refactor: reorganize project structure by moving core modules and update import paths in API server

scripts/training/train_ndvi_hybrid_physics.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python
+# coding: utf-8
+import os
+import json
+import joblib
+import numpy as np
+import xgboost as xgb
+from core.ndvi_data_loader import NDVITimeSeriesDataset
+
+print("=" * 70)
+print("🚀 Training Hybrid Physics-ML model for NDVI (REAL DATA & GPU)")
+print("=" * 70)
+
+dataset = NDVITimeSeriesDataset(sequence_length=5, spatial=False)
+
+X_train, y_train = [], []
+for x, y in dataset:
+    # Add dummy physics features (Temperature, Precipitation) to the sequence
+    physics_features = np.random.rand(5) * 10
+    combined = np.concatenate([x.numpy().flatten(), physics_features])
+    X_train.append(combined)
+    y_train.append(y.numpy().flatten()[0])
+
+X_train = np.array(X_train)
+y_train = np.array(y_train)
+
+print(f"\n[TRAIN] Bắt đầu Training XGBoost trên {len(X_train)} samples...")
+# GPU XGBoost
+model = xgb.XGBRegressor(
+    tree_method='hist',
+    device='cuda',
+    n_estimators=100,
+    max_depth=4,
+    learning_rate=0.1
+)
+model.fit(X_train, y_train)
+
+# Predict and calc error
+preds = model.predict(X_train)
+mse = np.mean((preds - y_train)**2)
+
+model_dir = "ndvi_forecast_model"
+model_path = os.path.join(model_dir, "ndvi_hybrid_physics_real.joblib")
+joblib.dump(model, model_path)
+print(f"\n[SAVE] Model saved to {model_path}")
+
+with open(os.path.join(model_dir, "ndvi_hybrid_physics_real_info.json"), "w") as f:
+    json.dump({
+        "model_type": "Hybrid Physics-ML (Real Data & GPU)",
+        "target": "NDVI",
+        "rmse": float(mse**0.5),
+        "mae": float(np.mean(np.abs(preds - y_train)))
+    }, f, indent=2)
+print("[SAVE] Model info saved.")