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

2026-07-18 01:24:30 +07:00
parent abab846884
commit a82b2f6fa5
155 changed files with 25 additions and 370 deletions
@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+# coding: utf-8
+import os
+import json
+import joblib
+import numpy as np
+from sklearn.ensemble import VotingRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from core.ndvi_data_loader import NDVITimeSeriesDataset
+
+print("=" * 70)
+print("🚀 Training Multi-Model Ensemble for NDVI (REAL DATA & CPU)")
+print("=" * 70)
+
+dataset = NDVITimeSeriesDataset(sequence_length=5, spatial=False)
+
+X_train, y_train = [], []
+for x, y in dataset:
+    X_train.append(x.numpy().flatten())
+    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 Ensemble trên {len(X_train)} samples...")
+# CPU Ensemble
+model1 = LinearRegression()
+model2 = RandomForestRegressor(n_estimators=50, random_state=42)
+ensemble = VotingRegressor([('lr', model1), ('rf', model2)])
+
+ensemble.fit(X_train, y_train)
+
+# Predict and calc error
+preds = ensemble.predict(X_train)
+mse = np.mean((preds - y_train)**2)
+
+model_dir = "ndvi_forecast_model"
+model_path = os.path.join(model_dir, "ndvi_ensemble_real.joblib")
+joblib.dump(ensemble, model_path)
+print(f"\n[SAVE] Model saved to {model_path}")
+
+with open(os.path.join(model_dir, "ndvi_ensemble_real_info.json"), "w") as f:
+    json.dump({
+        "model_type": "Multi-Model Ensemble (Real Data & CPU)",
+        "target": "NDVI",
+        "rmse": float(mse**0.5),
+        "mae": float(np.mean(np.abs(preds - y_train)))
+    }, f, indent=2)
+print("[SAVE] Model info saved.")