# main.py

# A demonstration of advanced model evaluation for a classification algorithm.

#

# Before running, you may need to install scikit-learn, pandas, seaborn, and matplotlib:

# pip install scikit-learn pandas seaborn matplotlib

import os

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.naive_bayes import GaussianNB

from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

print("--- Starting Advanced Model Evaluation Demonstration ---")

# --- Section 1: Load and Prepare the Dataset ---

# We will use the Iris dataset.

print("\n--- 1. Loading the Iris Dataset ---")

iris = load_iris()

X = iris.data  # The features

y = iris.target # The target classes

# For clarity, let's see the feature and target names

print(f"Features: {iris.feature_names}")

print(f"Target Classes: {iris.target_names}")

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(

    X, y, test_size=0.3, random_state=42

)

print(f"\nData split into {len(X_train)} training samples and {len(X_test)} testing samples.")

# --- Section 2: Train the Gaussian Naive Bayes Model ---

print("\n--- 2. Training the Gaussian Naive Bayes Model ---")

gnb = GaussianNB()

gnb.fit(X_train, y_train)

print("Model training complete.")

# --- Section 3: Advanced Model Evaluation ---

# Now we use the trained model to make predictions and evaluate its performance

# using more detailed metrics than just accuracy.

print("\n--- 3. Evaluating the Model ---")

y_pred = gnb.predict(X_test)

# a) Accuracy Score

accuracy = accuracy_score(y_test, y_pred)

print(f"\nModel Accuracy: {accuracy:.4f} (A good starting point, but can be misleading)")

# b) Classification Report

# This report is crucial, especially for imbalanced datasets, as it shows

# precision, recall, and F1-score for each individual class.

print("\nClassification Report:")

print(classification_report(y_test, y_pred, target_names=iris.target_names))

# c) Confusion Matrix

# A confusion matrix gives a detailed breakdown of correct and incorrect predictions

# for each class.

print("\nConfusion Matrix:")

cm = confusion_matrix(y_test, y_pred)

print(cm)

# d) Visualizing the Confusion Matrix

# A heatmap makes the confusion matrix much easier to interpret.

print("\nVisualizing the Confusion Matrix as a Heatmap...")

try:

    plt.figure(figsize=(8, 6))

    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',

                xticklabels=iris.target_names,

                yticklabels=iris.target_names)

    plt.title('Confusion Matrix')

    plt.ylabel('Actual Label')

    plt.xlabel('Predicted Label')

    # Save the plot to a file

    plot_filename = 'confusion_matrix.png'

    plt.savefig(plot_filename)

    print(f"Confusion matrix plot saved as '{plot_filename}'")

    plt.show()

    plt.close()

except Exception as e:

    print(f"An error occurred during visualization: {e}")

# --- Clean up the created image file ---

finally:

    if 'plot_filename' in locals() and os.path.exists(plot_filename):

        os.remove(plot_filename)

        print(f"\n--- Clean up: Removed '{plot_filename}' ---")

print("\n--- End of Demonstration ---")