size=1024,\n",
        "    virtual_batch_size=128,\n",
        "    num_workers=0,\n",
        "    drop_last=False\n",
        ")\n",
        "\n",
        "# Get TabNet predictions and probabilities\n",
        "tabnet_pred_proba = tabnet_model.predict_proba(X_test_scaled)\n",
        "tabnet_predictions = np.argmax(tabnet_pred_proba, axis=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8dbJdzKbjRfe",
        "outputId": "4d2d1c06-9472-4090-b96e-cefb3fe7f569"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
            "  warnings.warn(f\"Device used : {self.device}\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 0  | loss: 0.19585 | val_0_auc: 0.75025 |  0:00:05s\n",
            "epoch 1  | loss: 0.16083 | val_0_auc: 0.76468 |  0:00:11s\n",
            "epoch 2  | loss: 0.15923 | val_0_auc: 0.77108 |  0:00:16s\n",
            "epoch 3  | loss: 0.15763 | val_0_auc: 0.77959 |  0:00:22s\n",
            "epoch 4  | loss: 0.15567 | val_0_auc: 0.77155 |  0:00:28s\n",
            "epoch 5  | loss: 0.15464 | val_0_auc: 0.77688 |  0:00:33s\n",
            "epoch 6  | loss: 0.15463 | val_0_auc: 0.77897 |  0:00:39s\n",
            "epoch 7  | loss: 0.15366 | val_0_auc: 0.78024 |  0:00:44s\n",
            "epoch 8  | loss: 0.15367 | val_0_auc: 0.78756 |  0:00:51s\n",
            "epoch 9  | loss: 0.15311 | val_0_auc: 0.78999 |  0:00:56s\n",
            "epoch 10 | loss: 0.15272 | val_0_auc: 0.78549 |  0:01:01s\n",
            "epoch 11 | loss: 0.15265 | val_0_auc: 0.78733 |  0:01:07s\n",
            "epoch 12 | loss: 0.15272 | val_0_auc: 0.78708 |  0:01:13s\n",
            "epoch 13 | loss: 0.15336 | val_0_auc: 0.78652 |  0:01:19s\n",
            "epoch 14 | loss: 0.15188 | val_0_auc: 0.78942 |  0:01:24s\n",
            "epoch 15 | loss: 0.15093 | val_0_auc: 0.79042 |  0:01:30s\n",
            "epoch 16 | loss: 0.1511  | val_0_auc: 0.79315 |  0:01:36s\n",
            "epoch 17 | loss: 0.1527  | val_0_auc: 0.79021 |  0:01:41s\n",
            "epoch 18 | loss: 0.15371 | val_0_auc: 0.77912 |  0:01:47s\n",
            "epoch 19 | loss: 0.15459 | val_0_auc: 0.77935 |  0:01:52s\n",
            "epoch 20 | loss: 0.15338 | val_0_auc: 0.7825  |  0:01:58s\n",
            "epoch 21 | loss: 0.1537  | val_0_auc: 0.7786  |  0:02:04s\n",
            "epoch 22 | loss: 0.15453 | val_0_auc: 0.78647 |  0:02:10s\n",
            "epoch 23 | loss: 0.15287 | val_0_auc: 0.78131 |  0:02:15s\n",
            "epoch 24 | loss: 0.15376 | val_0_auc: 0.77901 |  0:02:20s\n",
            "epoch 25 | loss: 0.15361 | val_0_auc: 0.78332 |  0:02:27s\n",
            "epoch 26 | loss: 0.15396 | val_0_auc: 0.78116 |  0:02:32s\n",
            "epoch 27 | loss: 0.15352 | val_0_auc: 0.7845  |  0:02:38s\n",
            "epoch 28 | loss: 0.15393 | val_0_auc: 0.78015 |  0:02:43s\n",
            "epoch 29 | loss: 0.1534  | val_0_auc: 0.78542 |  0:02:49s\n",
            "epoch 30 | loss: 0.15301 | val_0_auc: 0.78627 |  0:02:54s\n",
            "epoch 31 | loss: 0.15146 | val_0_auc: 0.78992 |  0:03:00s\n",
            "\n",
            "Early stopping occurred at epoch 31 with best_epoch = 16 and best_val_0_auc = 0.79315\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
            "  warnings.warn(wrn_msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade xgboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddyrtn9OoEq2",
        "outputId": "2a8ce141-ba84-4436-eae2-585f61146654"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.1.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.26.4)\n",
            "Requirement already satisfied: nvidia-nccl-cu12 in /usr/local/lib/python3.10/dist-packages (from xgboost) (2.23.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.13.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Train XGBoost\n",
        "xgb_model = xgb.XGBClassifier(\n",
        "    n_estimators=200,\n",
        "    learning_rate=0.1,\n",
        "    max_depth=5,\n",
        "    min_child_weight=1,\n",
        "    gamma=0,\n",
        "    subsample=0.8,\n",
        "    colsample_bytree=0.8,\n",
        "    objective='binary:logistic',\n",
        "    scale_pos_weight=sum(y_train == 0) / sum(y_train == 1),  # Handle class imbalance\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "xgb_model.fit(\n",
        "    X_train_scaled,\n",
        "    y_train,\n",
        "    eval_set=[(X_test_scaled, y_test)],\n",
        "\n",
        "    # eval_metric='auc',\n",
        "    # early_stopping_rounds=10,\n",
        "    verbose=False\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "omFl-d2UmQDR",
        "outputId": "f1f3ad9c-b609-4dc7-96d2-a449d713d4bb"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=0, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=42, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=0, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=0, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Get XGBoost predictions and probabilities\n",
        "xgb_pred_proba = xgb_model.predict_proba(X_test_scaled)[:, 1]\n",
        "xgb_predictions = xgb_model.predict(X_test_scaled)\n",
        "\n",
        "# Combine predictions (weighted average)\n",
        "weight_tabnet = 0.5  # You can adjust these weights\n",
        "weight_xgb = 0.5\n",
        "\n",
        "combined_pred_proba = (weight_tabnet * tabnet_pred_proba[:, 1] +\n",
        "                      weight_xgb * xgb_pred_proba)\n",
        "combined_predictions = (combined_pred_proba >= 0.5).astype(int)\n",
        "\n",
        "def calculate_metrics(y_true, y_pred, y_pred_proba):\n",
        "    \"\"\"Calculate and return all metrics.\"\"\"\n",
        "    return {\n",
        "        'Accuracy': accuracy_score(y_true, y_pred),\n",
        "        'Precision': precision_score(y_true, y_pred),\n",
        "        'Recall': recall_score(y_true, y_pred),\n",
        "        'F1 Score': f1_score(y_true, y_pred),\n",
        "        'AUC-ROC': roc_auc_score(y_true, y_pred_proba),\n",
        "        'Matthews Correlation': matthews_corrcoef(y_true, y_pred)\n",
        "    }\n",
        "\n",
        "# Calculate metrics for all models\n",
        "tabnet_metrics = calculate_metrics(y_test, tabnet_predictions, tabnet_pred_proba[:, 1])\n",
        "xgb_metrics = calculate_metrics(y_test, xgb_predictions, xgb_pred_proba)\n",
        "combined_metrics = calculate_metrics(y_test, combined_predictions, combined_pred_proba)\n",
        "\n",
        "# Print results\n",
        "def print_metrics(name, metrics):\n",
        "    print(f\"\\n{name} Model Metrics:\")\n",
        "    print(\"-\" * 50)\n",
        "    for metric, value in metrics.items():\n",
        "        print(f\"{metric}: {value:.4f}\")\n",
        "\n",
        "print_metrics(\"TabNet\", tabnet_metrics)\n",
        "print_metrics(\"XGBoost\", xgb_metrics)\n",
        "print_metrics(\"Combined\", combined_metrics)\n",
        "\n",
        "# Visualizations\n",
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "# 1. ROC Curves\n",
        "plt.subplot(2, 2, 1)\n",
        "fpr_tab, tpr_tab, _ = roc_curve(y_test, tabnet_pred_proba[:, 1])\n",
        "fpr_xgb, tpr_xgb, _ = roc_curve(y_test, xgb_pred_proba)\n",
        "fpr_comb, tpr_comb, _ = roc_curve(y_test, combined_pred_proba)\n",
        "\n",
        "plt.plot(fpr_tab, tpr_tab, label=f'TabNet (AUC = {tabnet_metrics[\"AUC-ROC\"]:.3f})')\n",
        "plt.plot(fpr_xgb, tpr_xgb, label=f'XGBoost (AUC = {xgb_metrics[\"AUC-ROC\"]:.3f})')\n",
        "plt.plot(fpr_comb, tpr_comb, label=f'Combined (AUC = {combined_metrics[\"AUC-ROC\"]:.3f})')\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curves Comparison')\n",
        "plt.legend()\n",
        "\n",
        "# 2. Feature Importance Comparison\n",
        "plt.subplot(2, 2, 2)\n",
        "feature_imp_tab = tabnet_model.feature_importances_\n",
        "feature_imp_xgb = xgb_model.feature_importances_\n",
        "\n",
        "# Normalize importances\n",
        "feature_imp_tab = feature_imp_tab / feature_imp_tab.sum()\n",
        "feature_imp_xgb = feature_imp_xgb / feature_imp_xgb.sum()\n",
        "\n",
        "importance_df = pd.DataFrame({\n",
        "    'Feature': features,\n",
        "    'TabNet': feature_imp_tab,\n",
        "    'XGBoost': feature_imp_xgb\n",
        "})\n",
        "importance_df = importance_df.sort_values('TabNet', ascending=True)\n",
        "\n",
        "plt.barh(np.arange(len(features)), importance_df['TabNet'], alpha=0.5, label='TabNet')\n",
        "plt.barh(np.arange(len(features)), importance_df['XGBoost'], alpha=0.5, label='XGBoost')\n",
        "plt.yticks(np.arange(len(features)), importance_df['Feature'])\n",
        "plt.xlabel('Normalized Importance')\n",
        "plt.title('Feature Importance Comparison')\n",
        "plt.legend()\n",
        "\n",
        "# 3. Confusion Matrix for Combined Model\n",
        "plt.subplot(2, 2, 3)\n",
        "cm = confusion_matrix(y_test, combined_predictions)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "plt.title('Confusion Matrix (Combined Model)')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "\n",
        "# 4. Metrics Comparison\n",
        "plt.subplot(2, 2, 4)\n",
        "metrics_df = pd.DataFrame({\n",
        "    'TabNet': list(tabnet_metrics.values()),\n",
        "    'XGBoost': list(xgb_metrics.values()),\n",
        "    'Combined': list(combined_metrics.values())\n",
        "}, index=list(tabnet_metrics.keys()))\n",
        "\n",
        "metrics_df.plot(kind='bar', width=0.8)\n",
        "plt.title('Metrics Comparison Across Models')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Print detailed classification reports\n",
        "print(\"\\nTabNet Classification Report:\")\n",
        "print(classification_report(y_test, tabnet_predictions))\n",
        "\n",
        "print(\"\\nXGBoost Classification Report:\")\n",
        "print(classification_report(y_test, xgb_predictions))\n",
        "\n",
        "print(\"\\nCombined Model Classification Report:\")\n",
        "print(classification_report(y_test, combined_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "450JpHcLnhz5",
        "outputId": "12416662-f088-4783-eccd-daedf8f660ef"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TabNet Model Metrics:\n",
            "--------------------------------------------------\n",
            "Accuracy: 0.9572\n",
            "Precision: 0.0000\n",
            "Recall: 0.0000\n",
            "F1 Score: 0.0000\n",
            "AUC-ROC: 0.7932\n",
            "Matthews Correlation: -0.0021\n",
            "\n",
            "XGBoost Model Metrics:\n",
            "--------------------------------------------------\n",
            "Accuracy: 0.8210\n",
            "Precision: 0.1594\n",
            "Recall: 0.7471\n",
            "F1 Score: 0.2628\n",
            "AUC-ROC: 0.8704\n",
            "Matthews Correlation: 0.2887\n",
            "\n",
            "Combined Model Metrics:\n",
            "--------------------------------------------------\n",
            "Accuracy: 0.9536\n",
            "Precision: 0.4094\n",
            "Recall: 0.1932\n",
            "F1 Score: 0.2625\n",
            "AUC-ROC: 0.8664\n",
            "Matthews Correlation: 0.2601\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TabNet Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98     19146\n",
            "           1       0.00      0.00      0.00       854\n",
            "\n",
            "    accuracy                           0.96     20000\n",
            "   macro avg       0.48      0.50      0.49     20000\n",
            "weighted avg       0.92      0.96      0.94     20000\n",
            "\n",
            "\n",
            "XGBoost Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.82      0.90     19146\n",
            "           1       0.16      0.75      0.26       854\n",
            "\n",
            "    accuracy                           0.82     20000\n",
            "   macro avg       0.57      0.79      0.58     20000\n",
            "weighted avg       0.95      0.82      0.87     20000\n",
            "\n",
            "\n",
            "Combined Model Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.98     19146\n",
            "           1       0.41      0.19      0.26       854\n",
            "\n",
            "    accuracy                           0.95     20000\n",
            "   macro avg       0.69      0.59      0.62     20000\n",
            "weighted avg       0.94      0.95      0.95     20000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import (\n",
        "    precision_score, recall_score, f1_score,\n",
        "    roc_auc_score, accuracy_score, matthews_corrcoef\n",
        ")\n",
        "from pytorch_tabnet.tab_model import TabNetClassifier\n",
        "import xgboost as xgb\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Load and prepare data\n",
        "features = [\n",
        "    'Total.miles.driven', 'Annual.pct.driven', 'Duration', 'Credit.score',\n",
        "    'Pct.drive.2hrs', 'Avgdays.week', 'Pct.drive.thr', 'Pct.drive.rush am',\n",
        "    'Pct.drive.rush pm', 'Pct.drive.wed', 'Pct.drive.mon', 'Pct.drive.tue',\n",
        "    'Pct.drive.fri', 'Brake.06miles', 'Pct.drive.sat', 'Pct.drive.sun',\n",
        "    'Car.age', 'Accel.06miles', 'Years.noclaims', 'Left.turn.intensity09'\n",
        "]\n",
        "\n",
        "X = data[features]\n",
        "y = (data['NB_Claim'] > 0).astype(int)\n",
        "\n",
        "# Split the data\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",
        "# Scale features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Apply SMOTE to handle class imbalance\n",
        "smote = SMOTE(random_state=42)\n",
        "X_train_balanced, y_train_balanced = smote.fit_resample(X_train_scaled, y_train)\n",
        "\n",
        "# Convert to PyTorch tensors for TabNet\n",
        "X_train_tensor = torch.FloatTensor(X_train_balanced)\n",
        "y_train_tensor = torch.LongTensor(y_train_balanced)\n",
        "X_test_tensor = torch.FloatTensor(X_test_scaled)\n",
        "\n",
        "# Initialize TabNet with adjusted parameters\n",
        "tabnet_model = TabNetClassifier(\n",
        "    n_d=32,  # Increased complexity\n",
        "    n_a=32,\n",
        "    n_steps=5,\n",
        "    gamma=1.5,\n",
        "    n_independent=2,\n",
        "    n_shared=2,\n",
        "    cat_idxs=[],\n",
        "    cat_dims=[],\n",
        "    cat_emb_dim=1,\n",
        "    optimizer_fn=torch.optim.Adam,\n",
        "    optimizer_params=dict(lr=1e-3),  # Reduced learning rate\n",
        "    mask_type='entmax',\n",
        "    lambda_sparse=1e-3,\n",
        "    momentum=0.3,\n",
        "    clip_value=2.,\n",
        "    device_name='auto'\n",
        ")\n",
        "\n",
        "# Train TabNet with adjusted parameters\n",
        "tabnet_model.fit(\n",
        "    X_train_tensor.numpy(), y_train_tensor.numpy(),\n",
        "    eval_set=[(X_test_scaled, y_test)],\n",
        "    max_epochs=20,  # Increased epochs\n",
        "    patience=20,\n",
        "    batch_size=512,\n",
        "    virtual_batch_size=64,\n",
        "    num_workers=0,\n",
        "    drop_last=False,\n",
        "    eval_metric=['auc']  # Monitor AUC during training\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pygBQ48MpjmM",
        "outputId": "e422bf65-ce8a-4503-ad82-9d0473976d12"
      },
      "execution_count": 37,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cuda\n",
            "  warnings.warn(f\"Device used : {self.device}\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 0  | loss: 0.70818 | val_0_auc: 0.72604 |  0:00:18s\n",
            "epoch 1  | loss: 0.5874  | val_0_auc: 0.75117 |  0:00:36s\n",
            "epoch 2  | loss: 0.56355 | val_0_auc: 0.76736 |  0:00:56s\n",
            "epoch 3  | loss: 0.55209 | val_0_auc: 0.76908 |  0:01:14s\n",
            "epoch 4  | loss: 0.54381 | val_0_auc: 0.76542 |  0:01:33s\n",
            "epoch 5  | loss: 0.53735 | val_0_auc: 0.77445 |  0:01:51s\n",
            "epoch 6  | loss: 0.52976 | val_0_auc: 0.77658 |  0:02:09s\n",
            "epoch 7  | loss: 0.52658 | val_0_auc: 0.77977 |  0:02:29s\n",
            "epoch 8  | loss: 0.52175 | val_0_auc: 0.78384 |  0:02:47s\n",
            "epoch 9  | loss: 0.51775 | val_0_auc: 0.78082 |  0:03:05s\n",
            "epoch 10 | loss: 0.51373 | val_0_auc: 0.78971 |  0:03:24s\n",
            "epoch 11 | loss: 0.50882 | val_0_auc: 0.78623 |  0:03:42s\n",
            "epoch 12 | loss: 0.50568 | val_0_auc: 0.79121 |  0:04:01s\n",
            "epoch 13 | loss: 0.50401 | val_0_auc: 0.79047 |  0:04:19s\n",
            "epoch 14 | loss: 0.49923 | val_0_auc: 0.79029 |  0:04:37s\n",
            "epoch 15 | loss: 0.49469 | val_0_auc: 0.79213 |  0:04:57s\n",
            "epoch 16 | loss: 0.49232 | val_0_auc: 0.78996 |  0:05:16s\n",
            "epoch 17 | loss: 0.49012 | val_0_auc: 0.78937 |  0:05:35s\n",
            "epoch 18 | loss: 0.48676 | val_0_auc: 0.79743 |  0:05:53s\n",
            "epoch 19 | loss: 0.48317 | val_0_auc: 0.79536 |  0:06:11s\n",
            "Stop training because you reached max_epochs = 20 with best_epoch = 18 and best_val_0_auc = 0.79743\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
            "  warnings.warn(wrn_msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize XGBoost with adjusted parameters\n",
        "xgb_model = xgb.XGBClassifier(\n",
        "    n_estimators=300,\n",
        "    learning_rate=0.05,\n",
        "    max_depth=6,\n",
        "    min_child_weight=1,\n",
        "    gamma=0.1,\n",
        "    subsample=0.8,\n",
        "    colsample_bytree=0.8,\n",
        "    objective='binary:logistic',\n",
        "    scale_pos_weight=1,  # No need for explicit weight as we used SMOTE\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Train XGBoost\n",
        "xgb_model.fit(\n",
        "    X_train_balanced,\n",
        "    y_train_balanced,\n",
        "    eval_set=[(X_test_scaled, y_test)],\n",
        "    # eval_metric='auc',\n",
        "    # early_stopping_rounds=20,\n",
        "    verbose=False\n",
        ")\n",
        "\n",
        "# Get predictions from both models\n",
        "tabnet_pred_proba = tabnet_model.predict_proba(X_test_scaled)\n",
        "xgb_pred_proba = xgb_model.predict_proba(X_test_scaled)\n",
        "\n",
        "# Dynamic weight calculation based on validation performance\n",
        "tabnet_val_auc = roc_auc_score(y_test, tabnet_pred_proba[:, 1])\n",
        "xgb_val_auc = roc_auc_score(y_test, xgb_pred_proba[:, 1])\n",
        "\n",
        "# Calculate weights based on relative performance\n",
        "total_auc = tabnet_val_auc + xgb_val_auc\n",
        "weight_tabnet = tabnet_val_auc / total_auc\n",
        "weight_xgb = xgb_val_auc / total_auc\n",
        "\n",
        "# Combine predictions with dynamic weights\n",
        "combined_pred_proba = (weight_tabnet * tabnet_pred_proba[:, 1] +\n",
        "                      weight_xgb * xgb_pred_proba[:, 1])\n",
        "\n",
        "# Find optimal threshold using validation set\n",
        "thresholds = np.arange(0.1, 0.9, 0.05)\n",
        "best_f1 = 0\n",
        "best_threshold = 0.5\n",
        "\n",
        "for threshold in thresholds:\n",
        "    combined_pred = (combined_pred_proba >= threshold).astype(int)\n",
        "    f1 = f1_score(y_test, combined_pred)\n",
        "    if f1 > best_f1:\n",
        "        best_f1 = f1\n",
        "        best_threshold = threshold\n",
        "\n",
        "# Make final predictions using best threshold\n",
        "combined_predictions = (combined_pred_proba >= best_threshold).astype(int)\n",
        "tabnet_predictions = (tabnet_pred_proba[:, 1] >= best_threshold).astype(int)\n",
        "xgb_predictions = (xgb_pred_proba[:, 1] >= best_threshold).astype(int)\n",
        "\n",
        "# Calculate and print metrics for all models\n",
        "def print_all_metrics(y_true, y_pred, y_pred_proba, model_name):\n",
        "    print(f\"\\n{model_name} Metrics:\")\n",
        "    print(\"-\" * 50)\n",
        "    print(f\"Accuracy: {accuracy_score(y_true, y_pred):.4f}\")\n",
        "    print(f\"Precision: {precision_score(y_true, y_pred):.4f}\")\n",
        "    print(f\"Recall: {recall_score(y_true, y_pred):.4f}\")\n",
        "    print(f\"F1 Score: {f1_score(y_true, y_pred):.4f}\")\n",
        "    print(f\"AUC-ROC: {roc_auc_score(y_true, y_pred_proba):.4f}\")\n",
        "    print(f\"Matthews Correlation: {matthews_corrcoef(y_true, y_pred):.4f}\")\n",
        "\n",
        "print_all_metrics(y_test, tabnet_predictions, tabnet_pred_proba[:, 1], \"TabNet\")\n",
        "print_all_metrics(y_test, xgb_predictions, xgb_pred_proba[:, 1], \"XGBoost\")\n",
        "print_all_metrics(y_test, combined_predictions, combined_pred_proba, \"Combined\")\n",
        "\n",
        "print(f\"\\nBest threshold: {best_threshold:.3f}\")\n",
        "print(f\"Model weights - TabNet: {weight_tabnet:.3f}, XGBoost: {weight_xgb:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5kRkLwApq3d",
        "outputId": "61d736e5-935d-4a67-fff3-7cf527c8c4ac"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TabNet Metrics:\n",
            "--------------------------------------------------\n",
            "Accuracy: 0.8040\n",
            "Precision: 0.1281\n",
            "Recall: 0.6183\n",
            "F1 Score: 0.2122\n",
            "AUC-ROC: 0.7974\n",
            "Matthews Correlation: 0.2152\n",
            "\n",
            "XGBoost Metrics:\n",
            "--------------------------------------------------\n",
            "Accuracy: 0.9571\n",
            "Precision: 0.4949\n",
            "Recall: 0.1698\n",
            "F1 Score: 0.2528\n",
            "AUC-ROC: 0.8526\n",
            "Matthews Correlation: 0.2727\n",
            "\n",
            "Combined Metrics:\n",
            "--------------------------------------------------\n",
            "Accuracy: 0.9395\n",
            "Precision: 0.2969\n",
            "Recall: 0.3056\n",
            "F1 Score: 0.3012\n",
            "AUC-ROC: 0.8398\n",
            "Matthews Correlation: 0.2696\n",
            "\n",
            "Best threshold: 0.650\n",
            "Model weights - TabNet: 0.483, XGBoost: 0.517\n"
          ]
        }
      ]
    }
  ]
}