Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -2183,46 +2183,10 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
         final_score = score * severity_multiplier
         return max(0.2, min(1.0, final_score))
 
-    # def calculate_base_score(scores: dict, weights: dict) -> float:
-    #     """
-    #     計算基礎分數，加入更嚴格的評估機制。
-    #     就像學校的評分系統，某些科目不及格會嚴重影響總成績。
-    #     """
-    #     # 檢查關鍵指標是否有嚴重不足
-    #     critical_thresholds = {
-    #         'space': 0.7,
-    #         'exercise': 0.7,
-    #         'experience': 0.7,
-    #         'noise': 0.65
-    #     }
-        
-    #     critical_failures = []
-    #     for metric, threshold in critical_thresholds.items():
-    #         if scores[metric] < threshold:
-    #             critical_failures.append((metric, scores[metric]))
-        
-    #     # 計算基礎加權分數
-    #     base_score = sum(scores[k] * weights[k] for k in scores.keys())
-        
-    #     # 根據關鍵指標的不足程度進行懲罰
-    #     if critical_failures:
-    #         # 計算最嚴重的不足程度
-    #         worst_failure = min(score for _, score in critical_failures)
-    #         penalty = (critical_thresholds['space'] - worst_failure) * 0.6
-    #         base_score *= (1 - penalty)
-            
-    #         # 多個指標不足時的額外懲罰
-    #         if len(critical_failures) > 1:
-    #             base_score *= (0.9 ** (len(critical_failures) - 1))
-        
-    #     return base_score
-
     def calculate_base_score(scores: dict, weights: dict) -> float:
         """
-        計算基礎分數，特別加強：
-        1. 公寓住戶高運動量的評估
-        2. 確保理想匹配的最低分數
-        3. 平衡空間限制與運動能力的關係
+        計算基礎分數，加入更嚴格的評估機制。
+        就像學校的評分系統，某些科目不及格會嚴重影響總成績。
         """
         # 檢查關鍵指標是否有嚴重不足
         critical_thresholds = {
@@ -2240,41 +2204,20 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
         # 計算基礎加權分數
         base_score = sum(scores[k] * weights[k] for k in scores.keys())
         
-        # 特殊情況處理：公寓+高運動量
-        is_apartment_with_high_exercise = (
-            user_prefs.living_space == 'apartment' and
-            user_prefs.exercise_time >= 120 and
-            scores['exercise'] >= 0.7  # 確保運動能力符合
-        )
-        
         # 根據關鍵指標的不足程度進行懲罰
         if critical_failures:
             # 計算最嚴重的不足程度
             worst_failure = min(score for _, score in critical_failures)
-            
-            # 特殊情況：公寓+高運動量時降低空間限制的懲罰
-            if is_apartment_with_high_exercise:
-                penalty = (critical_thresholds['space'] - worst_failure) * 0.3  # 降低懲罰係數
-            else:
-                penalty = (critical_thresholds['space'] - worst_failure) * 0.6
-                
+            penalty = (critical_thresholds['space'] - worst_failure) * 0.6
             base_score *= (1 - penalty)
             
             # 多個指標不足時的額外懲罰
             if len(critical_failures) > 1:
-                if is_apartment_with_high_exercise:
-                    base_score *= (0.95 ** (len(critical_failures) - 1))  # 降低疊加懲罰
-                else:
-                    base_score *= (0.9 ** (len(critical_failures) - 1))
+                base_score *= (0.9 ** (len(critical_failures) - 1))
         
-        # 確保理想匹配的最低分數
-        if (user_prefs.living_space == 'apartment' and
-            scores['exercise'] >= 0.8 and
-            scores['noise'] >= 0.7):
-            base_score = max(base_score, 0.85)  # 設定最低分數
-            
         return base_score
 
+  
     def evaluate_condition_interactions(scores: dict) -> float:
         """
         評估不同條件之間的相互影響。