Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -1460,6 +1460,7 @@ def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -
         
     return min(0.2, adaptability_score)
 
+
 def calculate_final_weighted_score(
     scores: dict,
     user_prefs: UserPreferences,
@@ -1467,81 +1468,81 @@ def calculate_final_weighted_score(
     adaptability_bonus: float
 ) -> float:
     """
-    計算最終加權分數，強化條件變化的影響力
+    優化的最終分數計算系統
     """
-    # 基礎權重設定 - 更極端化
+    # 1. 基礎分數計算 - 使用更極端的權重
     base_weights = {
-        'space': 0.30,        # 提高空間權重
-        'exercise': 0.25,     # 提高運動權重
+        'space': 0.35,        # 大幅提高空間權重
+        'exercise': 0.25,
         'grooming': 0.15,
         'experience': 0.15,
-        'health': 0.10,
-        'noise': 0.05
+        'health': 0.07,
+        'noise': 0.03        # 降低噪音的基礎權重
     }
-
-    # 條件特殊化加權
-    special_conditions = 0.0
-    
-    # 1. 極端條件加權
-    if user_prefs.noise_tolerance == 'low':
-        if scores['noise'] < 0.7:  # 對低噪音容忍度更嚴格
-            special_conditions -= 0.15
-    
-    if user_prefs.grooming_commitment == 'high':
-        if breed_info.get('Grooming Needs', '').upper() == 'HIGH':
-            special_conditions += 0.12  # 獎勵高美容需求品種
-    
-    # 2. 專業度差異化
-    if user_prefs.experience_level == 'advanced':
-        if breed_info.get('Care Level', '').upper() == 'HIGH':
-            special_conditions += 0.15  # 資深者配高難度品種加分
-        elif breed_info.get('Care Level', '').upper() == 'LOW':
-            special_conditions -= 0.10  # 資深者配低難度品種扣分
-    
-    # 3. 居住環境極端匹配
-    if user_prefs.living_space == 'apartment':
-        if breed_info.get('Size', '') == 'Large':
-            special_conditions -= 0.20  # 大型犬在公寓嚴重扣分
-        elif breed_info.get('Size', '') == 'Small':
-            special_conditions += 0.10  # 小型犬在公寓額外加分
-    
-    # 4. 品種特色加權
-    breed_traits = breed_info.get('Temperament', '').lower()
-    description = breed_info.get('Description', '').lower()
     
-    if user_prefs.exercise_time > 120:  # 高運動量使用者
-        if 'athletic' in breed_traits or 'energetic' in breed_traits:
-            special_conditions += 0.15
-    elif user_prefs.exercise_time < 45:  # 低運動量使用者
-        if 'calm' in breed_traits or 'lazy' in breed_traits:
-            special_conditions += 0.12
+    # 2. 條件特殊化評分
+    condition_bonus = calculate_condition_bonus(breed_info, user_prefs, scores)
     
-    # 重新計算加權總分
+    # 3. 計算加權基礎分數
     weighted_base = sum(score * base_weights[category] for category, score in scores.items())
     
-    # 品種特性加成
+    # 4. 品種特性加成
     breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
     
-    # 最終分數計算 - 加大特殊條件的影響
-    final_score = (weighted_base * 0.65) + (breed_bonus * 0.15) + (adaptability_bonus * 0.10) + (special_conditions * 0.10)
-    
-    # 分數放大，使差異更明顯
-    if final_score > 0.8:
-        final_score = 0.8 + (final_score - 0.8) * 1.5
-    elif final_score < 0.6:
-        final_score = 0.6 - (0.6 - final_score) * 1.5
+    # 5. 最終分數計算 - 改變權重分配
+    raw_score = (weighted_base * 0.60) + (breed_bonus * 0.25) + 
+                (adaptability_bonus * 0.10) + (condition_bonus * 0.05)
     
-    return round(min(0.95, max(0.45, final_score)), 4)
+    # 6. 分數轉換 - 使用更激進的轉換函數
+    return amplify_score_extreme(raw_score)
+
+def calculate_condition_bonus(breed_info: dict, user_prefs: UserPreferences, scores: dict) -> float:
+    """
+    計算條件特殊化加分，強化極端條件的影響
+    """
+    bonus = 0.0
     
+    # 居住空間極端匹配
+    if user_prefs.living_space == 'apartment':
+        if breed_info['Size'] == 'Small' and scores['noise'] > 0.8:
+            bonus += 0.25  # 顯著獎勵適合公寓的小型安靜犬種
+        elif breed_info['Size'] in ['Large', 'Giant']:
+            bonus -= 0.35  # 嚴重懲罰大型犬
+    
+    # 美容需求匹配
+    if user_prefs.grooming_commitment == 'low':
+        if breed_info.get('Grooming Needs') == 'HIGH':
+            bonus -= 0.30  # 嚴重懲罰高美容需求
+    
+    # 經驗等級匹配
+    if user_prefs.experience_level == 'beginner':
+        if breed_info.get('Care Level') == 'HIGH':
+            bonus -= 0.25
+    elif user_prefs.experience_level == 'advanced':
+        if breed_info.get('Care Level') == 'LOW':
+            bonus -= 0.20
+    
+    return bonus
 
-def amplify_score_range(score: float) -> float:
-    """擴大分數範圍，使差異更明顯"""
-    min_score = 0.40    # 降低最小分數
-    max_score = 0.98    # 提高最高分數
+def amplify_score_extreme(score: float) -> float:
+    """
+    更激進��分數轉換函數
+    """
+    # 基礎範圍調整
+    base_min = 0.65  # 提高最低分
+    base_max = 0.98  # 提高最高分
     
-    # 使用更激進的非線性轉換
+    # 非線性轉換
     normalized = (score - 0.5) / 0.5
-    amplified = math.pow(abs(normalized), 1.3) * math.copysign(1, normalized)
+    amplified = math.pow(abs(normalized), 1.2) * math.copysign(1, normalized)
+    
+    # S型曲線轉換
+    sigmoid = 1 / (1 + math.exp(-amplified * 3))
+    
+    # 映射到目標範圍
+    final = base_min + (base_max - base_min) * sigmoid
+    
+    # 加入隨機微擾動以打破相似分數
+    noise = random.uniform(-0.002, 0.002)
     
-    final = min_score + (max_score - min_score) * ((amplified + 1) / 2)
-    return max(min_score, min(max_score, final))
+    return round(min(base_max, max(base_min, final + noise)), 4)