Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -2133,38 +2133,154 @@ def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreference
         final_score = score * severity_multiplier
         return max(0.2, min(1.0, final_score))
 
-    # 評估完美匹配條件
-    perfect_conditions = evaluate_perfect_conditions()
-    
+    def calculate_base_score(scores: dict, weights: dict) -> float:
+        """
+        計算基礎分數，加入更嚴格的評估機制。
+        就像學校的評分系統，某些科目不及格會嚴重影響總成績。
+        """
+        # 檢查關鍵指標是否有嚴重不足
+        critical_thresholds = {
+            'space': 0.7,
+            'exercise': 0.7,
+            'experience': 0.7,
+            'noise': 0.65
+        }
+        
+        critical_failures = []
+        for metric, threshold in critical_thresholds.items():
+            if scores[metric] < threshold:
+                critical_failures.append((metric, scores[metric]))
+        
+        # 計算基礎加權分數
+        base_score = sum(scores[k] * weights[k] for k in scores.keys())
+        
+        # 根據關鍵指標的不足程度進行懲罰
+        if critical_failures:
+            # 計算最嚴重的不足程度
+            worst_failure = min(score for _, score in critical_failures)
+            penalty = (critical_thresholds['space'] - worst_failure) * 0.6
+            base_score *= (1 - penalty)
+            
+            # 多個指標不足時的額外懲罰
+            if len(critical_failures) > 1:
+                base_score *= (0.9 ** (len(critical_failures) - 1))
+        
+        return base_score
+
+    def evaluate_condition_interactions(scores: dict) -> float:
+        """
+        評估不同條件之間的相互影響。
+        就像運動訓練中，不同因素之間的配合度會影響整體效果。
+        """
+        interaction_penalty = 1.0
+        
+        # 居住空間與運動需求的互動
+        if user_prefs.living_space == 'house_small':
+            if user_prefs.exercise_time > 120:
+                interaction_penalty *= 0.85
+            elif user_prefs.exercise_time > 90:
+                interaction_penalty *= 0.9
+                
+        # 經驗等級與其他因素的互動
+        if user_prefs.experience_level == 'beginner':
+            if breed_info.get('Care Level') == 'HIGH':
+                interaction_penalty *= 0.8
+            if user_prefs.exercise_time > 150:
+                interaction_penalty *= 0.85
+            if breed_info.get('Exercise Needs', 'MODERATE').upper() == 'VERY HIGH':
+                interaction_penalty *= 0.85
+                
+        # 空間限制與品種大小的互動
+        if user_prefs.living_space != 'house_large':
+            if breed_info['Size'] in ['Large', 'Giant']:
+                interaction_penalty *= 0.8
+                
+        # 運動時間與類型的互動
+        exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
+        if exercise_needs == 'VERY HIGH' and user_prefs.exercise_type == 'light_walks':
+            interaction_penalty *= 0.85
+            
+        return interaction_penalty
+
+    def calculate_adjusted_perfect_bonus(perfect_conditions: dict) -> float:
+        """
+        計算完美匹配獎勵，但更注重條件的整體表現。
+        就像全能運動員的評分，需要在各個項目都表現出色。
+        """
+        bonus = 1.0
+        
+        # 降低單項獎勵的影響力
+        bonus += 0.06 * perfect_conditions['size_match']
+        bonus += 0.06 * perfect_conditions['exercise_match']
+        bonus += 0.06 * perfect_conditions['experience_match']
+        bonus += 0.03 * perfect_conditions['living_condition_match']
+        
+        # 如果有任何條件表現不佳，降低整體獎勵
+        low_scores = [score for score in perfect_conditions.values() if score < 0.6]
+        if low_scores:
+            bonus *= (0.85 ** len(low_scores))
+            
+        # 確保獎勵不會過高
+        return min(1.25, bonus)
+
+    def apply_breed_specific_adjustments(score: float) -> float:
+        """
+        根據品種特性進行最終調整。
+        考慮品種的特殊性質和限制因素。
+        """
+        # 檢查是否存在極端不匹配的情況
+        exercise_mismatch = False
+        size_mismatch = False
+        experience_mismatch = False
+        
+        # 運動需求極端不匹配
+        if breed_info.get('Exercise Needs', 'MODERATE').upper() == 'VERY HIGH':
+            if user_prefs.exercise_time < 90 or user_prefs.exercise_type == 'light_walks':
+                exercise_mismatch = True
+                
+        # 體型與空間極端不匹配
+        if user_prefs.living_space == 'apartment' and breed_info['Size'] in ['Large', 'Giant']:
+            size_mismatch = True
+            
+        # 經驗需求極端不匹配
+        if user_prefs.experience_level == 'beginner' and breed_info.get('Care Level') == 'HIGH':
+            experience_mismatch = True
+            
+        # 根據不匹配的數量進行懲罰
+        mismatch_count = sum([exercise_mismatch, size_mismatch, experience_mismatch])
+        if mismatch_count > 0:
+            score *= (0.8 ** mismatch_count)
+            
+        return score
+
     # 計算動態權重
     weights = calculate_weights()
     
-    # 正規化權重確保總和為1
+    # 正規化權重
     total_weight = sum(weights.values())
     normalized_weights = {k: v/total_weight for k, v in weights.items()}
     
     # 計算基礎分數
-    base_score = sum(scores[k] * normalized_weights[k] for k in scores.keys())
+    base_score = calculate_base_score(scores, normalized_weights)
     
-    # 計算完美匹配獎勵（降低獎勵影響以避免過高分數）
-    perfect_bonus = 1.0
-    perfect_bonus += 0.10 * perfect_conditions['size_match']      # 降低單項獎勵
-    perfect_bonus += 0.10 * perfect_conditions['exercise_match']
-    perfect_bonus += 0.10 * perfect_conditions['experience_match']
-    perfect_bonus += 0.05 * perfect_conditions['living_condition_match']
-    perfect_bonus += 0.05 * perfect_conditions['breed_trait_match']  # 新增品種特性獎勵
+    # 評估條件互動
+    interaction_multiplier = evaluate_condition_interactions(scores)
     
-    # 計算品種特性加成（使用更嚴格的係數）
-    breed_bonus = calculate_breed_bonus(breed_info, user_prefs) * 0.15  # 降低品種加成的影響
+    # 計算完美匹配獎勵
+    perfect_conditions = evaluate_perfect_conditions()
+    perfect_bonus = calculate_adjusted_perfect_bonus(perfect_conditions)
     
     # 計算初步分數
-    initial_score = (base_score * 0.85 + breed_bonus * 0.15) * perfect_bonus
+    preliminary_score = base_score * interaction_multiplier * perfect_bonus
+    
+    # 應用品種特定調整
+    final_score = apply_breed_specific_adjustments(preliminary_score)
     
-    # 應用特殊情況調整
-    final_score = apply_special_case_adjustments(initial_score)
+    # 確保分數在合理範圍內，並降低最高可能分數
+    max_possible_score = 0.96  # 降低最高可能分數
+    min_possible_score = 0.3
     
-    # 確保最終分數在有效範圍內
-    return min(1.0, max(0.3, final_score))
+    return min(max_possible_score, max(min_possible_score, final_score))
     
 
 def amplify_score_extreme(score: float) -> float: