examples.py

"""
Example: Using the Linear Regression Model
===========================================

This script demonstrates how to use individual components of the pipeline.
"""

import numpy as np
from src.linear_regression import LinearRegression
from src.data_ingestion import fetch_data
from src.data_preprocessing import preprocess_data
from src.model_training import train_model
from src.model_evaluation import evaluate_model
from src.prediction import predict_single

# Suppress warnings
import warnings
warnings.filterwarnings('ignore')

def example_basic_usage():
    """Example: Basic usage of LinearRegression class"""
    print("\n" + "="*80)
    print("EXAMPLE 1: Basic Linear Regression Usage")
    print("="*80 + "\n")
    
    # Create simple dataset
    X = np.array([[1], [2], [3], [4], [5]])
    y = np.array([2, 4, 6, 8, 10])
    
    # Create and train model
    model = LinearRegression(learning_rate=0.1, n_iterations=1000)
    model.fit(X, y)
    
    # Make predictions
    predictions = model.predict(X)
    
    print("Training Data:")
    for i in range(len(X)):
        print(f"  X={X[i][0]}, y={y[i]}, predicted={predictions[i]:.2f}")
    
    print(f"\nModel Parameters:")
    print(f"  Weight: {model.weights[0]:.4f}")
    print(f"  Bias: {model.bias:.4f}")


def example_full_pipeline():
    """Example: Using the full pipeline"""
    print("\n" + "="*80)
    print("EXAMPLE 2: Full Pipeline with Boston Housing Data")
    print("="*80 + "\n")
    
    # Load data
    print("1. Loading data...")
    data = fetch_data()
    print(f"   Loaded {len(data)} samples\n")
    
    # Preprocess
    print("2. Preprocessing data...")
    X_train, X_test, y_train, y_test, scaler = preprocess_data(data, test_size=0.2)
    print(f"   Training: {len(X_train)}, Testing: {len(X_test)}\n")
    
    # Train
    print("3. Training model...")
    model = train_model(X_train, y_train, learning_rate=0.01, n_iterations=1000)
    
    # Evaluate
    print("\n4. Evaluating model...")
    train_metrics, test_metrics, _, _ = evaluate_model(
        model, X_train, y_train, X_test, y_test
    )
    
    # Single prediction
    print("\n5. Making single prediction...")
    sample_features = X_test[0]
    prediction = predict_single(model, scaler, scaler.inverse_transform([sample_features])[0])
    actual = y_test.iloc[0]
    print(f"   Predicted: {prediction:.2f}")
    print(f"   Actual: {actual:.2f}")


def example_hyperparameter_tuning():
    """Example: Testing different hyperparameters"""
    print("\n" + "="*80)
    print("EXAMPLE 3: Comparing Different Learning Rates")
    print("="*80 + "\n")
    
    # Load and preprocess data
    data = fetch_data()
    X_train, X_test, y_train, y_test, scaler = preprocess_data(data, test_size=0.2)
    
    learning_rates = [0.001, 0.01, 0.1]
    
    print("Testing different learning rates:\n")
    
    for lr in learning_rates:
        model = LinearRegression(learning_rate=lr, n_iterations=1000)
        model.fit(X_train, y_train)
        
        # Test predictions
        y_pred = model.predict(X_test)
        mse = np.mean((y_test - y_pred) ** 2)
        
        print(f"Learning Rate: {lr}")
        print(f"  Final Cost: {model.cost_history[-1]:.4f}")
        print(f"  Test MSE: {mse:.4f}\n")


if __name__ == "__main__":
    # Run examples
    example_basic_usage()
    example_full_pipeline()
    example_hyperparameter_tuning()
    
    print("\n" + "="*80)
    print("All examples completed!")
    print("="*80 + "\n")