Update model_predictor.py

model_predictor.py

@@ -159,81 +159,81 @@ class XGBoostModel:
         return input_df
 
     def predict(self, model_name, input_df):
-    """
-    進行股價漲幅預測（已加入自動對齊模型 feature_names 的邏輯）
-    """
-    try:
-        # 載入模型（如果尚未載入）
-        if self.model is None:
-            model_path = f"{model_name}.json"
-            if not self.load_model(model_path):
-                return None
-
-        # 先做基本的特徵預處理（會補缺失欄位為 0，但不重新排序）
-        processed_df = input_df.copy()
-        processed_df = self.preprocess_features(processed_df.copy())
-
-        # 嘗試從已載入的 xgboost 模型中取得訓練時的 feature names
-        expected_features = None
+        """
+        進行股價漲幅預測（已加入自動對齊模型 feature_names 的邏輯）
+        """
         try:
-            booster = self.model.get_booster()
-            expected_features = getattr(booster, "feature_names", None)
-        except Exception:
+            # 載入模型（如果尚未載入）
+            if self.model is None:
+                model_path = f"{model_name}.json"
+                if not self.load_model(model_path):
+                    return None
+    
+            # 先做基本的特徵預處理（會補缺失欄位為 0，但不重新排序）
+            processed_df = input_df.copy()
+            processed_df = self.preprocess_features(processed_df.copy())
+    
+            # 嘗試從已載入的 xgboost 模型中取得訓練時的 feature names
             expected_features = None
-
-        if expected_features:
-            # 檢查缺失或多餘欄位
-            missing = [f for f in expected_features if f not in processed_df.columns]
-            extra = [f for f in processed_df.columns if f not in expected_features]
-
-            if missing:
-                print(f"警告：模型期待以下特徵但輸入缺失，將以 0 補齊: {missing}")
-                for f in missing:
-                    processed_df[f] = 0.0
-
-            if extra:
-                print(f"注意：輸入含有模型未使用的額外特徵，將忽略: {extra}")
-
-            # 依模型期待欄位順序重排（且只保留 expected_features）
-            processed_df = processed_df[expected_features]
-        else:
-            # 如果模型沒有記錄 feature_names，退回到 class 裡預設的 feature_columns（如有）
-            processed_df = processed_df[self.feature_columns]
-
-        # DEBUG 訊息
-        print("=== 模型輸入特徵檢查（對齊後） ===")
-        print(f"輸入形狀: {processed_df.shape}")
-        print("前5個特徵值:")
-        for i, col in enumerate(processed_df.columns[:5]):
-            print(f"  {col}: {processed_df[col].iloc[0]:.6f}")
-
-        # 進行預測
-        predictions = self.model.predict(processed_df)
-        print(f"原始預測輸出形狀: {getattr(predictions, 'shape', str(type(predictions)))}")
-        print(f"原始預測值: {predictions}")
-
-        # 處理多輸出或單輸出
-        if getattr(predictions, 'ndim', 1) == 1:
-            # 單輸出
-            result = {'Change_pct_t1_pred': float(predictions[0])}
-        else:
-            result = {}
-            target_keys = ['Change_pct_t1_pred', 'Change_pct_t5_pred',
-                           'Change_pct_t10_pred', 'Change_pct_t20_pred']
-            for i, key in enumerate(target_keys):
-                if i < predictions.shape[1]:
-                    result[key] = float(predictions[0][i])
-                else:
-                    result[key] = 0.0
-
-        # 列印摘要
-        print("=== 漲幅預測結果 ===")
-        for key, value in result.items():
-            days = key.split('_')[2][1:]
-            direction = "上漲" if value > 0 else "下跌"
-            print(f"  {days}日後預測: {value:+.2f}% ({direction})")
-
-        return result
+            try:
+                booster = self.model.get_booster()
+                expected_features = getattr(booster, "feature_names", None)
+            except Exception:
+                expected_features = None
+    
+            if expected_features:
+                # 檢查缺失或多餘欄位
+                missing = [f for f in expected_features if f not in processed_df.columns]
+                extra = [f for f in processed_df.columns if f not in expected_features]
+    
+                if missing:
+                    print(f"警告：模型期待以下特徵但輸入缺失，將以 0 補齊: {missing}")
+                    for f in missing:
+                        processed_df[f] = 0.0
+    
+                if extra:
+                    print(f"注意：輸入含有模型未使用的額外特徵，將忽略: {extra}")
+    
+                # 依模型期待欄位順序重排（且只保留 expected_features）
+                processed_df = processed_df[expected_features]
+            else:
+                # 如果模型沒有記錄 feature_names，退回到 class 裡預設的 feature_columns（如有）
+                processed_df = processed_df[self.feature_columns]
+    
+            # DEBUG 訊息
+            print("=== 模型輸入特徵檢查（對齊後） ===")
+            print(f"輸入形狀: {processed_df.shape}")
+            print("前5個特徵值:")
+            for i, col in enumerate(processed_df.columns[:5]):
+                print(f"  {col}: {processed_df[col].iloc[0]:.6f}")
+    
+            # 進行預測
+            predictions = self.model.predict(processed_df)
+            print(f"原始預測輸出形狀: {getattr(predictions, 'shape', str(type(predictions)))}")
+            print(f"原始預測值: {predictions}")
+    
+            # 處理多輸出或單輸出
+            if getattr(predictions, 'ndim', 1) == 1:
+                # 單輸出
+                result = {'Change_pct_t1_pred': float(predictions[0])}
+            else:
+                result = {}
+                target_keys = ['Change_pct_t1_pred', 'Change_pct_t5_pred',
+                               'Change_pct_t10_pred', 'Change_pct_t20_pred']
+                for i, key in enumerate(target_keys):
+                    if i < predictions.shape[1]:
+                        result[key] = float(predictions[0][i])
+                    else:
+                        result[key] = 0.0
+    
+            # 列印摘要
+            print("=== 漲幅預測結果 ===")
+            for key, value in result.items():
+                days = key.split('_')[2][1:]
+                direction = "上漲" if value > 0 else "下跌"
+                print(f"  {days}日後預測: {value:+.2f}% ({direction})")
+    
+            return result
 
     except Exception as e:
         print(f"預測過程中發生錯誤：{e}")