Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -838,51 +838,54 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
         
         # 調整權重配置
         base_weights = {
-            'space': 0.25,      # 從 0.28 降低
-            'exercise': 0.15,   # 從 0.18 降低
-            'grooming': 0.10,   # 從 0.12 降低
-            'experience': 0.20, # 從 0.22 降低
-            'health': 0.10,     # 從 0.12 降低
-            'noise': 0.05       # 從 0.08 降低
+            'space': 0.25,      
+            'exercise': 0.15,   
+            'grooming': 0.10,   
+            'experience': 0.20, 
+            'health': 0.10,     
+            'noise': 0.05      
         }
         
-        # 如果有孩童，加入家庭安全權重
         if user_prefs.has_children:
-            base_weights['family_safety'] = 0.15  # 新增家庭安全權重
-            # 重新標準化其他權重
-            total = sum(base_weights.values())
-            weights = {k: v/total for k, v in base_weights.items()}
+            # 不直接加入 family_safety 到權重中
+            # 而是作為一個獨立的修正因子
+            family_safety_score = calculate_family_safety_score(breed_info, user_prefs.children_age)
+            
+            # 計算基礎加權分數
+            weighted_score = sum(score * base_weights[category] for category, score in scores.items())
+            
+            # 將 family_safety_score 作為調整因子
+            # 使用較溫和的影響方式
+            safety_adjustment = (family_safety_score * 0.7) + 0.3  # 確保即使最低的安全分數也只會降低 70%
+            adjusted_score = weighted_score * safety_adjustment
         else:
-            weights = base_weights
+            weighted_score = sum(score * base_weights[category] for category, score in scores.items())
+            adjusted_score = weighted_score
         
-        # 計算加權總分
-        weighted_score = sum(score * weights[category] for category, score in scores.items())
-        
-        # 加入品種加分的影響
-        # breed_bonus 的影響應該要在加權總分之後，但在最終放大之前
-        adjusted_score = weighted_score * (1 + breed_bonus)  # breed_bonus 作為乘數效果
+        # 再加入品種加分的影響
+        breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
+        final_weighted_score = adjusted_score * (1 + breed_bonus)
         
         def amplify_score(score):
             """
-            優化分數放大函數，加入更強的差異化效果
+            修改放大函數，讓分數差異更明顯
             """
-            # 基礎調整 - 降低基準點使差異更明顯
-            adjusted = (score - 0.40) * 2.0  # 從 0.35 降至 0.40，乘數從 1.8 提高到 2.0
+            # 基礎調整
+            adjusted = (score - 0.3) * 1.5
             
-            # 使用更高的指數使曲線更陡峭
-            amplified = pow(adjusted, 3.5) / 6.2 + score  # 指數從 3.2 提高到 3.5
+            # 使用較溫和的指數
+            amplified = pow(adjusted, 2.8) / 4.5 + score
             
-            # 更嚴格的高分處理
-            if amplified > 0.85:  # 從 0.90 降至 0.85
-                amplified = 0.85 + (amplified - 0.85) * 0.4  # 係數從 0.5 降至 0.4
+            # 高分區間的處理
+            if amplified > 0.90:
+                amplified = 0.90 + (amplified - 0.90) * 0.3
             
-            # 調整分數範圍
-            final_score = max(0.60, min(0.95, amplified))  # 最低分從 0.55 提高到 0.60
+            # 擴大分數範圍（0.45-0.95）
+            final_score = max(0.45, min(0.95, amplified))
             
             return round(final_score, 3)
         
-        # 計算最終分數
-        final_score = amplify_score(adjusted_score)
+        final_score = amplify_score(final_weighted_score)
         
         # 四捨五入所有分數並回傳
         scores = {k: round(v, 4) for k, v in scores.items()}