完成模型更新

core/scoring/inference/scorer.py

@@ -1,5 +1,5 @@
 """
-grid_seeker v6.6 三级模型推理管道
+grid_seeker v6.7r3 三级模型推理管道
 Rank → Top → Stacking → stacking_probability (最终排序)
 """
 import pickle
@@ -18,7 +18,7 @@ from core.logger import LogLevel, PrintLog
 
 
 # ============================================================
-# Rank 模型输入特征 (52维, v3.4, 直接从模型文件的 selected_features 读取)
+# Rank 模型输入特征 (56维 v6.7/v3.4, 同时支持 feat_names 和 selected_features)
 # ============================================================
 def _get_rank_features() -> list:
     import pickle
@@ -27,17 +27,26 @@ def _get_rank_features() -> list:
     with open(path, 'rb') as f:
         obj = pickle.load(f)
     if isinstance(obj, dict):
-        sf = obj.get('selected_features', [])
+        # v6.7r3 使用 feat_names, v6.6 使用 selected_features
+        sf = obj.get('feat_names', []) or obj.get('selected_features', [])
         if sf:
             return sf
-    raise RuntimeError("无法从 rank.pkl 读取 selected_features")
+    raise RuntimeError("无法从 rank.pkl 读取 feat_names 或 selected_features")
 
 RANK_FEATURE_COLS = _get_rank_features()
 
+# Top/Stacking 模型只用 52 维基础特征（不含 v6.7 新增的4维）
+# v6.7 新增: vol_decay_5d, grid_touch_relative_10d, vol_decay_x_grid_balance, vol_decay_x_dist_lower
+_V67_NEW_FEATS = {
+    'vol_decay_5d', 'grid_touch_relative_10d',
+    'vol_decay_x_grid_balance', 'vol_decay_x_dist_lower'
+}
+BASE_52_COLS = [f for f in RANK_FEATURE_COLS if f not in _V67_NEW_FEATS]
+
 
 class GridSeekerPipeline:
     """
-    grid_seeker v6.6 三级模型评分管道。
+    grid_seeker v6.7r3 三级模型评分管道。
 
     Usage:
         engine = GridSeekerPipeline()
@@ -120,7 +129,7 @@ class GridSeekerPipeline:
             DataFrame indexed by stock_code, 含 stacking_probability / rank 等列,
             按 stacking_probability 降序排列
         """
-        PrintLog(LogLevel.INFO, f'[scorer] ===== grid_seeker v6.6 评分开始 ({trade_date}) =====')
+        PrintLog(LogLevel.INFO, f'[scorer] ===== grid_seeker v6.7r3 评分开始 ({trade_date}) =====')
 
         # 1. 特征工程
         pipeline = FeaturePipeline(trade_date)
@@ -140,16 +149,16 @@ class GridSeekerPipeline:
             self.rank_model, feature_df, RANK_FEATURE_COLS
         )
 
-        # 3. Stage 2: Top 模型 → top_elite_prob (53维 = 52 + rank_predicted_rounds)
+        # 3. Stage 2: Top 模型 → top_elite_prob (53维 = 52基础 + rank)
         PrintLog(LogLevel.INFO, '[scorer] Stage 2/3: Top 模型...')
-        top_cols = RANK_FEATURE_COLS + ['rank_predicted_rounds']
+        top_cols = BASE_52_COLS + ['rank_predicted_rounds']
         feature_df['top_elite_prob'] = self._predict_with_model(
             self.top_model, feature_df, top_cols
         )
 
-        # 4. Stage 3: Stacking 模型 → stacking_probability (54维 = 52 + rank + top)
+        # 4. Stage 3: Stacking 模型 → stacking_probability (55维 = 52基础 + rank + top)
         PrintLog(LogLevel.INFO, '[scorer] Stage 3/3: Stacking 模型...')
-        stk_cols = RANK_FEATURE_COLS + ['rank_predicted_rounds', 'top_elite_prob']
+        stk_cols = BASE_52_COLS + ['rank_predicted_rounds', 'top_elite_prob']
         feature_df['stacking_probability'] = self._predict_with_model(
             self.stacking_model, feature_df, stk_cols
         )