Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -1291,87 +1291,142 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
     #     # print(f"Error in calculate_compatibility_score: {str(e)}")
     #     return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']}
 
-        scores = {
-            'space': calculate_space_score(
+        #
+        print("\n=== 開始計算品種相容性分數 ===")
+        print(f"處理品種: {breed_info.get('Breed', 'Unknown')}")
+        print(f"品種信息: {breed_info}")
+        print(f"使用者偏好: {vars(user_prefs)}")
+
+        # 1. 計算基礎分數
+        try:
+            space_score = calculate_space_score(
                 breed_info['Size'], 
                 user_prefs.living_space,
                 user_prefs.space_for_play,
                 breed_info.get('Exercise Needs', 'Moderate')
-            ),
-            'exercise': calculate_exercise_score(
+            )
+            print(f"\n空間分數計算結果: {space_score}")
+            print(f"使用參數 - Size: {breed_info['Size']}, Living Space: {user_prefs.living_space}")
+        except Exception as e:
+            print(f"空間分數計算錯誤: {str(e)}")
+            print(f"錯誤詳情: {traceback.format_exc()}")
+            raise
+
+        try:
+            exercise_score = calculate_exercise_score(
                 breed_info.get('Exercise Needs', 'Moderate'),
                 user_prefs.exercise_time
-            ),
-            'grooming': calculate_grooming_score(
+            )
+            print(f"\n運動分數計算結果: {exercise_score}")
+            print(f"使用參數 - Exercise Needs: {breed_info.get('Exercise Needs', 'Moderate')}, Time: {user_prefs.exercise_time}")
+        except Exception as e:
+            print(f"運動分數計算錯誤: {str(e)}")
+            print(f"錯誤詳情: {traceback.format_exc()}")
+            raise
+
+        try:
+            grooming_score = calculate_grooming_score(
                 breed_info.get('Grooming Needs', 'Moderate'),
                 user_prefs.grooming_commitment.lower(),
                 breed_info['Size']
-            ),
-            'experience': calculate_experience_score(
+            )
+            print(f"\n美容分數計算結果: {grooming_score}")
+            print(f"使用參數 - Grooming Needs: {breed_info.get('Grooming Needs', 'Moderate')}, Commitment: {user_prefs.grooming_commitment}")
+        except Exception as e:
+            print(f"美容分數計算錯誤: {str(e)}")
+            print(f"錯誤詳情: {traceback.format_exc()}")
+            raise
+
+        try:
+            experience_score = calculate_experience_score(
                 breed_info.get('Care Level', 'Moderate'),
                 user_prefs.experience_level,
                 breed_info.get('Temperament', '')
-            ),
-            'health': calculate_health_score(breed_info.get('Breed', '')),
-            'noise': calculate_noise_score(breed_info.get('Breed', ''), user_prefs.noise_tolerance)
+            )
+            print(f"\n經驗分數計算結果: {experience_score}")
+            print(f"使用參數 - Care Level: {breed_info.get('Care Level', 'Moderate')}, Experience: {user_prefs.experience_level}")
+        except Exception as e:
+            print(f"經驗分數計算錯誤: {str(e)}")
+            print(f"錯誤詳情: {traceback.format_exc()}")
+            raise
+
+        try:
+            health_score = calculate_health_score(breed_info.get('Breed', ''))
+            print(f"\n健康分數計算結果: {health_score}")
+            print(f"使用參數 - Breed: {breed_info.get('Breed', '')}")
+        except Exception as e:
+            print(f"健康分數計算錯誤: {str(e)}")
+            print(f"錯誤詳情: {traceback.format_exc()}")
+            raise
+
+        try:
+            noise_score = calculate_noise_score(breed_info.get('Breed', ''), user_prefs.noise_tolerance)
+            print(f"\n噪音分數計算結果: {noise_score}")
+            print(f"使用參數 - Breed: {breed_info.get('Breed', '')}, Noise Tolerance: {user_prefs.noise_tolerance}")
+        except Exception as e:
+            print(f"噪音分數計算錯誤: {str(e)}")
+            print(f"錯誤詳情: {traceback.format_exc()}")
+            raise
+
+        # 整合所有分數
+        scores = {
+            'space': space_score,
+            'exercise': exercise_score,
+            'grooming': grooming_score,
+            'experience': experience_score,
+            'health': health_score,
+            'noise': noise_score
         }
+        print("\n=== 所有基礎分數 ===")
+        for category, score in scores.items():
+            print(f"{category}: {score}")
 
-        # 2. 直接識別關鍵問題
-        penalties = []
-        
-        # 檢查重要的不適配情況
-        if user_prefs.living_space == 'apartment':
-            if breed_info['Size'] == 'Large':
-                penalties.append(0.75)  # 大型犬在公寓
-            elif breed_info['Size'] == 'Giant':
-                penalties.append(0.65)  # 超大型犬在公寓
-        
-        if user_prefs.experience_level == 'beginner':
-            temperament = breed_info.get('Temperament', '').lower()
-            if 'stubborn' in temperament or 'dominant' in temperament:
-                penalties.append(0.80)  # 新手配到難訓練的犬種
-        
-        # 3. 計算基礎分數
-        importance_factors = {
-            'space': 3.0,      # 空間評分的重要性提高
-            'exercise': 2.0,   # 運動需求次之
-            'experience': 2.5, # 經驗要求很重要
-            'health': 1.5,     # 健康因素適中
-            'grooming': 1.2,   # 美容需求較次要
-            'noise': 1.8       # 噪音也要考慮
+        # 計算加權分數
+        weights = {
+            'space': 0.28,      
+            'exercise': 0.18,   
+            'grooming': 0.12,   
+            'experience': 0.22, 
+            'health': 0.12,     
+            'noise': 0.08      
         }
+        
+        weighted_score = sum(score * weights[category] for category, score in scores.items())
+        print(f"\n加權前總分: {weighted_score}")
 
-        # 調整後的分數計算
-        adjusted_scores = []
-        for category, score in scores.items():
-            # 根據重要性調整分數
-            factor = importance_factors[category]
+        # 分數放大
+        def amplify_score(score):
+            print(f"\n開始分數放大，原始分數: {score}")
+            adjusted = (score - 0.35) * 1.8
+            print(f"調整後分數 (adjusted): {adjusted}")
             
-            # 分數調整：保持差異性
-            if score < 0.4:
-                adjusted = score * 0.7  # 低分更低
-            elif score > 0.8:
-                adjusted = score * 1.1  # 高分更高
-            else:
-                adjusted = score
-                
-            adjusted_scores.append(adjusted * factor)
-        
-        # 4. 計算最終分數
-        base_score = sum(adjusted_scores) / sum(importance_factors.values())
-        
-        # 應用懲罰（如果有）
-        final_score = base_score
-        if penalties:
-            final_score *= min(penalties)  # 使用最嚴重的懲罰
-        
-        # 確保分數在合理範圍內
-        final_score = max(0.55, min(0.95, final_score))
-        
-        # 5. 準備返回結果
+            amplified = pow(adjusted, 3.2) / 5.8 + score
+            print(f"放大後分數 (amplified): {amplified}")
+            
+            if amplified > 0.90:
+                amplified = 0.90 + (amplified - 0.90) * 0.5
+            
+            final = max(0.55, min(0.95, amplified))
+            print(f"最終分數: {final}")
+            return round(final, 3)
+
+        final_score = amplify_score(weighted_score)
+        print(f"\n=== 最終計算結果 ===")
+        print(f"最終分數: {final_score}")
+
+        # 準備返回結果
+        scores = {k: round(v, 4) for k, v in scores.items()}
         scores['overall'] = round(final_score, 4)
-        return {k: round(v, 4) for k, v in scores.items()}
+        
+        print("\n=== 返回結果 ===")
+        print(scores)
+        return scores
 
     except Exception as e:
-        print(f"Error details: {str(e)}")
+        print(f"\n!!!!! 發生嚴重錯誤 !!!!!")
+        print(f"錯誤類型: {type(e).__name__}")
+        print(f"錯誤訊息: {str(e)}")
+        print(f"完整錯誤追蹤:")
+        print(traceback.format_exc())
+        print("\n返回默認值...")
         return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']}