Patch XGBoost SHAP base_score bug

chatbot/dual_inference.py

@@ -200,11 +200,23 @@ def run_xgboost_inference(glake_id: str, weather_seq_df: pd.DataFrame):
     X = xgb_input[features_used]
     prob = float(model.predict_proba(X)[0, 1])
 
-    explainer = shap.TreeExplainer(model)
-    shap_values = explainer.shap_values(X)
-
-    feature_impacts = list(zip(features_used, shap_values[0]))
-    positive_drivers = sorted([f for f in feature_impacts if f[1] > 0], key=lambda x: x[1], reverse=True)[:3]   
+    try:
+        # Fix known SHAP bug with XGBoost 2.0+ where base_score is saved as a string array e.g. "[0.39]"
+        import json
+        booster = model.get_booster()
+        config = json.loads(booster.save_config())
+        base_score = config.get('learner', {}).get('learner_model_param', {}).get('base_score', '')
+        if isinstance(base_score, str) and base_score.startswith('[') and base_score.endswith(']'):
+            config['learner']['learner_model_param']['base_score'] = base_score.strip('[]')
+            booster.load_config(json.dumps(config))
+            
+        explainer = shap.TreeExplainer(model)
+        shap_values = explainer.shap_values(X)
+        feature_impacts = list(zip(features_used, shap_values[0]))
+        positive_drivers = sorted([f for f in feature_impacts if f[1] > 0], key=lambda x: x[1], reverse=True)[:3]   
+    except Exception as e:
+        print(f"Warning: SHAP explainability skipped due to error: {e}")
+        positive_drivers = []
 
     return {
         "probability": prob,