Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -1316,56 +1316,57 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
             'noise': calculate_noise_score(breed_info.get('Breed', ''), user_prefs.noise_tolerance)
         }
 
-        # 2. 設定基礎權重
-        weights = {
-            'space': 0.28,      
-            'exercise': 0.18,   
-            'grooming': 0.12,   
-            'experience': 0.22, 
-            'health': 0.12,     
-            'noise': 0.08      
+        # 2. 直接識別關鍵問題
+        penalties = []
+        
+        # 檢查重要的不適配情況
+        if user_prefs.living_space == 'apartment':
+            if breed_info['Size'] == 'Large':
+                penalties.append(0.75)  # 大型犬在公寓
+            elif breed_info['Size'] == 'Giant':
+                penalties.append(0.65)  # 超大型犬在公寓
+        
+        if user_prefs.experience_level == 'beginner':
+            temperament = breed_info.get('Temperament', '').lower()
+            if 'stubborn' in temperament or 'dominant' in temperament:
+                penalties.append(0.80)  # 新手配到難訓練的犬種
+        
+        # 3. 計算基礎分數
+        importance_factors = {
+            'space': 3.0,      # 空間評分的重要性提高
+            'exercise': 2.0,   # 運動需求次之
+            'experience': 2.5, # 經驗要求很重要
+            'health': 1.5,     # 健康因素適中
+            'grooming': 1.2,   # 美容需求較次要
+            'noise': 1.8       # 噪音也要考慮
         }
 
-        # 3. 改進的分數整合方法
-        def calculate_final_score(scores: dict, weights: dict, breed_info: dict) -> float:
-            weighted_components = []
+        # 調整後的分數計算
+        adjusted_scores = []
+        for category, score in scores.items():
+            # 根據重要性調整分數
+            factor = importance_factors[category]
             
-            for category, score in scores.items():
-                # 先調整個別分數，保持差異性
-                if score < 0.4:
-                    adjusted = score * 0.7  # 低分更低
-                elif score > 0.8:
-                    adjusted = score * 1.1  # 高分更高
-                else:
-                    adjusted = score  # 中間分數保持不變
+            # 分數調整：保持差異性
+            if score < 0.4:
+                adjusted = score * 0.7  # 低分更低
+            elif score > 0.8:
+                adjusted = score * 1.1  # 高分更高
+            else:
+                adjusted = score
                 
-                # 應用權重
-                weighted_components.append(adjusted * weights[category])
-            
-            # 計算基礎加權分數
-            base_score = sum(weighted_components)
-            
-            # 關鍵條件檢查和調整
-            size = breed_info['Size']
-            temperament = breed_info.get('Temperament', '').lower()
-            
-            # 應用條件調整
-            if user_prefs.living_space == 'apartment':
-                if size in ['Large', 'Giant']:
-                    base_score *= 0.8
-                elif 'high energy' in temperament:
-                    base_score *= 0.85
-            
-            if user_prefs.experience_level == 'beginner':
-                if any(trait in temperament for trait in ['stubborn', 'dominant']):
-                    base_score *= 0.85
-            
-            # 最終分數映射到合理範圍
-            final_score = 0.6 + (base_score - 0.5) * 1.5
-            return max(0.55, min(0.95, final_score))
-
+            adjusted_scores.append(adjusted * factor)
+        
         # 4. 計算最終分數
-        final_score = calculate_final_score(scores, weights, breed_info)
+        base_score = sum(adjusted_scores) / sum(importance_factors.values())
+        
+        # 應用懲罰（如果有）
+        final_score = base_score
+        if penalties:
+            final_score *= min(penalties)  # 使用最嚴重的懲罰
+        
+        # 確保分數在合理範圍內
+        final_score = max(0.55, min(0.95, final_score))
         
         # 5. 準備返回結果
         scores['overall'] = round(final_score, 4)