Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -1326,66 +1326,51 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
             'noise': 0.08      
         }
 
-        # 3. 條件權重調整
-        if user_prefs.living_space == 'apartment':
-            if scores['space'] < 0.7:  # 空間不足時加重權重
-                weights['space'] *= 1.3
-                weights['noise'] *= 1.2
-        
-        if user_prefs.experience_level == 'beginner':
-            if scores['experience'] < 0.6:  # 經驗要求高時加重權重
-                weights['experience'] *= 1.4
-
-        # 重新正規化權重
-        total = sum(weights.values())
-        weights = {k: v/total for k, v in weights.items()}
-
-        # 4. 計算加權分數
-        weighted_score = sum(score * weights[category] for category, score in scores.items())
-
-        # 5. 新的分數放大函數
-        def amplify_score(raw_score, scores):
-            """
-            直接的分數轉換，保持分數差異性
-            """
-            # 基礎分數調整：擴大差異
-            adjusted = (raw_score - 0.5) * 1.8
+        # 3. 改進的分數整合方法
+        def calculate_final_score(scores: dict, weights: dict, breed_info: dict) -> float:
+            weighted_components = []
             
-            # 線性轉換到目標範圍
-            score = 0.7 + adjusted * 0.5
+            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
+                
+                # 應用權重
+                weighted_components.append(adjusted * weights[category])
             
-            # 處理極端情況
-            if any(v < 0.4 for v in scores.values()):
-                score *= 0.85  # 有極低分項目時降低整體分數
+            # 計算基礎加權分數
+            base_score = sum(weighted_components)
             
-            # 確保分數在合理範圍內
-            return max(0.55, min(0.95, score))
+            # 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))
 
-        # 6. 計算最終分數
-        final_score = amplify_score(weighted_score, scores)
-        
-        # 7. 品種特定調整
-        breed_name = breed_info.get('Breed', '')
-        temperament = breed_info.get('Temperament', '').lower()
+        # 4. 計算最終分數
+        final_score = calculate_final_score(scores, weights, breed_info)
         
-        # 根據具體條件進行品種特定的調整
-        if user_prefs.living_space == 'apartment':
-            if breed_info['Size'] in ['Large', 'Giant']:
-                final_score *= 0.85
-            elif 'high energy' in temperament or 'very active' in temperament:
-                final_score *= 0.9
-                
-        if user_prefs.experience_level == 'beginner':
-            if any(trait in temperament for trait in ['dominant', 'stubborn', 'independent']):
-                final_score *= 0.88
-
-        # 8. 整理並返回結果
-        scores = {k: round(v, 4) for k, v in scores.items()}
+        # 5. 準備返回結果
         scores['overall'] = round(final_score, 4)
-        
-        return scores
+        return {k: round(v, 4) for k, v in scores.items()}
 
     except Exception as e:
         print(f"Error details: {str(e)}")
-        print(f"breed_info: {breed_info}")
         return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']}