Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -1518,22 +1518,22 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
     3. 更嚴格的不適配懲罰
     4. 非線性分數調整
     """
-    # 1. 關鍵不適配參數檢查 - 加強懲罰機制
+    # 關鍵不適配參數檢查 - 加強懲罰機制
     critical_params = {
         'space': {
-            'threshold': 0.35,  # 提高門檻
+            'threshold': 0.35,
             'conditions': lambda p: True,
-            'penalty': 0.25     # 更嚴重的懲罰
+            'penalty': 0.3
         },
         'noise': {
             'threshold': 0.35,
             'conditions': lambda p: p.living_space == 'apartment',
-            'penalty': 0.3
+            'penalty': 0.35
         },
         'experience': {
             'threshold': 0.35,
             'conditions': lambda p: p.experience_level == 'beginner',
-            'penalty': 0.3
+            'penalty': 0.35
         }
     }
 
@@ -1542,66 +1542,95 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
         if scores[param] < config['threshold'] and config['conditions'](user_prefs):
             return config['penalty']
 
-    # 2. 基礎權重設定 - 提高關鍵參數權重
+    # 基礎權重設定
     base_weights = {
-        'space': 0.40,      # 提高空間權重
-        'exercise': 0.35,   # 提高運動需求權重
-        'experience': 0.25, # 提高經驗需求權重
+        'space': 0.35,
+        'exercise': 0.30,
+        'experience': 0.20,
         'grooming': 0.15,
         'health': 0.10,
         'noise': 0.10
     }
 
-    # 3. 動態權重調整 - 加強調整幅度
+    # 計算各項指標的調整後分數
+    adjusted_scores = {}
+    for param, score in scores.items():
+        # 基礎分數調整：優秀匹配得到更高分數
+        if score > 0.8:
+            adjusted_scores[param] = min(1.0, score * 1.3)  # 優秀匹配額外加分
+        elif score < 0.4:
+            adjusted_scores[param] = score * 0.7  # 較差匹配更大懲罰
+        else:
+            adjusted_scores[param] = score
+
+    # 權重動態調整
     adjusted_weights = {}
     for param, weight in base_weights.items():
         multiplier = 1.0
         
-        # 空間相關調整
+        # 空間適配性調整
         if param == 'space':
             if user_prefs.living_space == 'apartment':
-                multiplier *= 1.4  # 加大調整幅度
-            elif breed_info['Size'] in ['Large', 'Giant']:
-                multiplier *= 1.5
-        
+                if breed_info['Size'] in ['Large', 'Giant']:
+                    multiplier *= 0.5  # 大型犬在公寓極不適合
+                elif breed_info['Size'] == 'Small':
+                    multiplier *= 1.4  # 小型犬在公寓更合適
+            elif user_prefs.living_space == 'house_large':
+                if breed_info['Size'] in ['Large', 'Giant']:
+                    multiplier *= 1.3  # 大型犬在大房子更合適
+
         # 運動需求調整
         elif param == 'exercise':
-            if user_prefs.exercise_time > 150:
-                multiplier *= 1.6  # 加大高運動需求的影響
-            elif user_prefs.exercise_time < 60:
-                multiplier *= 1.4
-        
+            exercise_needs = breed_info.get('Exercise Needs', 'Moderate').upper()
+            if exercise_needs == 'VERY HIGH':
+                if user_prefs.exercise_time > 150:
+                    multiplier *= 1.4  # 高運動量需求與高運動時間匹配
+                elif user_prefs.exercise_time < 60:
+                    multiplier *= 0.6  # 運動時間嚴重不足
+            elif exercise_needs == 'LOW' and user_prefs.exercise_time > 120:
+                multiplier *= 0.8  # 過度運動對低運動需求品種不利
+
         # 經驗需求調整
         elif param == 'experience':
-            if user_prefs.experience_level == 'beginner':
-                multiplier *= 1.5
-            elif breed_info.get('Care Level') == 'High':
-                multiplier *= 1.4
-        
+            if breed_info.get('Care Level') == 'High':
+                if user_prefs.experience_level == 'beginner':
+                    multiplier *= 0.6
+                elif user_prefs.experience_level == 'advanced':
+                    multiplier *= 1.3
+
         adjusted_weights[param] = weight * multiplier
 
     # 重新正規化權重
     total_weight = sum(adjusted_weights.values())
     normalized_weights = {k: v/total_weight for k, v in adjusted_weights.items()}
 
-    # 4. 分數計算 - 加強非線性調整
-    base_score = 0
+    # 計算加權分數
+    weighted_scores = {}
     for param, weight in normalized_weights.items():
-        score = scores[param]
-        
-        # 非線性分數調整
-        if score > 0.8:
-            score = min(1.0, score * 1.3)  # 加大高分獎勵
-        elif score < 0.5:  # 降低門檻，加大懲罰
-            score = score * 0.7  # 加重低分懲罰
-        
-        base_score += score * weight
+        weighted_scores[param] = adjusted_scores[param] * weight
+
+    # 分開計算主要參數和次要參數
+    primary_params = {'space', 'exercise', 'experience'}
+    primary_score = sum(weighted_scores[p] for p in primary_params) / sum(normalized_weights[p] for p in primary_params)
+    secondary_score = sum(weighted_scores[p] for p in weighted_scores if p not in primary_params) / \
+                     sum(normalized_weights[p] for p in normalized_weights if p not in primary_params)
+
+    # 綜合評分，主要參數占更大權重
+    base_score = (primary_score * 0.7) + (secondary_score * 0.3)
+
+    # 計算完美匹配加成
+    perfect_match_bonus = 0.0
+    if all(adjusted_scores[p] > 0.8 for p in primary_params):
+        perfect_match_bonus = 0.1
+
+    # 計算最終分數
+    final_score = base_score + perfect_match_bonus
 
-    # 5. 特性加成整合
+    # 品種特性加成
     breed_bonus = calculate_breed_bonus(breed_info, user_prefs)
     
-    # 6. 最終分數計算 - 調整加成比例
-    final_score = (base_score * 0.80) + (breed_bonus * 0.20)
+    # 整合最終分數
+    final_score = (final_score * 0.8) + (breed_bonus * 0.2)
 
     return final_score
 
@@ -1621,23 +1650,23 @@ def amplify_score_extreme(score: float) -> float:
     ranges = {
         'poor': {
             'range': (0.0, 0.3),
-            'out_min': 60.0,
-            'out_max': 70.0
+            'out_min': 0.6,
+            'out_max': 0.7
         },
         'mediocre': {
             'range': (0.3, 0.6),
-            'out_min': 70.0,
-            'out_max': 80.0
+            'out_min': 0.7,
+            'out_max': 0.8
         },
         'good': {
             'range': (0.6, 0.8),
-            'out_min': 80.0,
-            'out_max': 90.0
+            'out_min': 0.8,
+            'out_max': 0.9
         },
         'excellent': {
             'range': (0.8, 1.0),
-            'out_min': 90.0,
-            'out_max': 95.0
+            'out_min': 0.9,
+            'out_max': 0.95
         }
     }
     
@@ -1645,7 +1674,6 @@ def amplify_score_extreme(score: float) -> float:
     for config in ranges.values():
         range_min, range_max = config['range']
         if range_min <= score <= range_max:
-            # 計算在當前區間的相對位置（0-1）
             position = (score - range_min) / (range_max - range_min)
             
             # 線性映射到目標範圍
@@ -1655,4 +1683,4 @@ def amplify_score_extreme(score: float) -> float:
             return round(result, 1)
     
     # 處理超出範圍的情況
-    return 60.0 if score < 0.0 else 95.0
+    return 0.6 if score < 0.0 else 0.95