Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -1294,44 +1294,101 @@ 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:
 #     """
 #     重構的品種相容性評分系統
-#     目標：實現更大的分數差異和更高的頂部分數
+#     目標：實現更大的分數差異和更高的頂部分數，更精確的條件匹配
 #     """
 #     def evaluate_perfect_conditions():
-#         """評估完美條件匹配度"""
+#         """評估完美條件匹配度，允許部分匹配"""
 #         perfect_matches = {
-#             'size_match': False,
-#             'exercise_match': False,
-#             'experience_match': False,
+#             'size_match': 0,
+#             'exercise_match': 0,
+#             'experience_match': 0,
 #             'general_match': False
 #         }
         
-#         # 體型與空間匹配
+#         # 體型與空間匹配更細緻化
 #         if user_prefs.living_space == 'apartment':
-#             perfect_matches['size_match'] = breed_info['Size'] == 'Small'
+#             if breed_info['Size'] == 'Small':
+#                 perfect_matches['size_match'] = 1.0
+#             elif breed_info['Size'] == 'Medium':
+#                 perfect_matches['size_match'] = 0.5
+#             else:
+#                 perfect_matches['size_match'] = 0
+#         elif user_prefs.living_space == 'house_small':
+#             if breed_info['Size'] in ['Small', 'Medium']:
+#                 perfect_matches['size_match'] = 1.0
+#             elif breed_info['Size'] == 'Large':
+#                 perfect_matches['size_match'] = 0.6
+#             else:
+#                 perfect_matches['size_match'] = 0.3
 #         elif user_prefs.living_space == 'house_large':
-#             perfect_matches['size_match'] = breed_info['Size'] in ['Medium', 'Large']
+#             if breed_info['Size'] in ['Medium', 'Large']:
+#                 perfect_matches['size_match'] = 1.0
+#             elif breed_info['Size'] == 'Small':
+#                 perfect_matches['size_match'] = 0.7
+#             else:
+#                 perfect_matches['size_match'] = 0.8
             
-#         # 運動需求匹配
+#         # 運動需求匹配更精確
 #         exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
-#         if exercise_needs == 'VERY HIGH' and user_prefs.exercise_time >= 150:
-#             perfect_matches['exercise_match'] = True
-#         elif exercise_needs == 'LOW' and 30 <= user_prefs.exercise_time <= 90:
-#             perfect_matches['exercise_match'] = True
-#         elif 60 <= user_prefs.exercise_time <= 120:
-#             perfect_matches['exercise_match'] = True
-            
-#         # 經驗匹配
+#         exercise_time = user_prefs.exercise_time
+        
+#         if exercise_needs == 'VERY HIGH':
+#             if exercise_time >= 150:
+#                 perfect_matches['exercise_match'] = 1.0
+#             elif exercise_time >= 120:
+#                 perfect_matches['exercise_match'] = 0.7
+#             elif exercise_time >= 90:
+#                 perfect_matches['exercise_match'] = 0.4
+#             else:
+#                 perfect_matches['exercise_match'] = 0
+#         elif exercise_needs == 'HIGH':
+#             if 120 <= exercise_time <= 150:
+#                 perfect_matches['exercise_match'] = 1.0
+#             elif exercise_time >= 90:
+#                 perfect_matches['exercise_match'] = 0.8
+#             elif exercise_time >= 60:
+#                 perfect_matches['exercise_match'] = 0.5
+#             else:
+#                 perfect_matches['exercise_match'] = 0.2
+#         elif exercise_needs == 'MODERATE':
+#             if 60 <= exercise_time <= 120:
+#                 perfect_matches['exercise_match'] = 1.0
+#             elif exercise_time > 120:
+#                 perfect_matches['exercise_match'] = 0.8
+#             else:
+#                 perfect_matches['exercise_match'] = 0.6
+#         elif exercise_needs == 'LOW':
+#             if 30 <= exercise_time <= 90:
+#                 perfect_matches['exercise_match'] = 1.0
+#             elif exercise_time > 90:
+#                 perfect_matches['exercise_match'] = 0.7
+#             else:
+#                 perfect_matches['exercise_match'] = 0.5
+                
+#         # 經驗匹配更細緻
 #         care_level = breed_info.get('Care Level', 'MODERATE')
-#         if care_level == 'High' and user_prefs.experience_level == 'advanced':
-#             perfect_matches['experience_match'] = True
-#         elif care_level == 'Low' and user_prefs.experience_level == 'beginner':
-#             perfect_matches['experience_match'] = True
-#         elif user_prefs.experience_level == 'intermediate':
-#             perfect_matches['experience_match'] = True
+#         if care_level == 'High':
+#             if user_prefs.experience_level == 'advanced':
+#                 perfect_matches['experience_match'] = 1.0
+#             elif user_prefs.experience_level == 'intermediate':
+#                 perfect_matches['experience_match'] = 0.6
+#             else:
+#                 perfect_matches['experience_match'] = 0.2
+#         elif care_level == 'Moderate':
+#             if user_prefs.experience_level == 'advanced':
+#                 perfect_matches['experience_match'] = 0.9
+#             elif user_prefs.experience_level == 'intermediate':
+#                 perfect_matches['experience_match'] = 1.0
+#             else:
+#                 perfect_matches['experience_match'] = 0.7
+#         elif care_level == 'Low':
+#             if user_prefs.experience_level == 'beginner':
+#                 perfect_matches['experience_match'] = 1.0
+#             else:
+#                 perfect_matches['experience_match'] = 0.9
             
 #         # 一般條件匹配
 #         if all(score >= 0.85 for score in scores.values()):
@@ -1340,11 +1397,11 @@ def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -
 #         return perfect_matches
 
 #     def calculate_weights():
-#         """計算動態權重"""
+#         """計算更動態的權重"""
 #         base_weights = {
 #             'space': 0.20,
 #             'exercise': 0.20,
-#             'experience': 0.20,  
+#             'experience': 0.20,
 #             'grooming': 0.15,
 #             'health': 0.15,
 #             'noise': 0.10
@@ -1353,15 +1410,25 @@ def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -
 #         # 極端條件權重調整
 #         multipliers = {}
         
-#         # 經驗權重調整
+#         # 經驗權重更細緻的調整
 #         if user_prefs.experience_level == 'beginner':
-#             multipliers['experience'] = 3.0  # 新手經驗極其重要
+#             if breed_info.get('Care Level') == 'High':
+#                 multipliers['experience'] = 3.5
+#             else:
+#                 multipliers['experience'] = 3.0
 #         elif user_prefs.experience_level == 'advanced':
-#             multipliers['experience'] = 2.5  # 專家經驗很重要
-            
-#         # 運動需求權重調整
-#         if user_prefs.exercise_time > 150:
-#             multipliers['exercise'] = 3.0
+#             if breed_info.get('Care Level') == 'High':
+#                 multipliers['experience'] = 2.8
+#             else:
+#                 multipliers['experience'] = 2.5
+
+#         # 運動需求更細緻的調整
+#         exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
+#         if exercise_needs == 'VERY HIGH':
+#             if user_prefs.exercise_time < 90:
+#                 multipliers['exercise'] = 4.0
+#             elif user_prefs.exercise_time > 150:
+#                 multipliers['exercise'] = 3.0
 #         elif user_prefs.exercise_time < 30:
 #             multipliers['exercise'] = 3.5
             
@@ -1370,12 +1437,37 @@ def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -
 #             multipliers['space'] = 2.5
 #             multipliers['noise'] = 2.0
             
+#         # 噪音敏感度調整
+#         if user_prefs.noise_tolerance == 'low':
+#             multipliers['noise'] = multipliers.get('noise', 1.0) * 2.5
+            
 #         # 應用乘數
 #         for key, multiplier in multipliers.items():
 #             base_weights[key] *= multiplier
             
 #         return base_weights
 
+#     def apply_special_case_adjustments(score):
+#         """處理特殊情況"""
+#         # 新手不適合的特殊情況
+#         if user_prefs.experience_level == 'beginner':
+#             if (breed_info.get('Care Level') == 'High' and 
+#                 breed_info.get('Exercise Needs') == 'VERY HIGH'):
+#                 score *= 0.7
+                
+#         # 運動時間極端不匹配的情況
+#         exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
+#         if exercise_needs == 'VERY HIGH' and user_prefs.exercise_time < 60:
+#             score *= 0.6
+            
+#         # 噪音敏感度極端情況
+#         if (user_prefs.noise_tolerance == 'low' and 
+#             breed_info.get('Breed') in breed_noise_info and 
+#             breed_noise_info[breed_info['Breed']]['noise_level'].lower() == 'high'):
+#             score *= 0.7
+            
+#         return score
+
 #     # 評估完美匹配條件
 #     perfect_conditions = evaluate_perfect_conditions()
     
@@ -1389,129 +1481,162 @@ def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -
 #     # 計算基礎分數
 #     base_score = sum(scores[k] * normalized_weights[k] for k in scores.keys())
     
-#     # 完美匹配獎勵
+#     # 完美匹配獎勵更動態
 #     perfect_bonus = 1.0
-#     if perfect_conditions['size_match']:
-#         perfect_bonus += 0.2
-#     if perfect_conditions['exercise_match']:
-#         perfect_bonus += 0.2
-#     if perfect_conditions['experience_match']:
-#         perfect_bonus += 0.2
+#     perfect_bonus += 0.2 * perfect_conditions['size_match']
+#     perfect_bonus += 0.2 * perfect_conditions['exercise_match']
+#     perfect_bonus += 0.2 * perfect_conditions['experience_match']
 #     if perfect_conditions['general_match']:
 #         perfect_bonus += 0.2
         
 #     # 品種特性加成
-#     breed_bonus = calculate_breed_bonus(breed_info, user_prefs) * 1.5  # 增加品種特性影響
+#     breed_bonus = calculate_breed_bonus(breed_info, user_prefs) * 1.5
     
 #     # 計算最終分數
 #     final_score = (base_score * 0.7 + breed_bonus * 0.3) * perfect_bonus
     
+#     # 應用特殊情況調整
+#     final_score = apply_special_case_adjustments(final_score)
+    
 #     return min(1.0, final_score)
 
+
 def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreferences, breed_info: dict) -> float:
     """
-    重構的品種相容性評分系統
-    目標：實現更大的分數差異和更高的頂部分數，更精確的條件匹配
+    優化後的品種相容性評分系統
+    主要改進：
+    1. 更精確的條件匹配度評估
+    2. 更動態的權重分配
+    3. 更嚴格的特殊情況處理
     """
     def evaluate_perfect_conditions():
-        """評估完美條件匹配度，允許部分匹配"""
+        """評估完美條件匹配度，重點優化不同條件組合的評估邏輯"""
         perfect_matches = {
             'size_match': 0,
             'exercise_match': 0,
             'experience_match': 0,
-            'general_match': False
+            'living_condition_match': 0
         }
         
-        # 體型與空間匹配更細緻化
-        if user_prefs.living_space == 'apartment':
-            if breed_info['Size'] == 'Small':
-                perfect_matches['size_match'] = 1.0
-            elif breed_info['Size'] == 'Medium':
-                perfect_matches['size_match'] = 0.5
-            else:
-                perfect_matches['size_match'] = 0
-        elif user_prefs.living_space == 'house_small':
-            if breed_info['Size'] in ['Small', 'Medium']:
-                perfect_matches['size_match'] = 1.0
-            elif breed_info['Size'] == 'Large':
-                perfect_matches['size_match'] = 0.6
-            else:
-                perfect_matches['size_match'] = 0.3
-        elif user_prefs.living_space == 'house_large':
-            if breed_info['Size'] in ['Medium', 'Large']:
-                perfect_matches['size_match'] = 1.0
-            elif breed_info['Size'] == 'Small':
-                perfect_matches['size_match'] = 0.7
-            else:
-                perfect_matches['size_match'] = 0.8
-            
-        # 運動需求匹配更精確
+        # 體型與居住空間匹配評估
+        size_living_matrix = {
+            'apartment': {
+                'Small': 1.0,
+                'Medium': 0.4,
+                'Large': 0.1,
+                'Giant': 0.05
+            },
+            'house_small': {
+                'Small': 0.9,
+                'Medium': 1.0,
+                'Large': 0.5,
+                'Giant': 0.3
+            },
+            'house_large': {
+                'Small': 0.7,
+                'Medium': 0.9,
+                'Large': 1.0,
+                'Giant': 0.9
+            }
+        }
+        perfect_matches['size_match'] = size_living_matrix.get(user_prefs.living_space, {}).get(breed_info['Size'], 0.5)
+        
+        # 運動需求匹配評估
         exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
         exercise_time = user_prefs.exercise_time
+        exercise_type = user_prefs.exercise_type
         
-        if exercise_needs == 'VERY HIGH':
-            if exercise_time >= 150:
-                perfect_matches['exercise_match'] = 1.0
-            elif exercise_time >= 120:
-                perfect_matches['exercise_match'] = 0.7
-            elif exercise_time >= 90:
-                perfect_matches['exercise_match'] = 0.4
-            else:
-                perfect_matches['exercise_match'] = 0
-        elif exercise_needs == 'HIGH':
-            if 120 <= exercise_time <= 150:
-                perfect_matches['exercise_match'] = 1.0
-            elif exercise_time >= 90:
-                perfect_matches['exercise_match'] = 0.8
-            elif exercise_time >= 60:
-                perfect_matches['exercise_match'] = 0.5
-            else:
-                perfect_matches['exercise_match'] = 0.2
-        elif exercise_needs == 'MODERATE':
-            if 60 <= exercise_time <= 120:
-                perfect_matches['exercise_match'] = 1.0
-            elif exercise_time > 120:
-                perfect_matches['exercise_match'] = 0.8
-            else:
-                perfect_matches['exercise_match'] = 0.6
-        elif exercise_needs == 'LOW':
-            if 30 <= exercise_time <= 90:
-                perfect_matches['exercise_match'] = 1.0
-            elif exercise_time > 90:
-                perfect_matches['exercise_match'] = 0.7
+        # 建立運動時間範圍對照表
+        exercise_ranges = {
+            'VERY HIGH': {'ideal': (150, 180), 'acceptable': (120, 200)},
+            'HIGH': {'ideal': (120, 150), 'acceptable': (90, 180)},
+            'MODERATE': {'ideal': (60, 120), 'acceptable': (45, 150)},
+            'LOW': {'ideal': (30, 60), 'acceptable': (20, 90)}
+        }
+        
+        # 評估運動時間匹配度
+        breed_range = exercise_ranges.get(exercise_needs, exercise_ranges['MODERATE'])
+        if breed_range['ideal'][0] <= exercise_time <= breed_range['ideal'][1]:
+            time_match = 1.0
+        elif breed_range['acceptable'][0] <= exercise_time <= breed_range['acceptable'][1]:
+            time_match = 0.7
+        else:
+            time_match = 0.3
+            
+        # 運動類型匹配評估
+        exercise_type_matrix = {
+            'VERY HIGH': {
+                'light_walks': 0.2,
+                'moderate_activity': 0.5,
+                'active_training': 1.0
+            },
+            'HIGH': {
+                'light_walks': 0.3,
+                'moderate_activity': 0.8,
+                'active_training': 1.0
+            },
+            'MODERATE': {
+                'light_walks': 0.7,
+                'moderate_activity': 1.0,
+                'active_training': 0.8
+            },
+            'LOW': {
+                'light_walks': 1.0,
+                'moderate_activity': 0.7,
+                'active_training': 0.4
+            }
+        }
+        
+        type_match = exercise_type_matrix.get(exercise_needs, {}).get(exercise_type, 0.5)
+        perfect_matches['exercise_match'] = (time_match * 0.7) + (type_match * 0.3)
+        
+        # 經驗匹配度評估
+        care_level = breed_info.get('Care Level', 'MODERATE').upper()
+        experience_matrix = {
+            'HIGH': {
+                'beginner': 0.1,
+                'intermediate': 0.6,
+                'advanced': 1.0
+            },
+            'MODERATE': {
+                'beginner': 0.5,
+                'intermediate': 1.0,
+                'advanced': 0.9
+            },
+            'LOW': {
+                'beginner': 1.0,
+                'intermediate': 0.9,
+                'advanced': 0.8
+            }
+        }
+        perfect_matches['experience_match'] = experience_matrix.get(care_level, {}).get(user_prefs.experience_level, 0.5)
+        
+        # 生活條件整體匹配評估
+        living_factors = []
+        
+        # 院子可用性評估
+        if breed_info.get('Exercise Needs', 'MODERATE').upper() in ['HIGH', 'VERY HIGH']:
+            if user_prefs.yard_access == 'no_yard':
+                living_factors.append(0.3)
+            elif user_prefs.yard_access == 'shared_yard':
+                living_factors.append(0.7)
             else:
-                perfect_matches['exercise_match'] = 0.5
+                living_factors.append(1.0)
                 
-        # 經驗匹配更細緻
-        care_level = breed_info.get('Care Level', 'MODERATE')
-        if care_level == 'High':
-            if user_prefs.experience_level == 'advanced':
-                perfect_matches['experience_match'] = 1.0
-            elif user_prefs.experience_level == 'intermediate':
-                perfect_matches['experience_match'] = 0.6
-            else:
-                perfect_matches['experience_match'] = 0.2
-        elif care_level == 'Moderate':
-            if user_prefs.experience_level == 'advanced':
-                perfect_matches['experience_match'] = 0.9
-            elif user_prefs.experience_level == 'intermediate':
-                perfect_matches['experience_match'] = 1.0
-            else:
-                perfect_matches['experience_match'] = 0.7
-        elif care_level == 'Low':
-            if user_prefs.experience_level == 'beginner':
-                perfect_matches['experience_match'] = 1.0
-            else:
-                perfect_matches['experience_match'] = 0.9
-            
-        # 一般條件匹配
-        if all(score >= 0.85 for score in scores.values()):
-            perfect_matches['general_match'] = True
-            
+        # 時間可用性評估
+        time_availability_scores = {
+            'limited': 0.4,
+            'moderate': 0.7,
+            'flexible': 1.0
+        }
+        living_factors.append(time_availability_scores.get(user_prefs.time_availability, 0.7))
+        
+        perfect_matches['living_condition_match'] = sum(living_factors) / len(living_factors) if living_factors else 0.5
+        
         return perfect_matches
 
     def calculate_weights():
-        """計算更動態的權重"""
+        """計算動態權重，根據使用者條件調整各項評分的重要性"""
         base_weights = {
             'space': 0.20,
             'exercise': 0.20,
@@ -1521,22 +1646,16 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
             'noise': 0.10
         }
         
-        # 極端條件權重調整
         multipliers = {}
         
-        # 經驗權重更細緻的調整
-        if user_prefs.experience_level == 'beginner':
-            if breed_info.get('Care Level') == 'High':
-                multipliers['experience'] = 3.5
-            else:
-                multipliers['experience'] = 3.0
-        elif user_prefs.experience_level == 'advanced':
-            if breed_info.get('Care Level') == 'High':
-                multipliers['experience'] = 2.8
-            else:
-                multipliers['experience'] = 2.5
-
-        # 運動需求更細緻的調整
+        # 居住空間權重調整
+        if user_prefs.living_space == 'apartment':
+            multipliers['space'] = 3.0
+            multipliers['noise'] = 2.5
+            if breed_info['Size'] in ['Large', 'Giant']:
+                multipliers['space'] = 4.0
+                
+        # 運動需求權重調整
         exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
         if exercise_needs == 'VERY HIGH':
             if user_prefs.exercise_time < 90:
@@ -1546,40 +1665,62 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
         elif user_prefs.exercise_time < 30:
             multipliers['exercise'] = 3.5
             
-        # 空間限制權重調整
-        if user_prefs.living_space == 'apartment':
-            multipliers['space'] = 2.5
-            multipliers['noise'] = 2.0
-            
+        # 經驗需求權重調整
+        if user_prefs.experience_level == 'beginner':
+            if breed_info.get('Care Level', 'MODERATE').upper() == 'HIGH':
+                multipliers['experience'] = 4.0
+            else:
+                multipliers['experience'] = 3.0
+                
         # 噪音敏感度調整
         if user_prefs.noise_tolerance == 'low':
-            multipliers['noise'] = multipliers.get('noise', 1.0) * 2.5
+            multipliers['noise'] = multipliers.get('noise', 1.0) * 3.0
+            
+        # 有小孩的情況特別注重經驗需求
+        if user_prefs.has_children and user_prefs.children_age == 'toddler':
+            multipliers['experience'] = multipliers.get('experience', 1.0) * 2.0
             
-        # 應用乘數
+        # 應用權重調整
         for key, multiplier in multipliers.items():
             base_weights[key] *= multiplier
             
         return base_weights
 
     def apply_special_case_adjustments(score):
-        """處理特殊情況"""
-        # 新手不適合的特殊情況
+        """處理特殊情況，給予更嚴格的分數調整"""
+        # 極端不匹配情況的嚴格懲罰
         if user_prefs.experience_level == 'beginner':
-            if (breed_info.get('Care Level') == 'High' and 
-                breed_info.get('Exercise Needs') == 'VERY HIGH'):
-                score *= 0.7
-                
-        # 運動時間極端不匹配的情況
+            if breed_info.get('Care Level') == 'HIGH':
+                if breed_info.get('Exercise Needs') == 'VERY HIGH':
+                    score *= 0.4
+                else:
+                    score *= 0.6
+                    
+        # 運動需求極端不匹配
         exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
         if exercise_needs == 'VERY HIGH' and user_prefs.exercise_time < 60:
-            score *= 0.6
-            
-        # 噪音敏感度極端情況
-        if (user_prefs.noise_tolerance == 'low' and 
-            breed_info.get('Breed') in breed_noise_info and 
-            breed_noise_info[breed_info['Breed']]['noise_level'].lower() == 'high'):
-            score *= 0.7
+            score *= 0.4
+        elif exercise_needs == 'LOW' and user_prefs.exercise_time > 150:
+            score *= 0.5
             
+        # 居住空間極端不匹配
+        if user_prefs.living_space == 'apartment':
+            if breed_info['Size'] == 'Giant':
+                score *= 0.3
+            elif breed_info['Size'] == 'Large':
+                score *= 0.5
+                
+        # 噪音敏感度極端不匹配
+        if user_prefs.noise_tolerance == 'low':
+            if breed_info.get('Breed') in breed_noise_info:
+                if breed_noise_info[breed_info['Breed']]['noise_level'].lower() == 'high':
+                    score *= 0.4
+                    
+        # 時間限制的影響
+        if user_prefs.time_availability == 'limited':
+            if breed_info.get('Exercise Needs').upper() in ['HIGH', 'VERY HIGH']:
+                score *= 0.6
+                
         return score
 
     # 評估完美匹配條件
@@ -1595,19 +1736,18 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
     # 計算基礎分數
     base_score = sum(scores[k] * normalized_weights[k] for k in scores.keys())
     
-    # 完美匹配獎勵更動態
+    # 完美匹配獎勵計算
     perfect_bonus = 1.0
-    perfect_bonus += 0.2 * perfect_conditions['size_match']
-    perfect_bonus += 0.2 * perfect_conditions['exercise_match']
-    perfect_bonus += 0.2 * perfect_conditions['experience_match']
-    if perfect_conditions['general_match']:
-        perfect_bonus += 0.2
-        
+    perfect_bonus += 0.15 * perfect_conditions['size_match']
+    perfect_bonus += 0.15 * perfect_conditions['exercise_match']
+    perfect_bonus += 0.15 * perfect_conditions['experience_match']
+    perfect_bonus += 0.05 * perfect_conditions['living_condition_match']
+    
     # 品種特性加成
-    breed_bonus = calculate_breed_bonus(breed_info, user_prefs) * 1.5
+    breed_bonus = calculate_breed_bonus(breed_info, user_prefs) * 1.2
     
     # 計算最終分數
-    final_score = (base_score * 0.7 + breed_bonus * 0.3) * perfect_bonus
+    final_score = (base_score * 0.8 + breed_bonus * 0.2) * perfect_bonus
     
     # 應用特殊情況調整
     final_score = apply_special_case_adjustments(final_score)