ModelVisualizer.ipynb

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  "metadata": {
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      "authorship_tag": "ABX9TyOOdQ1INOpHN+I2Wb68KzZt",
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Saeidhoseinipour/100Data/blob/main/ModelVisualizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8QWnyaG3Xg32"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import roc_curve, auc, plot_confusion_matrix\n",
        "\n",
        "class ModelVisualizer:\n",
        "    def __init__(self, model, X_test, y_test):\n",
        "        \"\"\"\n",
        "        Initialize the class with a trained model and test data.\n",
        "        :param model: Trained machine learning model.\n",
        "        :param X_test: Test features.\n",
        "        :param y_test: Test target.\n",
        "        \"\"\"\n",
        "        self.model = model\n",
        "        self.X_test = X_test\n",
        "        self.y_test = y_test\n",
        "\n",
        "    def plot_confusion_matrix(self):\n",
        "        \"\"\"Plot the confusion matrix for classification models.\"\"\"\n",
        "        plot_confusion_matrix(self.model, self.X_test, self.y_test, cmap=plt.cm.Blues)\n",
        "        plt.show()\n",
        "\n",
        "    def plot_roc_curve(self):\n",
        "        \"\"\"Plot the ROC curve for classification models.\"\"\"\n",
        "        y_pred_proba = self.model.predict_proba(self.X_test)[:, 1]\n",
        "        fpr, tpr, _ = roc_curve(self.y_test, y_pred_proba)\n",
        "        roc_auc = auc(fpr, tpr)\n",
        "\n",
        "        plt.figure()\n",
        "        plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
        "        plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "        plt.xlabel('False Positive Rate')\n",
        "        plt.ylabel('True Positive Rate')\n",
        "        plt.title('ROC Curve')\n",
        "        plt.legend(loc=\"lower right\")\n",
        "        plt.show()\n",
        "\n",
        "    def plot_residuals(self):\n",
        "        \"\"\"Plot residuals for regression models.\"\"\"\n",
        "        y_pred = self.model.predict(self.X_test)\n",
        "        residuals = self.y_test - y_pred\n",
        "\n",
        "        plt.figure()\n",
        "        plt.scatter(y_pred, residuals)\n",
        "        plt.axhline(y=0, color='r', linestyle='--')\n",
        "        plt.xlabel('Predicted Values')\n",
        "        plt.ylabel('Residuals')\n",
        "        plt.title('Residual Plot')\n",
        "        plt.show()\n",
        "\n",
        "    def plot_clusters(self, transformed_data, labels):\n",
        "        \"\"\"Plot clusters for unsupervised models.\"\"\"\n",
        "        plt.figure()\n",
        "        plt.scatter(transformed_data[:, 0], transformed_data[:, 1], c=labels, cmap='viridis')\n",
        "        plt.xlabel('Component 1')\n",
        "        plt.ylabel('Component 2')\n",
        "        plt.title('Cluster Visualization')\n",
        "        plt.show()"
      ]
    }
  ]
}