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 +1,97 @@
+import os
+import glob
+import json
+from tabulate import tabulate
+
+print("📊 BẢNG SO SÁNH KẾT QUẢ CÁC MÔ HÌNH\n")
+
+# 1. Phân loại đất
+print("### 1. Nhóm Phân loại Lớp phủ (Land Classification)")
+land_data = []
+
+if os.path.exists("model_xgboost_info.json"):
+    with open("model_xgboost_info.json", 'r') as f:
+        data = json.load(f)
+        params = data.get('params', {})
+        param_str = f"estimators:{params.get('n_estimators')}, depth:{params.get('max_depth')}" if params else "N/A"
+        land_data.append([
+            data.get('model_type', 'XGBoost'),
+            data.get('accuracy', ''),
+            data.get('precision', ''),
+            data.get('recall', ''),
+            data.get('f1_score', ''),
+            param_str
+        ])
+
+for info_file in glob.glob("model_train/*_info.json"):
+    with open(info_file, 'r') as f:
+        data = json.load(f)
+        # Support both 'accuracy' and 'test_accuracy'
+        acc = data.get('accuracy', data.get('test_accuracy', ''))
+        
+        f1 = data.get('f1_score', '')
+        precision = data.get('precision', '')
+        recall = data.get('recall', '')
+        
+        clf_rep = data.get('classification_report')
+        if isinstance(clf_rep, dict) and 'macro avg' in clf_rep:
+            if not f1:
+                f1 = clf_rep['macro avg'].get('f1-score', '')
+            if not precision:
+                precision = clf_rep['macro avg'].get('precision', '')
+            if not recall:
+                recall = clf_rep['macro avg'].get('recall', '')
+            
+        if not acc and not f1:
+            continue
+            
+        params = data.get('params', {})
+        param_str = f"estimators:{params.get('n_estimators')}, depth:{params.get('max_depth')}" if params else "N/A"
+        
+        if data.get('model_type') == 'RandomForest_RealData':
+            param_str = "estimators:100, depth:15"
+            
+        land_data.append([
+            data.get('model_type', ''),
+            acc,
+            precision,
+            recall,
+            f1,
+            param_str
+        ])
+
+if land_data:
+    print(tabulate(land_data, headers=["Model", "Accuracy", "Precision", "Recall", "F1-Score", "Parameters"], tablefmt="github"))
+print("\n")
+
+# 2. Xóa mây
+print("### 2. Nhóm Xóa mây (Cloud Removal)")
+cloud_data = []
+for info_file in glob.glob("cloud_removal_model/*_info.json"):
+    with open(info_file, 'r') as f:
+        data = json.load(f)
+        cloud_data.append([
+            data.get('model_type', ''),
+            data.get('epoch', ''),
+            data.get('train_loss', ''),
+            data.get('val_loss', '')
+        ])
+if cloud_data:
+    print(tabulate(cloud_data, headers=["Model", "Epochs", "Train Loss", "Val Loss"], tablefmt="github"))
+print("\n")
+
+# 3. Dự báo NDVI
+print("### 3. Nhóm Dự báo Thực vật (NDVI Forecasting)")
+ndvi_data = []
+for info_file in glob.glob("ndvi_forecast_model/*_info.json"):
+    with open(info_file, 'r') as f:
+        data = json.load(f)
+        ndvi_data.append([
+            data.get('model_type', ''),
+            data.get('rmse', ''),
+            data.get('mae', ''),
+            data.get('epoch', 'N/A')
+        ])
+if ndvi_data:
+    print(tabulate(ndvi_data, headers=["Model", "RMSE", "MAE", "Epochs"], tablefmt="github"))
+print("\n")