Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -1508,27 +1508,24 @@ def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -
     return min(0.2, adaptability_score)
 
 
-def calculate_breed_compatibility_score(
-    scores: dict,
-    user_prefs: UserPreferences,
-    breed_info: dict
-) -> float:
+def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreferences, breed_info: dict) -> float:
     """
-    整合的品種相容性評分系統
-    
-    這個函數整合了：
-    1. 關鍵參數評估
-    2. 動態權重計算
-    3. 環境適應性評估
-    4. 品種特性加成
-    5. 最終分數計算和轉換
+    計算品種與使用者的整體相容性分數
+    
+    Args:
+        scores: 基礎分項分數字典
+        user_prefs: 使用者偏好
+        breed_info: 品種資訊
+        
+    Returns:
+        最終相容性分數 (0.3-0.95)
     """
-    # 1. 首先檢查關鍵不適配情況
+    # 1. 檢查關鍵不適配參數
     critical_params = {
         'space': {
             'threshold': 0.3,
-            'conditions': lambda: True,  # 永遠檢查
-            'penalty': 0.3  # 極低分數
+            'conditions': lambda p: True,
+            'penalty': 0.3
         },
         'noise': {
             'threshold': 0.3,
@@ -1542,12 +1539,12 @@ def calculate_breed_compatibility_score(
         }
     }
 
-    # 檢查關鍵參數
+    # 檢查並處理關鍵不適配情況
     for param, config in critical_params.items():
         if scores[param] < config['threshold'] and config['conditions'](user_prefs):
             return config['penalty']
 
-    # 2. 計算基礎權重
+    # 2. 基礎權重設定
     base_weights = {
         'space': 0.35,
         'exercise': 0.30,
@@ -1562,42 +1559,63 @@ def calculate_breed_compatibility_score(
     for param, weight in base_weights.items():
         multiplier = 1.0
         
-        # 根據具體條件調整權重
-        if param == 'space' and user_prefs.living_space == 'apartment':
+        # 居住空間相關調整
+        if param == 'space':
+            if user_prefs.living_space == 'apartment':
+                multiplier *= 1.2
+            elif breed_info['Size'] in ['Large', 'Giant']:
+                multiplier *= 1.3
+        
+        # 運動需求相關調整
+        elif param == 'exercise':
+            if user_prefs.exercise_time > 150:
+                multiplier *= 1.4
+            elif user_prefs.exercise_time < 60:
+                multiplier *= 1.2
+                
+        # 經驗相關調整
+        elif param == 'experience' and user_prefs.experience_level == 'beginner':
+            multiplier *= 1.3
+            
+        # 美容需求調整
+        elif param == 'grooming' and breed_info.get('Grooming Needs') == 'High':
             multiplier *= 1.2
-        elif param == 'exercise' and user_prefs.exercise_time > 150:
+            
+        # 健康相關調整
+        elif param == 'health' and user_prefs.health_sensitivity == 'high':
+            multiplier *= 1.3
+            
+        # 噪音相關調整
+        elif param == 'noise' and user_prefs.living_space == 'apartment':
             multiplier *= 1.4
-        # ... 其他調整條件
-        
+            
         adjusted_weights[param] = weight * multiplier
 
     # 重新正規化權重
     total_weight = sum(adjusted_weights.values())
     normalized_weights = {k: v/total_weight for k, v in adjusted_weights.items()}
 
-    # 4. 計算加權基礎分數
+    # 4. 計算基礎加權分數
     base_score = 0
     for param, weight in normalized_weights.items():
         score = scores[param]
         
-        # 非線性調整
+        # 非線性分數調整
         if score > 0.8:
-            score = min(1.0, score * 1.2)
+            score = min(1.0, score * 1.2)  # 高分獎勵
         elif score < 0.6:
-            score = score * 0.8
+            score = score * 0.8  # 低分懲罰
             
         base_score += score * weight
 
-    # 5. 計算環境適應性加成
+    # 5. 整合特性加成
     adaptability_bonus = calculate_environmental_fit(breed_info, user_prefs)
-
-    # 6. 計算品種特性加成
     breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
 
-    # 7. 整合最終分數
+    # 6. 計算最終分數
     final_score = (base_score * 0.70) + (breed_bonus * 0.20) + (adaptability_bonus * 0.10)
 
-    # 8. 轉換和限制分數範圍
+    # 7. 轉換並限制分數範圍
     return amplify_score_extreme(final_score)