Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -2188,10 +2188,10 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
         """
         # 進一步降低關鍵指標閾值，使系統更包容極端組合
         critical_thresholds = {
-            'space': 0.5,     
-            'exercise': 0.5,  
-            'experience': 0.6,# 重要的安全考量
-            'noise': 0.6      # 影響生活品質
+        'space': 0.45,     # 進一步降低閾值
+        'exercise': 0.45,
+        'experience': 0.55,
+        'noise': 0.55
         }
         
         critical_failures = []
@@ -2199,30 +2199,23 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
             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())
         
-        # 降低懲罰程度，特別是對空間和運動的組合
         if critical_failures:
-            # 分開處理不同類型的失敗
             space_exercise_penalty = 0
             other_penalty = 0
             
             for metric, score in critical_failures:
                 if metric in ['space', 'exercise']:
-                    # 空間和運動相關的失敗給予更溫和的懲罰
-                    space_exercise_penalty += (critical_thresholds[metric] - score) * 0.2
+                    space_exercise_penalty += (critical_thresholds[metric] - score) * 0.15  # 降低懲罰
                 else:
-                    # 其他失敗維持原有懲罰程度
-                    other_penalty += (critical_thresholds[metric] - score) * 0.4
+                    other_penalty += (critical_thresholds[metric] - score) * 0.3
             
-            # 應用懲罰時更有彈性
             total_penalty = (space_exercise_penalty + other_penalty) / 2
             base_score *= (1 - total_penalty)
             
-            # 進一步降低多重失敗的懲罰
             if len(critical_failures) > 1:
-                base_score *= (0.97 ** (len(critical_failures) - 1))  
+                base_score *= (0.98 ** (len(critical_failures) - 1))  # 進一步降低多重失敗懲罰
         
         return base_score
 
@@ -2339,45 +2332,28 @@ def amplify_score_extreme(score: float) -> float:
     使用sigmoid曲線來平滑較低分數的轉換，避免突兀的分數跳變
     """
     def smooth_curve(x: float, steepness: float = 12) -> float:
-        """
-        使用sigmoid曲線來平滑分數轉換
-        
-        參數：
-        x: 0-1之間的位置值
-        steepness: 曲線的陡峭程度，較大的值會使轉換更加陡峭
-        
-        返回：
-        0-1之間的平滑轉換值
-        """
         import math
         return 1 / (1 + math.exp(-steepness * (x - 0.5)))
 
-    # 處理最高分數範圍：90-100 -> 92-99
     if score >= 0.9:
-        position = (score - 0.9) / 0.1  # 計算在這個區間內的相對位置
-        return 0.92 + (position * 0.07)  # 線性映射到92-99
+        position = (score - 0.9) / 0.1
+        return 0.92 + (position * 0.08)  # 擴大高分區間
         
-    # 處理優秀分數範圍：80-90 -> 85-92
     elif score >= 0.8:
         position = (score - 0.8) / 0.1
-        return 0.85 + (position * 0.07)
+        return 0.82 + (position * 0.10)  # 增加分數差異
         
-    # 處理良好分數範圍：70-80 -> 78-85
     elif score >= 0.7:
         position = (score - 0.7) / 0.1
-        return 0.78 + (position * 0.07)
+        return 0.72 + (position * 0.10)  # 增加分數差異
         
-    # 處理一般分數範圍：50-70 -> 70-78
     elif score >= 0.5:
         position = (score - 0.5) / 0.2
-        base = 0.70
-        return base + (smooth_curve(position) * 0.08)
+        return 0.65 + (smooth_curve(position) * 0.07)
         
-    # 處理較低分數範圍：0-50 -> 60-70
     else:
         position = score / 0.5
-        base = 0.60
-        return base + (smooth_curve(position) * 0.10)
+        return 0.60 + (smooth_curve(position) * 0.05)
         
 
 # def amplify_score_extreme(score: float) -> float: