Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -293,65 +293,171 @@ 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:
+        #     """飼養經驗需求計算"""
+        #     # 初始化 temperament_adjustments，確保所有路徑都有值
+        #     temperament_adjustments = 0.0
+            
+        #     # 降低初學者的基礎分數
+        #     base_scores = {
+        #         "High": {"beginner": 0.15, "intermediate": 0.70, "advanced": 1.0},     
+        #         "Moderate": {"beginner": 0.40, "intermediate": 0.85, "advanced": 1.0}, 
+        #         "Low": {"beginner": 0.75, "intermediate": 0.95, "advanced": 1.0}       
+        #     }
+            
+        #     score = base_scores.get(care_level, base_scores["Moderate"])[user_experience]
+            
+        #     # 擴展性格特徵評估
+        #     temperament_lower = temperament.lower()
+            
+        #     if user_experience == "beginner":
+        #         # 增加更多特徵評估
+        #         difficult_traits = {
+        #             'stubborn': -0.12,     
+        #             'independent': -0.10,
+        #             'dominant': -0.10,
+        #             'strong-willed': -0.08,
+        #             'protective': -0.06,     
+        #             'energetic': -0.05      
+        #         }
+                
+        #         easy_traits = {
+        #             'gentle': 0.06,         
+        #             'friendly': 0.06,
+        #             'eager to please': 0.06,
+        #             'patient': 0.05,
+        #             'adaptable': 0.05,      
+        #             'calm': 0.04           
+        #         }
+                
+        #         # 更精確的特徵影響計算
+        #         temperament_adjustments = sum(value for trait, value in easy_traits.items() if trait in temperament_lower)
+        #         temperament_adjustments += sum(value for trait, value in difficult_traits.items() if trait in temperament_lower)
+                
+        #         # 品種特定調整
+        #         if "terrier" in breed_info['Description'].lower():
+        #             temperament_adjustments -= 0.1  # 梗類犬對新手不友善
+            
+        #     elif user_experience == "intermediate":
+        #         # 中級飼主的調整較溫和
+        #         if any(trait in temperament_lower for trait in ['gentle', 'friendly', 'patient']):
+        #             temperament_adjustments += 0.03
+        #         if any(trait in temperament_lower for trait in ['stubborn', 'independent']):
+        #             temperament_adjustments -= 0.02
+            
+        #     else:  # advanced
+        #         # 資深飼主能處理更具挑戰性的犬種
+        #         if any(trait in temperament_lower for trait in ['stubborn', 'independent', 'dominant']):
+        #             temperament_adjustments += 0.02  # 反而可能是優點
+        #         if any(trait in temperament_lower for trait in ['protective', 'energetic']):
+        #             temperament_adjustments += 0.03
+            
+        #     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:
-            """飼養經驗需求計算"""
-            # 初始化 temperament_adjustments，確保所有路徑都有值
-            temperament_adjustments = 0.0
+            """
+            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
             
-            # 降低初學者的基礎分數
+            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": 0.15, "intermediate": 0.70, "advanced": 1.0},     
-                "Moderate": {"beginner": 0.40, "intermediate": 0.85, "advanced": 1.0}, 
-                "Low": {"beginner": 0.75, "intermediate": 0.95, "advanced": 1.0}       
+                "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]
             
-            # 擴展性格特徵評估
+            # 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":
-                # 增加更多特徵評估
+                # 新手不適合的特徵 - 更嚴格的懲罰
                 difficult_traits = {
-                    'stubborn': -0.12,     
-                    'independent': -0.10,
-                    'dominant': -0.10,
-                    'strong-willed': -0.08,
-                    'protective': -0.06,     
-                    'energetic': -0.05      
+                    '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': 0.06,         
-                    'friendly': 0.06,
-                    'eager to please': 0.06,
-                    'patient': 0.05,
-                    'adaptable': 0.05,      
-                    'calm': 0.04           
+                    '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
                 }
                 
-                # 更精確的特徵影響計算
-                temperament_adjustments = sum(value for trait, value in easy_traits.items() if trait in temperament_lower)
-                temperament_adjustments += sum(value for trait, value in difficult_traits.items() if trait in temperament_lower)
+                # 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
                 
-                # 品種特定調整
-                if "terrier" in breed_info['Description'].lower():
-                    temperament_adjustments -= 0.1  # 梗類犬對新手不友善
-            
+                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":
-                # 中級飼主的調整較溫和
-                if any(trait in temperament_lower for trait in ['gentle', 'friendly', 'patient']):
-                    temperament_adjustments += 0.03
-                if any(trait in temperament_lower for trait in ['stubborn', 'independent']):
-                    temperament_adjustments -= 0.02
-            
+                # 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
-                # 資深飼主能處理更具挑戰性的犬種
-                if any(trait in temperament_lower for trait in ['stubborn', 'independent', 'dominant']):
-                    temperament_adjustments += 0.02  # 反而可能是優點
-                if any(trait in temperament_lower for trait in ['protective', 'energetic']):
-                    temperament_adjustments += 0.03
+                # 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
 
@@ -511,11 +617,31 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
         # 計算加權總分
         weighted_score = sum(score * weights[category] for category, score in scores.items())
         
-        # 擴大分數差異
+        # # 擴大分數差異
+        # def amplify_score(score):
+        #     # 使用指數函數擴大差異
+        #     amplified = pow((score - 0.5) * 2, 3) / 8 + score
+        #     return max(0.65, min(0.95, amplified))  # 限制在65%-95%範圍內
+
         def amplify_score(score):
-            # 使用指數函數擴大差異
-            amplified = pow((score - 0.5) * 2, 3) / 8 + score
-            return max(0.65, min(0.95, amplified))  # 限制在65%-95%範圍內
+            """
+            將原始分數放大，創造更明顯的差異
+            
+            參數:
+            score: 原始分數 (0.0-1.0)
+            
+            返回:
+            float: 放大後的分數 (0.60-0.95)
+            """
+            # 基礎分數調整 - 將範圍集中到更有意義的區間
+            adjusted = (score - 0.4) * 1.67
+            
+            # 使用更強的指數關係來放大差異
+            # 指數3可以在高分區間產生更明顯的差異
+            amplified = pow(adjusted, 3) / 8 + score
+            
+            # 確保分數在60%-95%之間，並保持合理的分布
+            return max(0.60, min(0.95, amplified))
         
         final_score = amplify_score(weighted_score)