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 +1,89 @@
+import os
+from train_module import train_model
+from pathlib import Path
+
+def train_all_models():
+    # Ensure output directory exists
+    output_dir = Path("land_classification_model")
+    output_dir.mkdir(exist_ok=True)
+    
+    # Define models and their optimized hyperparameters
+    models_config = [
+        {'type': 'xgboost', 'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.05, 'use_gpu': True},
+        {'type': 'lightgbm', 'n_estimators': 300, 'max_depth': -1, 'learning_rate': 0.05, 'use_gpu': True},
+        {'type': 'random_forest', 'n_estimators': 100, 'max_depth': 15, 'use_gpu': False},
+        {'type': 'decision_tree', 'max_depth': 12, 'use_gpu': False},
+        {'type': 'svm', 'use_gpu': False},
+        {'type': 'cnn', 'n_estimators': 30, 'use_gpu': True},  # n_estimators acts as epochs
+        {'type': 'swin-unet', 'n_estimators': 80, 'learning_rate': 0.0003, 'use_gpu': True},
+        {'type': 'mobilenet-lraspp', 'n_estimators': 50, 'learning_rate': 0.0008, 'use_gpu': True}
+    ]
+    
+    results = []
+    
+    print("=" * 70)
+    print("🚀 BẮT ĐẦU HUẤN LUYỆN TẤT CẢ MÔ HÌNH PHÂN LOẠI LỚP PHỦ")
+    print("=" * 70)
+    
+    for cfg in models_config:
+        model_type = cfg['type']
+        print(f"\n[{model_type.upper()}] Đang tiến hành huấn luyện...")
+        
+        # Prepare parameters for train_model
+        params = {
+            'bbox': [105.5, 9.2, 106.3, 10.0], # Matching the working coordinates
+            'time_range': '2023-01-01/2023-04-30',
+            'model_type': model_type,
+            'feature_mode': 'extended',
+            'use_cache': True,
+            'output_model_path': f"land_classification_model/model_{model_type}_auto.joblib"
+        }
+        
+        # Merge specific hyperparameters
+        for key in ['n_estimators', 'max_depth', 'learning_rate', 'use_gpu']:
+            if key in cfg:
+                params[key] = cfg[key]
+                
+        try:
+            res = train_model(**params)
+            
+            # Extract metrics
+            if res.get('success'):
+                metrics = {
+                    'model': model_type.upper(),
+                    'accuracy': res.get('test_accuracy', 0.0),
+                    'params': f"Estimators:{cfg.get('n_estimators','-')}, Depth:{cfg.get('max_depth','-')}",
+                    'status': '✅ Success'
+                }
+            else:
+                metrics = {
+                    'model': model_type.upper(),
+                    'accuracy': 0.0,
+                    'params': '-',
+                    'status': f"❌ Failed: {res.get('error', 'Unknown')}"
+                }
+            results.append(metrics)
+            print(f"[{model_type.upper()}] ✅ Xong! Accuracy: {metrics['accuracy']:.4f}")
+            
+        except Exception as e:
+            print(f"[{model_type.upper()}] ❌ LỖI: {e}")
+            results.append({
+                'model': model_type.upper(),
+                'accuracy': 0.0,
+                'params': '-',
+                'status': f"❌ Error: {str(e)}"
+            })
+            
+    # Print summary table
+    print("\n\n" + "=" * 70)
+    print("📊 TỔNG HỢP KẾT QUẢ HUẤN LUYỆN")
+    print("=" * 70)
+    print(f"{'Mô hình':<20} | {'Độ chính xác (Acc)':<20} | {'Tham số':<25} | {'Trạng thái'}")
+    print("-" * 70)
+    for r in results:
+        acc_str = f"{r['accuracy']:.4f}" if isinstance(r['accuracy'], float) else str(r['accuracy'])
+        print(f"{r['model']:<20} | {acc_str:<20} | {r['params']:<25} | {r['status']}")
+    print("=" * 70)
+    
+if __name__ == "__main__":
+    train_all_models()