sine_wave_example.py

#!/usr/bin/env python3
"""
KortexDL Boston Housing Regression
===================================

Regression on the Boston Housing dataset.

Usage:
    python sine_wave_example.py
"""

import numpy as np
import kortexdl as bd

try:
    from sklearn.datasets import fetch_california_housing
    from sklearn.preprocessing import StandardScaler
    from sklearn.model_selection import train_test_split
    SKLEARN_AVAILABLE = True
except ImportError:
    SKLEARN_AVAILABLE = False


def main():
    print("🎯 KortexDL Regression - Diabetes Dataset")
    print("=" * 60)
    
    if not SKLEARN_AVAILABLE:
        print("❌ sklearn is required. Install with: pip install scikit-learn")
        return 1
    
    # Load Diabetes dataset
    print("\n📁 Loading Diabetes dataset...")
    from sklearn.datasets import load_diabetes
    data = load_diabetes()
    X = data.data.astype(np.float32)
    y = data.target.reshape(-1, 1).astype(np.float32)
    
    print(f"✅ Dataset: {len(X)} samples")
    print(f"✅ Features: {X.shape[1]} (age, sex, bmi, blood pressure, etc.)")
    print(f"✅ Target: Disease progression measure")
    
    # Normalize features and target
    scaler_X = StandardScaler()
    scaler_y = StandardScaler()
    X = scaler_X.fit_transform(X).astype(np.float32)
    y = scaler_y.fit_transform(y).astype(np.float32)
    
    # Split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    print(f"✅ Split: {len(X_train)} train, {len(X_test)} test")
    
    # Create network
    print("\n🧠 Creating regression network...")
    n_features = X.shape[1]
    net = bd.Network([n_features, 64, 32, 1], bd.ActivationType.ReLU)
    print(f"✅ Network: {n_features} -> 64 -> 32 -> 1")
    
    # Training
    print("\n🏋️ Training...")
    epochs = 200
    X_flat = X_train.flatten().tolist()
    y_flat = y_train.flatten().tolist()
    
    for epoch in range(epochs):
        loss = net.train_batch(X_flat, y_flat, bd.LossType.MSE, 0.01, len(X_train))
        if epoch % 50 == 0:
            print(f"  Epoch {epoch:3d}: Loss = {loss:.4f}")
    
    print("✅ Training complete!")
    
    # Evaluation
    print("\n📈 Evaluating...")
    predictions = [net.forward(X_test[i].tolist(), 1, False)[0] for i in range(len(X_test))]
    predictions = np.array(predictions)
    
    # Inverse transform for interpretable metrics
    y_test_orig = scaler_y.inverse_transform(y_test)
    pred_orig = scaler_y.inverse_transform(predictions.reshape(-1, 1))
    
    mse = bd.compute_mse(y_test.flatten().tolist(), predictions.tolist())
    r2 = bd.compute_r2_score(y_test.flatten().tolist(), predictions.tolist())
    
    print(f"✅ Test MSE (normalized): {mse:.4f}")
    print(f"✅ Test R²: {r2:.4f}")
    
    # Sample predictions
    print("\n🔍 Sample Predictions:")
    for i in range(5):
        true_val = y_test_orig[i][0]
        pred_val = pred_orig[i][0]
        print(f"  True: {true_val:6.1f}  Pred: {pred_val:6.1f}")
    
    print("\n" + "=" * 60)
    print("✅ Complete!")
    return 0


if __name__ == "__main__":
    exit(main())