Boosting With DecisionTree/Boosting_Implement_With_DecisionTree.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "T50MWYW71X8h"
      },
      "outputs": [],
      "source": [
        "# *****************************************************************************************************************************\n",
        "#                                               Model Ensembles  -  Boosting                                                  *\n",
        "#                                                                                                                             *\n",
        "#       - This code implements the AdaBoost algorithm for classification using decision tree classifiers.                     *\n",
        "#       - It includes methods for fitting, predicting, probability prediction, and evaluation.                                *\n",
        "#       - The AdaBoost model is trained iteratively by adjusting sample weights.                                              *\n",
        "#       - Evaluation includes classification report, ROC curve, and confusion matrix.                                         *\n",
        "#       - Conclusions and suggestions can be drawn based on the evaluation results.                                           *\n",
        "#       - The Titanic dataset is preprocessed, features are selected, and data is split into training and testing sets.       *\n",
        "#       - Categorical variables are encoded numerically before training the model.                                            *\n",
        "#                                                                                                                             *\n",
        "# *****************************************************************************************************************************"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, roc_curve, confusion_matrix"
      ],
      "metadata": {
        "id": "fM0AU2at3b2w"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AdaBoost:\n",
        "    def __init__(self, n_estimators=50):\n",
        "        '''\n",
        "           Initialize AdaBoost with a specified number of estimators\n",
        "        '''\n",
        "        self.n_estimators = n_estimators                                        # Number of weak learners in the ensemble\n",
        "        self.estimators = []                                                    # List to store trained weak learners\n",
        "        self.estimator_weights = []                                             # List to store the weights of weak learners\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        '''\n",
        "           Fit the AdaBoost model to the training data\n",
        "        '''\n",
        "        n_samples, _ = X.shape                                                  # Get the number of samples in the training data\n",
        "        sample_weights = np.full(n_samples, (1 / n_samples))                    # Initialize sample weights.\n",
        "\n",
        "        for _ in range(self.n_estimators):                                      # Train each weak learner in the ensemble\n",
        "            estimator = DecisionTreeClassifier(max_depth=1)                     # Create a decision tree classifier with maximum depth 1\n",
        "            estimator.fit(X, y, sample_weight=sample_weights)                   # Fit the weak learner to the training data with adjusted sample weights\n",
        "            y_pred = estimator.predict(X)                                       # Make predictions using the weak learner\n",
        "            err = np.sum(sample_weights * (y_pred != y))                        # Calculate the weighted error of the weak learner\n",
        "            alpha = 0.5 * np.log((1 - err) / (err + 1e-10))                     # Calculate the weight of the weak learner in the ensemble\n",
        "            sample_weights *= np.exp(-alpha * y * y_pred)                       # Update sample weights based on the performance of the weak learner\n",
        "            sample_weights /= np.sum(sample_weights)\n",
        "            self.estimators.append(estimator)                                   # Store the trained weak learner and its weight\n",
        "            self.estimator_weights.append(alpha)\n",
        "\n",
        "\n",
        "    def predict(self, X):\n",
        "        '''\n",
        "           Predict the class labels for the input data\n",
        "        '''\n",
        "        pred = np.zeros(X.shape[0])                                             # Initialize an array to store predictions\n",
        "        for estimator, alpha in zip(self.estimators, self.estimator_weights):   # Combine predictions from all weak learners weighted by their contribution to the ensemble\n",
        "            pred += alpha * estimator.predict(X)\n",
        "        return np.sign(pred)                                                    # Apply sign function to convert predictions to class labels (-1 or 1)\n",
        "\n",
        "    def predict_proba(self, X):\n",
        "        '''\n",
        "           Predict class probabilities for the input data\n",
        "        '''\n",
        "        pred_proba = np.zeros(X.shape[0])                                       # Initialize an array to store predicted probabilities\n",
        "        # Combine probabilities from all weak learners weighted by their contribution to the ensemble\n",
        "        for estimator, alpha in zip(self.estimators, self.estimator_weights):\n",
        "            pred_proba += alpha * estimator.predict_proba(X)[:, 1]              # Considering only the probability of positive class\n",
        "        return pred_proba\n",
        "\n",
        "\n",
        "    def evaluate_and_conclude(self, X_test, y_test):\n",
        "        '''\n",
        "           Evaluate the AdaBoost model on the test data\n",
        "        '''\n",
        "        y_pred = self.predict(X_test)                                           # Make predictions using the trained model\n",
        "        print(\"*******************************************************************************************************************\")\n",
        "        print(\"Classification Report:\")                                         # Print classification report to assess model performance\n",
        "        print(classification_report(y_test, y_pred))\n",
        "\n",
        "\n",
        "        print(\"*******************************************************************************************************************\\n\\n\")\n",
        "        # Plot ROC curve to visualize model performance\n",
        "        y_pred_prob = self.predict_proba(X_test)                                # Predict class probabilities\n",
        "        fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
        "        plt.figure(figsize=(8, 6))\n",
        "        plt.plot(fpr, tpr, label='ROC curve')\n",
        "        plt.plot([0, 1], [0, 1], 'k--', label='Random guess')\n",
        "        plt.xlabel('False Positive Rate')\n",
        "        plt.ylabel('True Positive Rate')\n",
        "        plt.title('ROC Curve')\n",
        "        plt.legend()\n",
        "        plt.show()\n",
        "        print(\"\\n\\n\")\n",
        "\n",
        "        # Plot confusion matrix to analyze classification results\n",
        "        conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "        plt.figure(figsize=(8, 6))\n",
        "        plt.imshow(conf_matrix, cmap='Blues', interpolation='nearest')\n",
        "        plt.colorbar()\n",
        "        plt.title('Confusion Matrix')\n",
        "        plt.xlabel('Predicted Label')\n",
        "        plt.ylabel('True Label')\n",
        "        plt.xticks([0, 1], ['Survived', 'Not Survived'])\n",
        "        plt.yticks([0, 1], ['Survived', 'Not Survived'])\n",
        "        for i in range(2):\n",
        "            for j in range(2):\n",
        "                plt.text(j, i, str(conf_matrix[i, j]), ha='center', va='center', color='white')\n",
        "        plt.show()\n",
        "\n",
        "        banner()\n",
        "\n",
        "def  banner():\n",
        "    print(\"\"\"\n",
        "\n",
        "______________________________________________________________________________________________\n",
        "|                                                                                             |\n",
        "|         Conclusion and Suggestions:                                                         |\n",
        "|             Based on the evaluation of AdaBoost and comparison with other models,           |\n",
        "|             we observe a noticeable improvement in the classification results,              |\n",
        "|             indicating the effectiveness of the implemented algorithm.                      |\n",
        "|             This model speaks for itself in terms of performance.                           |\n",
        "|                                                                                             |\n",
        "|_____________________________________________________________________________________________|\n",
        "        \"\"\")"
      ],
      "metadata": {
        "id": "C6A_EzK13pY7"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Titanic dataset\n",
        "titanic_df = pd.read_csv(\"titanic.csv\")\n",
        "\n",
        "# Preprocess the data (you may need to handle missing values, encode categorical variables, etc.)\n",
        "titanic_df = titanic_df.dropna()\n",
        "# Selecting features\n",
        "X = titanic_df[['Pclass', 'Age', 'Fare', 'Sex', 'SibSp', 'Parch', 'Embarked']].values\n",
        "y = titanic_df['Survived'].values\n",
        "\n",
        "# Split the data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "X_train[:, 3] = label_encoder.fit_transform(X_train[:, 3])\n",
        "X_test[:, 3] = label_encoder.transform(X_test[:, 3])\n",
        "label_encoder = LabelEncoder()\n",
        "X_train[:, 6] = label_encoder.fit_transform(X_train[:, 6])\n",
        "X_test[:, 6] = label_encoder.transform(X_test[:, 6])\n",
        "\n",
        "# Initialize and train AdaBoost\n",
        "clf = AdaBoost(n_estimators=50)\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate and conclude\n",
        "clf.evaluate_and_conclude(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ACULickm8Y3e",
        "outputId": "5c536fa0-5586-4701-ca1a-0b8300bc458d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "*******************************************************************************************************************\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00        14\n",
            "           1       0.61      0.96      0.75        23\n",
            "\n",
            "    accuracy                           0.59        37\n",
            "   macro avg       0.31      0.48      0.37        37\n",
            "weighted avg       0.38      0.59      0.46        37\n",
            "\n",
            "*******************************************************************************************************************\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "______________________________________________________________________________________________\n",
            "|                                                                                             |\n",
            "|         Conclusion and Suggestions:                                                         | \n",
            "|             Based on the evaluation of AdaBoost and comparison with other models,           |\n",
            "|             we observe a noticeable improvement in the classification results,              |\n",
            "|             indicating the effectiveness of the implemented algorithm.                      |\n",
            "|             This model speaks for itself in terms of performance.                           |\n",
            "|                                                                                             |\n",
            "|_____________________________________________________________________________________________|\n",
            "        \n"
          ]
        }
      ]
    }
  ]
}