Update model_predictor.py

model_predictor.py

@@ -1,3 +1,4 @@
+# 修正後的 model_predictor.py
 import xgboost as xgb
 import pandas as pd
 import numpy as np
@@ -27,72 +28,168 @@ class XGBoostModel:
         except Exception as e:
             raise FileNotFoundError(f"無法在本地找到或載入模型檔案 '{filename}'：{e}")
 
+    def _prepare_features(self, input_df):
+        """
+        將 yfinance 的數據格式轉換為模型期望的格式
+        """
+        # 創建新的 DataFrame 來存放轉換後的特徵
+        features_df = pd.DataFrame()
+        
+        # 基本價格和交易量特徵（轉換為小寫）
+        if 'Close' in input_df.columns:
+            features_df['close'] = input_df['Close']
+        if 'Volume' in input_df.columns:
+            features_df['volume'] = input_df['Volume']
+        
+        # 計算技術指標（如果不存在的話）
+        if len(input_df) >= 14:  # 確保有足夠的數據計算指標
+            # RSI
+            delta = input_df['Close'].diff()
+            gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+            loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+            rs = gain / loss
+            features_df['RSI'] = 100 - (100 / (1 + rs))
+            
+            # MACD
+            exp1 = input_df['Close'].ewm(span=12).mean()
+            exp2 = input_df['Close'].ewm(span=26).mean()
+            features_df['MACD'] = exp1 - exp2
+            features_df['MACDsign'] = features_df['MACD'].ewm(span=9).mean()
+            features_df['MACDvol'] = features_df['MACD'] - features_df['MACDsign']
+            
+            # KD指標
+            if len(input_df) >= 9:
+                low_min = input_df['Low'].rolling(window=9).min()
+                high_max = input_df['High'].rolling(window=9).max()
+                rsv = (input_df['Close'] - low_min) / (high_max - low_min) * 100
+                features_df['K'] = rsv.ewm(com=2).mean()
+                features_df['D'] = features_df['K'].ewm(com=2).mean()
+            
+            # DMI指標
+            up_move = input_df['High'] - input_df['High'].shift(1)
+            down_move = input_df['Low'].shift(1) - input_df['Low']
+            plus_dm = np.where((up_move > down_move) & (up_move > 0), up_move, 0)
+            minus_dm = np.where((down_move > up_move) & (down_move > 0), down_move, 0)
+            tr = np.max([input_df['High'] - input_df['Low'], 
+                        abs(input_df['High'] - input_df['Close'].shift(1)), 
+                        abs(input_df['Low'] - input_df['Close'].shift(1))], axis=0)
+            
+            plus_dm_series = pd.Series(plus_dm, index=input_df.index)
+            minus_dm_series = pd.Series(minus_dm, index=input_df.index)
+            tr_series = pd.Series(tr, index=input_df.index)
+            
+            features_df['+DI'] = (plus_dm_series.ewm(com=13, adjust=False).mean() / 
+                                 tr_series.ewm(com=13, adjust=False).mean()) * 100
+            features_df['-DI'] = (minus_dm_series.ewm(com=13, adjust=False).mean() / 
+                                 tr_series.ewm(com=13, adjust=False).mean()) * 100
+            dx = abs(features_df['+DI'] - features_df['-DI']) / (features_df['+DI'] + features_df['-DI']) * 100
+            features_df['ADX'] = dx.ewm(com=13, adjust=False).mean()
+        
+        # 計算報酬率
+        if 'Close' in input_df.columns:
+            features_df['rate'] = input_df['Close'].pct_change()
+        
+        # 模擬缺失的外部數據（使用合理的預設值或簡單的代理值）
+        # 這些值在實際部署時應該來自真實的數據源
+        features_df['DJI'] = 0.0  # 道瓊工業指數變化率的代理值
+        features_df['NAS'] = 0.0  # 納斯達克指數變化率的代理值
+        features_df['SOX'] = 0.0  # 費城半導體指數變化率的代理值
+        features_df['S&P_500'] = 0.0  # S&P 500指數變化率的代理值
+        features_df['TSM_ADR'] = 0.0  # 台積電ADR變化率的代理值
+        features_df['NEWS'] = 0.0  # 新聞情緒分數的代理值
+        features_df['business_climate'] = 25.0  # 景氣燈號的代理值
+        features_df['PMI'] = 50.0  # PMI指標的代理值
+        
+        # 確保所有必要的欄位都存在，並填充缺失值
+        required_columns = [
+            'close', 'volume', 'rate', 'DJI', 'NAS', 'SOX', 'S&P_500', 'TSM_ADR',
+            'NEWS', 'RSI', 'MACD', 'MACDsign', 'MACDvol', 'K', 'D', '+DI', '-DI', 
+            'ADX', 'business_climate', 'PMI'
+        ]
+        
+        for col in required_columns:
+            if col not in features_df.columns:
+                features_df[col] = 0.0  # 用0填充缺失的欄位
+        
+        # 只保留模型需要的欄位，並確保順序正確
+        features_df = features_df[required_columns]
+        
+        # 填充任何剩餘的NaN值
+        features_df = features_df.fillna(method='ffill').fillna(0)
+        
+        return features_df.tail(1)  # 只返回最後一行用於預測
+
     def predict(self, model_name, input_df):
         # 如果請求的模型名稱與目前載入的不同，則動態載入
         if model_name != self.current_model_name:
             self.model = self._load_model(model_name)
             self.current_model_name = model_name
         
-        # 進行預測
-        predictions = self.model.predict(input_df)
-        
-        # 調試：印出預測結果的形狀和內容
-        print(f"預測結果形狀: {predictions.shape}")
-        print(f"預測結果類型: {type(predictions)}")
-        print(f"預測結果內容: {predictions}")
-        
-        # 處理不同的輸出格式
         try:
-            # 情況1: 如果是二維陣列且有4個預測值 (原始期望格式)
-            if len(predictions.shape) == 2 and predictions.shape[1] == 4:
-                result = {
-                    'Close_t0_pred': float(predictions[0][0]),
-                    'Close_t5_pred': float(predictions[0][1]),
-                    'Close_t10_pred': float(predictions[0][2]),
-                    'Close_t20_pred': float(predictions[0][3])
-                }
-                
-            # 情況2: 如果是一維陣列且有4個預測值
-            elif len(predictions.shape) == 1 and len(predictions) == 4:
-                result = {
-                    'Close_t0_pred': float(predictions[0]),
-                    'Close_t5_pred': float(predictions[1]),
-                    'Close_t10_pred': float(predictions[2]),
-                    'Close_t20_pred': float(predictions[3])
-                }
-                
-            # 情況3: 如果只有一個預測值（單一輸出模型）
-            elif len(predictions.shape) == 1 and len(predictions) == 1:
-                # 假設這個預測值代表最近期的預測，其他用相同值
-                pred_value = float(predictions[0])
-                result = {
-                    'Close_t0_pred': pred_value,
-                    'Close_t5_pred': pred_value,
-                    'Close_t10_pred': pred_value,
-                    'Close_t20_pred': pred_value
-                }
-                
-            # 情況4: 如果是標量（單一數值）
-            elif np.isscalar(predictions):
-                pred_value = float(predictions)
-                result = {
-                    'Close_t0_pred': pred_value,
-                    'Close_t5_pred': pred_value,
-                    'Close_t10_pred': pred_value,
-                    'Close_t20_pred': pred_value
-                }
-                
+            # 轉換輸入特徵格式
+            prepared_features = self._prepare_features(input_df)
+            
+            print(f"準備的特徵形狀: {prepared_features.shape}")
+            print(f"特徵欄位: {list(prepared_features.columns)}")
+            
+            # 進行預測
+            predictions = self.model.predict(prepared_features)
+            
+            print(f"原始預測結果: {predictions}")
+            print(f"預測結果形狀: {predictions.shape if hasattr(predictions, 'shape') else 'scalar'}")
+            print(f"預測結果類型: {type(predictions)}")
+            
+            # 處理不同的輸出格式
+            if hasattr(predictions, 'shape'):
+                if len(predictions.shape) == 2 and predictions.shape[1] == 4:
+                    # 情況1: 二維陣列，4個預測值
+                    result = {
+                        'Close_t0_pred': float(predictions[0][0]),
+                        'Close_t5_pred': float(predictions[0][1]),
+                        'Close_t10_pred': float(predictions[0][2]),
+                        'Close_t20_pred': float(predictions[0][3])
+                    }
+                elif len(predictions.shape) == 1 and len(predictions) == 4:
+                    # 情況2: 一維陣列，4個預測值
+                    result = {
+                        'Close_t0_pred': float(predictions[0]),
+                        'Close_t5_pred': float(predictions[1]),
+                        'Close_t10_pred': float(predictions[2]),
+                        'Close_t20_pred': float(predictions[3])
+                    }
+                elif len(predictions.shape) == 1 and len(predictions) == 1:
+                    # 情況3: 一維陣列，1個預測值（使用同一個值代表所有時期）
+                    pred_value = float(predictions[0])
+                    result = {
+                        'Close_t0_pred': pred_value,
+                        'Close_t5_pred': pred_value,
+                        'Close_t10_pred': pred_value,
+                        'Close_t20_pred': pred_value
+                    }
+                else:
+                    # 其他情況：嘗試使用第一個值
+                    pred_value = float(predictions.flatten()[0])
+                    result = {
+                        'Close_t0_pred': pred_value,
+                        'Close_t5_pred': pred_value,
+                        'Close_t10_pred': pred_value,
+                        'Close_t20_pred': pred_value
+                    }
             else:
-                # 其他情況：嘗試使用第一個預測值
-                pred_value = float(predictions.flatten()[0])
+                # 標量值
+                pred_value = float(predictions)
                 result = {
                     'Close_t0_pred': pred_value,
                     'Close_t5_pred': pred_value,
                     'Close_t10_pred': pred_value,
                     'Close_t20_pred': pred_value
                 }
-                
-        except (IndexError, TypeError) as e:
-            raise ValueError(f"無法解析模型輸出格式。預測結果: {predictions}, 錯誤: {e}")
             
-        return result
+            print(f"最終結果: {result}")
+            return result
+            
+        except Exception as e:
+            print(f"預測過程中發生錯誤: {e}")
+            import traceback
+            traceback.print_exc()
+            raise e