Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -1316,108 +1316,74 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
             'noise': calculate_noise_score(breed_info.get('Breed', ''), user_prefs.noise_tolerance)
         }
 
-        # 2. 優化權重配置 - 根據使用者情況動態調整權重
-        def get_adjusted_weights(user_prefs, scores):
-            """
-            根據使用者條件動態調整各項評分的權重。
-            確保權重調整合理，避免過度偏移。
-            """
-            weights = {
-                'space': 0.28,      
-                'exercise': 0.18,   
-                'grooming': 0.12,   
-                'experience': 0.22, 
-                'health': 0.12,     
-                'noise': 0.08      
-            }
-            
-            # 公寓住戶需要更注重空間和噪音
-            if user_prefs.living_space == 'apartment':
-                if scores['space'] < 0.6:  # 空間評分不理想時更重視
-                    weights['space'] *= 1.25
-                    weights['noise'] *= 1.15
-                
-            # 新手飼主需要更注重經驗要求
-            if user_prefs.experience_level == 'beginner':
-                if scores['experience'] < 0.5:  # 經驗需求較高時更重視
-                    weights['experience'] *= 1.3
-            
-            # 有孩童時的特殊考量
-            if user_prefs.has_children:
-                child_age_weights = {
-                    'toddler': {'experience': 1.3, 'health': 1.2, 'noise': 1.2},
-                    'school_age': {'experience': 1.2, 'health': 1.1, 'noise': 1.1},
-                    'teenager': {'experience': 1.1, 'health': 1.05, 'noise': 1.05}
-                }
-                
-                age_adjustments = child_age_weights.get(user_prefs.children_age, 
-                                                      child_age_weights['school_age'])
-                for key, mult in age_adjustments.items():
-                    weights[key] *= mult
-
-            # 重新正規化權重總和為1
-            total = sum(weights.values())
-            return {k: v/total for k, v in weights.items()}
+        # 2. 設定基礎權重
+        weights = {
+            'space': 0.28,      
+            'exercise': 0.18,   
+            'grooming': 0.12,   
+            'experience': 0.22, 
+            'health': 0.12,     
+            'noise': 0.08      
+        }
 
-        # 3. 套用調整後的權重
-        weights = get_adjusted_weights(user_prefs, scores)
+        # 3. 條件權重調整
+        if user_prefs.living_space == 'apartment':
+            if scores['space'] < 0.7:  # 空間不足時加重權重
+                weights['space'] *= 1.3
+                weights['noise'] *= 1.2
         
-        # 4. 計算初始加權分數
+        if user_prefs.experience_level == 'beginner':
+            if scores['experience'] < 0.6:  # 經驗要求高時加重權重
+                weights['experience'] *= 1.4
+
+        # 重新正規化權重
+        total = sum(weights.values())
+        weights = {k: v/total for k, v in weights.items()}
+
+        # 4. 計算加權分數
         weighted_score = sum(score * weights[category] for category, score in scores.items())
 
-        # 5. 改進的分數放大函數
-        def amplify_score(raw_score, scores, user_prefs):
+        # 5. 新的分數放大函數
+        def amplify_score(raw_score, scores):
             """
-            優化的分數放大函數，確保分數分布合理且有意義。
-            考慮原始分數的分布和關鍵條件的影響。
+            直接的分數轉換，保持分數差異性
             """
-            # 基礎轉換：將分數範圍從[0.1-1.0]映射到[0-1]
-            normalized = (raw_score - 0.1) / 0.9
-            
-            # 使用S型曲線轉換，使中間範圍的差異更明顯
-            transformed = 1 / (1 + math.exp(-6 * (normalized - 0.5)))
+            # 基礎分數調整：擴大差異
+            adjusted = (raw_score - 0.5) * 1.8
             
-            # 將分數映射回目標範圍[0.6-0.95]
-            score = 0.6 + transformed * 0.35
+            # 線性轉換到目標範圍
+            score = 0.7 + adjusted * 0.5
             
-            # 關鍵條件檢查和調整
-            critical_conditions = [
-                (scores['experience'] < 0.4 and user_prefs.has_children, 0.85),
-                (scores['noise'] < 0.3 and user_prefs.living_space == 'apartment', 0.85),
-                (scores['health'] < 0.3, 0.9),
-                (scores['space'] < 0.3 and user_prefs.living_space == 'apartment', 0.85)
-            ]
+            # 處理極端情況
+            if any(v < 0.4 for v in scores.values()):
+                score *= 0.85  # 有極低分項目時降低整體分數
             
-            # 應用關鍵條件調整
-            for condition, factor in critical_conditions:
-                if condition:
-                    score *= factor
-            
-            return round(max(0.6, min(0.95, score)), 4)
+            # 確保分數在合理範圍內
+            return max(0.55, min(0.95, score))
 
         # 6. 計算最終分數
-        final_score = amplify_score(weighted_score, scores, user_prefs)
+        final_score = amplify_score(weighted_score, scores)
         
-        # 7. 確保分數差異性
-        def adjust_final_scores(base_scores, final_score):
-            """
-            確保最終分數能反映品種間的實際差異。
-            """
-            scores = base_scores.copy()
-            scores['overall'] = final_score
-            
-            # 檢查是否有極端低分項目
-            min_score = min(v for k, v in base_scores.items())
-            if min_score < 0.4:  # 如果有特別低的分數
-                scores['overall'] *= 0.92  # 適度降低整體評分
-            
-            return scores
+        # 7. 品種特定調整
+        breed_name = breed_info.get('Breed', '')
+        temperament = breed_info.get('Temperament', '').lower()
+        
+        # 根據具體條件進行品種特定的調整
+        if user_prefs.living_space == 'apartment':
+            if breed_info['Size'] in ['Large', 'Giant']:
+                final_score *= 0.85
+            elif 'high energy' in temperament or 'very active' in temperament:
+                final_score *= 0.9
+                
+        if user_prefs.experience_level == 'beginner':
+            if any(trait in temperament for trait in ['dominant', 'stubborn', 'independent']):
+                final_score *= 0.88
 
-        # 8. 準備最終結果
-        final_scores = adjust_final_scores(scores, final_score)
+        # 8. 整理並返回結果
+        scores = {k: round(v, 4) for k, v in scores.items()}
+        scores['overall'] = round(final_score, 4)
         
-        # 四捨五入所有分數
-        return {k: round(v, 4) for k, v in final_scores.items()}
+        return scores
 
     except Exception as e:
         print(f"Error details: {str(e)}")