Update model_predictor.py

model_predictor.py

@@ -74,73 +74,23 @@ class XGBoostModel:
             return False
 
     def load_scaler(self, scaler_path):
-        """
-        載入特徵標準化器
-        
-        Args:
-            scaler_path (str): 標準化器檔案路徑 (.pkl 格式)
-        
-        Returns:
-            bool: 是否成功載入
-        """
-        try:
-            if os.path.exists(scaler_path):
-                self.scaler = joblib.load(scaler_path)
-                print(f"成功載入標準化器：{scaler_path}")
-                return True
-            else:
-                print(f"警告：找不到標準化器檔案 {scaler_path}")
-                print("將使用預設標準化器")
-                self.scaler = StandardScaler()
-                return False
-                
-        except Exception as e:
-            print(f"載入標準化器時發生錯誤：{e}")
-            self.scaler = StandardScaler()
-            return False
+        """停用標準化流程"""
+        print("⚠️ 已停用標準化：模型使用原始特徵進行預測。")
+        self.scaler = None
+        return False
 
     def preprocess_features(self, input_df):
-        """
-        預處理輸入特徵
-        
-        Args:
-            input_df (pd.DataFrame): 輸入特徵 DataFrame
-            
-        Returns:
-            pd.DataFrame: 預處理後的特徵
-        """
-        try:
-            # 確保輸入包含所有必要特徵
-            missing_features = [f for f in self.feature_columns if f not in input_df.columns]
-            if missing_features:
-                print(f"警告：缺少以下特徵：{missing_features}")
-                # 用 0 填補缺少的特徵
-                for feature in missing_features:
-                    input_df[feature] = 0
-            
-            # 按照預期順序重新排列特徵
-            input_df = input_df[self.feature_columns]
-            
-            # 處理 NaN 值
-            input_df = input_df.fillna(0)
-            
-            # 如果有標準化器，進行標準化
-            if self.scaler is not None:
-                try:
-                    # 嘗試使用已訓練的標準化器
-                    scaled_features = self.scaler.transform(input_df)
-                    input_df = pd.DataFrame(scaled_features, 
-                                          columns=input_df.columns, 
-                                          index=input_df.index)
-                except Exception as scaler_error:
-                    print(f"標準化過程發生錯誤：{scaler_error}")
-                    print("跳過標準化步驟")
-            
-            return input_df
-            
-        except Exception as e:
-            print(f"特徵預處理時發生錯誤：{e}")
-            return input_df
+        # 確保特徵齊全
+        missing_features = [f for f in self.feature_columns if f not in input_df.columns]
+        if missing_features:
+            print(f"警告：缺少以下特徵：{missing_features}")
+            for feature in missing_features:
+                input_df[feature] = 0
+    
+        input_df = input_df[self.feature_columns].fillna(0)
+
+        # ✅ 直接回傳原始特徵
+        return input_df
 
     def predict(self, model_name, input_df):
         """