Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -222,17 +222,26 @@ def calculate_family_safety_score(breed_info: dict, children_age: str) -> float:
     
     # 基礎安全分數（根據體型）
     base_safety_scores = {
-        "Small": 0.85,    
-        "Medium": 0.75,   
-        "Large": 0.65,    
-        "Giant": 0.55     
+        "Small": 0.80,     # 從 0.85 降至 0.80
+        "Medium": 0.65,    # 從 0.75 降至 0.65
+        "Large": 0.50,     # 從 0.65 降至 0.50
+        "Giant": 0.40      # 從 0.55 降至 0.40
     }
-    safety_score = base_safety_scores.get(size, 0.70)
+    safety_score = base_safety_scores.get(size, 0.60)
     
-    # 年齡相關的調整
+    # 加強年齡相關的調整力度
     age_factors = {
         'toddler': {
-            'base_modifier': -0.15,
+            'base_modifier': -0.25,  # 從 -0.15 降至 -0.25
+            'size_penalty': {
+                "Small": -0.10,      # 從 -0.05 降至 -0.10
+                "Medium": -0.20,     # 從 -0.10 降至 -0.20
+                "Large": -0.30,      # 從 -0.20 降至 -0.30
+                "Giant": -0.35       # 從 -0.25 降至 -0.35
+            }
+        },
+        'school_age': {
+            'base_modifier': -0.15,  # 從 -0.08 降至 -0.15
             'size_penalty': {
                 "Small": -0.05,
                 "Medium": -0.10,
@@ -240,53 +249,34 @@ def calculate_family_safety_score(breed_info: dict, children_age: str) -> float:
                 "Giant": -0.25
             }
         },
-        'school_age': {
-            'base_modifier': -0.08,
+        'teenager': {
+            'base_modifier': -0.08,  # 從 -0.05 降至 -0.08
             'size_penalty': {
                 "Small": -0.02,
                 "Medium": -0.05,
                 "Large": -0.10,
                 "Giant": -0.15
             }
-        },
-        'teenager': {
-            'base_modifier': -0.05,
-            'size_penalty': {
-                "Small": 0,
-                "Medium": -0.02,
-                "Large": -0.05,
-                "Giant": -0.08
-            }
         }
     }
     
-    # 應用年齡相關調整
-    age_adjustment = age_factors.get(children_age, age_factors['school_age'])
-    safety_score += age_adjustment['base_modifier']
-    safety_score += age_adjustment['size_penalty'].get(size, -0.10)
-    
-    # 正面特徵評估
-    positive_traits = {
-        'gentle': 0.10,
-        'patient': 0.10,
-        'good with children': 0.15,
-        'friendly': 0.08,
-        'calm': 0.08,
-        'tolerant': 0.08,
-        'playful': 0.05
-    }
-    
-    # 危險特徵評估（更嚴格的懲罰）
+    # 加強對危險特徵的評估
     dangerous_traits = {
-        'aggressive': -0.25,
-        'territorial': -0.20,
-        'protective': -0.15,
-        'nervous': -0.15,
-        'dominant': -0.15,
-        'strong-willed': -0.12,
-        'independent': -0.10,
-        'energetic': -0.08
+        'aggressive': -0.35,      # 從 -0.25 加重到 -0.35
+        'territorial': -0.30,     # 從 -0.20 加重到 -0.30
+        'protective': -0.25,      # 從 -0.15 加重到 -0.25
+        'nervous': -0.25,         # 從 -0.15 加重到 -0.25
+        'dominant': -0.20,        # 從 -0.15 加重到 -0.20
+        'strong-willed': -0.18,   # 從 -0.12 加重到 -0.18
+        'independent': -0.15,     # 從 -0.10 加重到 -0.15
+        'energetic': -0.12       # 從 -0.08 加重到 -0.12
     }
+
+    # 特殊風險評估加重
+    if 'history of' in breed_info.get('Description', '').lower():
+        safety_score -= 0.25      # 從 -0.15 加重到 -0.25
+    if 'requires experienced' in breed_info.get('Description', '').lower():
+        safety_score -= 0.20      # 從 -0.10 加重到 -0.20
     
     # 計算特徵分數
     for trait, bonus in positive_traits.items():
@@ -403,6 +393,113 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
             return base_score
             
 
+        # def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float:
+        #     """
+        #     計算使用者經驗與品種需求的匹配分數
+            
+        #     參數說明:
+        #     care_level: 品種的照顧難度 ("High", "Moderate", "Low")
+        #     user_experience: 使用者經驗等級 ("beginner", "intermediate", "advanced") 
+        #     temperament: 品種的性格特徵描述
+            
+        #     返回:
+        #     float: 0.2-1.0 之間的匹配分數
+        #     """
+        #     # 基礎分數矩陣 - 更大的分數差異來反映經驗重要性
+        #     base_scores = {
+        #         "High": {
+        #             "beginner": 0.12,     # 降低起始分，反映高難度品種對新手的挑戰
+        #             "intermediate": 0.65,  # 中級玩家可以應付，但仍有改善空間
+        #             "advanced": 1.0       # 資深者能完全勝任
+        #         },
+        #         "Moderate": {
+        #             "beginner": 0.35,    # 適中難度對新手來說仍具挑戰
+        #             "intermediate": 0.82, # 中級玩家有很好的勝任能力
+        #             "advanced": 1.0      # 資深者完全勝任
+        #         },
+        #         "Low": {
+        #             "beginner": 0.72,    # 低難度品種適合新手
+        #             "intermediate": 0.92, # 中級玩家幾乎完全勝任
+        #             "advanced": 1.0      # 資深者完全勝任
+        #         }
+        #     }
+            
+        #     # 取得基礎分數
+        #     score = base_scores.get(care_level, base_scores["Moderate"])[user_experience]
+            
+        #     # 性格特徵評估 - 根據經驗等級調整權重
+        #     temperament_lower = temperament.lower()
+        #     temperament_adjustments = 0.0
+            
+        #     if user_experience == "beginner":
+        #         # 新手不適合的特徵 - 更嚴格的懲罰
+        #         difficult_traits = {
+        #             'stubborn': -0.15,      # 加重固執的懲罰
+        #             'independent': -0.12,    # 加重獨立性的懲罰
+        #             'dominant': -0.12,       # 加重支配性的懲罰
+        #             'strong-willed': -0.10,  # 加重強勢的懲罰
+        #             'protective': -0.08,     # 加重保護性的懲罰
+        #             'aloof': -0.08,         # 加重冷漠的懲罰
+        #             'energetic': -0.06      # 輕微懲罰高能量
+        #         }
+                
+        #         # 新手友善的特徵 - 提供更多獎勵
+        #         easy_traits = {
+        #             'gentle': 0.08,          # 增加溫和的獎勵
+        #             'friendly': 0.08,        # 增加友善的獎勵
+        #             'eager to please': 0.08, # 增加順從的獎勵
+        #             'patient': 0.06,         # 獎勵耐心
+        #             'adaptable': 0.06,       # 獎勵適應性
+        #             'calm': 0.05            # 獎勵冷靜
+        #         }
+                
+        #         # 計算特徵調整
+        #         for trait, penalty in difficult_traits.items():
+        #             if trait in temperament_lower:
+        #                 temperament_adjustments += penalty * 1.2  # 加重新手的懲罰
+                
+        #         for trait, bonus in easy_traits.items():
+        #             if trait in temperament_lower:
+        #                 temperament_adjustments += bonus
+                        
+        #         # 品種特殊調整
+        #         if any(term in temperament_lower for term in ['terrier', 'working', 'guard']):
+        #             temperament_adjustments -= 0.12  # 加重對特定類型品種的懲罰
+                    
+        #     elif user_experience == "intermediate":
+        #         # 中級玩家的調整更加平衡
+        #         moderate_traits = {
+        #             'intelligent': 0.05,     # 獎勵聰明
+        #             'athletic': 0.04,        # 獎勵運動能力
+        #             'versatile': 0.04,       # 獎勵多功能性
+        #             'stubborn': -0.06,       # 輕微懲罰固執
+        #             'independent': -0.05,    # 輕微懲罰獨立性
+        #             'protective': -0.04      # 輕微懲罰保護性
+        #         }
+                
+        #         for trait, adjustment in moderate_traits.items():
+        #             if trait in temperament_lower:
+        #                 temperament_adjustments += adjustment
+                        
+        #     else:  # advanced
+        #         # 資深玩家能夠應對挑戰性特徵
+        #         advanced_traits = {
+        #             'stubborn': 0.04,        # 反轉為優勢
+        #             'independent': 0.04,      # 反轉為優勢
+        #             'intelligent': 0.05,      # 獎勵聰明
+        #             'protective': 0.04,       # 獎勵保護性
+        #             'strong-willed': 0.03    # 獎勵強勢
+        #         }
+                
+        #         for trait, bonus in advanced_traits.items():
+        #             if trait in temperament_lower:
+        #                 temperament_adjustments += bonus
+            
+        #     # 確保最終分數在合理範圍內
+        #     final_score = max(0.2, min(1.0, score + temperament_adjustments))
+        #     return final_score
+
+
         def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float:
             """
             計算使用者經驗與品種需求的匹配分數
@@ -494,16 +591,26 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
             else:  # advanced
                 # 資深玩家能夠應對挑戰性特徵
                 advanced_traits = {
-                    'stubborn': 0.04,        # 反轉為優勢
-                    'independent': 0.04,      # 反轉為優勢
-                    'intelligent': 0.05,      # 獎勵聰明
-                    'protective': 0.04,       # 獎勵保護性
-                    'strong-willed': 0.03    # 獎勵強勢
+                    'stubborn': 0.02,        # 降低加分幅度
+                    'independent': 0.02,     
+                    'intelligent': 0.05,     
+                    'protective': 0.02,      
+                    'strong-willed': 0.02,   
+                    'aggressive': -0.04,     # 新增負面特徵
+                    'nervous': -0.03,        
+                    'dominant': -0.02        
                 }
                 
                 for trait, bonus in advanced_traits.items():
                     if trait in temperament_lower:
-                        temperament_adjustments += bonus
+                        # 加入條件評估
+                        if bonus > 0:  # 正面特徵
+                            # 限制正面特徵的累積加分不超過0.15
+                            if temperament_adjustments + bonus <= 0.15:
+                                temperament_adjustments += bonus
+                        else:  # 負面特徵
+                            # 負面特徵一定要計算
+                            temperament_adjustments += bonus
             
             # 確保最終分數在合理範圍內
             final_score = max(0.2, min(1.0, score + temperament_adjustments))
@@ -627,6 +734,75 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
             
             return max(0.2, min(1.0, final_score))
 
+        # # 計算所有基礎分數
+        # scores = {
+        #     'space': calculate_space_score(
+        #         breed_info['Size'], 
+        #         user_prefs.living_space,
+        #         user_prefs.space_for_play,
+        #         breed_info.get('Exercise Needs', 'Moderate')
+        #     ),
+        #     'exercise': calculate_exercise_score(
+        #         breed_info.get('Exercise Needs', 'Moderate'),
+        #         user_prefs.exercise_time
+        #     ),
+        #     'grooming': calculate_grooming_score(
+        #         breed_info.get('Grooming Needs', 'Moderate'),
+        #         user_prefs.grooming_commitment.lower(),
+        #         breed_info['Size']
+        #     ),
+        #     'experience': calculate_experience_score(
+        #         breed_info.get('Care Level', 'Moderate'),
+        #         user_prefs.experience_level,
+        #         breed_info.get('Temperament', '')
+        #     ),
+        #     'health': calculate_health_score(breed_info.get('Breed', '')),
+        #     'noise': calculate_noise_score(breed_info.get('Breed', ''), user_prefs.noise_tolerance)
+        # }
+
+
+        # # 優化權重配置
+        # weights = {
+        #     'space': 0.28,      
+        #     'exercise': 0.18,   
+        #     'grooming': 0.12,   
+        #     'experience': 0.22, 
+        #     'health': 0.12,     
+        #     'noise': 0.08      
+        # }
+
+        # # 計算加權總分
+        # weighted_score = sum(score * weights[category] for category, score in scores.items())
+
+        # def amplify_score(score):
+        #     """
+        #     優化分數放大函數，確保分數範圍合理且結果一致
+        #     """
+        #     # 基礎調整
+        #     adjusted = (score - 0.35) * 1.8
+            
+        #     # 使用 3.2 次方使曲線更平滑
+        #     amplified = pow(adjusted, 3.2) / 5.8 + score
+            
+        #     # 特別處理高分區間，確保不超過95%
+        #     if amplified > 0.90:
+        #         # 壓縮高分區間，確保最高到95%
+        #         amplified = 0.90 + (amplified - 0.90) * 0.5
+            
+        #     # 確保最終分數在合理範圍內（0.55-0.95）
+        #     final_score = max(0.55, min(0.95, amplified))
+            
+        #     # 四捨五入到小數點後第三位
+        #     return round(final_score, 3)
+        
+        # final_score = amplify_score(weighted_score)
+
+        # # 四捨五入所有分數
+        # scores = {k: round(v, 4) for k, v in scores.items()}
+        # scores['overall'] = round(final_score, 4)
+
+        # return scores
+
         # 計算所有基礎分數
         scores = {
             'space': calculate_space_score(
@@ -652,48 +828,65 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
             'health': calculate_health_score(breed_info.get('Breed', '')),
             'noise': calculate_noise_score(breed_info.get('Breed', ''), user_prefs.noise_tolerance)
         }
-
-
-        # 優化權重配置
-        weights = {
-            'space': 0.28,      
-            'exercise': 0.18,   
-            'grooming': 0.12,   
-            'experience': 0.22, 
-            'health': 0.12,     
-            'noise': 0.08      
+        
+        # 如果有孩童，計算家庭安全分數
+        if user_prefs.has_children:
+            scores['family_safety'] = calculate_family_safety_score(breed_info, user_prefs.children_age)
+        
+        # 計算品種額外加分
+        breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
+        
+        # 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
         }
-
+        
+        # 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()}
+        else:
+            weights = base_weights
+        
         # 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
+        
         def amplify_score(score):
             """
-            優化分數放大函數，確保分數範圍合理且結果一致
+            優化分數放大函數，加入更強的差異化效果
             """
-            # 基礎調整
-            adjusted = (score - 0.35) * 1.8
+            # 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
             
-            # 使用 3.2 次方使曲線更平滑
-            amplified = pow(adjusted, 3.2) / 5.8 + score
+            # 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
             
-            # 特別處理高分區間，確保不超過95%
-            if amplified > 0.90:
-                # 壓縮高分區間，確保最高到95%
-                amplified = 0.90 + (amplified - 0.90) * 0.5
+            # 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
             
-            # 確保最終分數在合理範圍內（0.55-0.95）
-            final_score = max(0.55, min(0.95, amplified))
+            # 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
             
-            # 四捨五入到小數點後第三位
             return round(final_score, 3)
         
-        final_score = amplify_score(weighted_score)
-
-        # 四捨五入所有分數
+        # 計算最終分數
+        final_score = amplify_score(adjusted_score)
+        
+        # 四捨五入所有分數並回傳
         scores = {k: round(v, 4) for k, v in scores.items()}
         scores['overall'] = round(final_score, 4)
-
         return scores
 
     except Exception as e: