main

ai-engine/models/v25_ensemble.py

@@ -228,15 +228,13 @@ class V25Predictor:
         print(f"[V25] Using fallback feature columns ({len(V25Predictor._FALLBACK_FEATURE_COLS)} features)")
         return V25Predictor._FALLBACK_FEATURE_COLS
 
-    FEATURE_COLS = _load_feature_cols.__func__()
-    
     # Model weights for ensemble
     DEFAULT_WEIGHTS = {
         'xgb': 0.50,
         'lgb': 0.50,
     }
-    
-    def __init__(self, models_dir: str = None):
+
+    def __init__(self, models_dir: Optional[str] = None):
         """
         Initialize V25 Predictor.
         
@@ -246,6 +244,7 @@ class V25Predictor:
         self.models_dir = models_dir or MODELS_DIR
         self.models = {}  # market -> {'xgb': model, 'lgb': model}
         self._loaded = False
+        self.FEATURE_COLS = self._load_feature_cols()
     
     # All trained market models available in V25
     ALL_MARKETS = [
@@ -412,7 +411,7 @@ class V25Predictor:
         
         return float(avg_prob), float(1 - avg_prob)
     
-    def predict_market(self, market: str, features: Dict[str, float]) -> np.ndarray:
+    def predict_market(self, market: str, features: Dict[str, float]) -> Optional[np.ndarray]:
         """
         Generic prediction for any loaded market.
         
@@ -510,15 +509,15 @@ class V25Predictor:
         
         # Determine picks
         ms_probs = {'1': home_prob, 'X': draw_prob, '2': away_prob}
-        ms_pick = max(ms_probs, key=ms_probs.get)
+        ms_pick = max(ms_probs, key=ms_probs.__getitem__)
         ms_confidence = ms_probs[ms_pick] * 100
         
         ou25_probs = {'Over': over_prob, 'Under': under_prob}
-        ou25_pick = max(ou25_probs, key=ou25_probs.get)
+        ou25_pick = max(ou25_probs, key=ou25_probs.__getitem__)
         ou25_confidence = ou25_probs[ou25_pick] * 100
         
         btts_probs = {'Yes': btts_yes_prob, 'No': btts_no_prob}
-        btts_pick = max(btts_probs, key=btts_probs.get)
+        btts_pick = max(btts_probs, key=btts_probs.__getitem__)
         btts_confidence = btts_probs[btts_pick] * 100
         
         # Create prediction