Update scoring_calculation_system.py

scoring_calculation_system.py

@@ -285,81 +285,328 @@ def calculate_breed_bonus(breed_info: dict, user_prefs: UserPreferences) -> floa
     return min(0.5, max(-0.35, bonus))
 
 
+# @staticmethod
+# def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict:
+#     """計算額外的評估因素"""
+#     factors = {
+#         'versatility': 0.0,        # 多功能性
+#         'trainability': 0.0,       # 可訓練度
+#         'energy_level': 0.0,       # 能量水平
+#         'grooming_needs': 0.0,     # 美容需求
+#         'social_needs': 0.0,       # 社交需求
+#         'weather_adaptability': 0.0 # 氣候適應性
+#     }
+    
+#     temperament = breed_info.get('Temperament', '').lower()
+#     size = breed_info.get('Size', 'Medium')
+    
+#     # 1. 多功能性評估
+#     versatile_traits = ['intelligent', 'adaptable', 'trainable', 'athletic']
+#     working_roles = ['working', 'herding', 'hunting', 'sporting', 'companion']
+    
+#     trait_score = sum(0.2 for trait in versatile_traits if trait in temperament)
+#     role_score = sum(0.2 for role in working_roles if role in breed_info.get('Description', '').lower())
+    
+#     factors['versatility'] = min(1.0, trait_score + role_score)
+    
+#     # 2. 可訓練度評估
+#     trainable_traits = {
+#         'intelligent': 0.3,
+#         'eager to please': 0.3,
+#         'trainable': 0.2,
+#         'quick learner': 0.2
+#     }
+#     factors['trainability'] = min(1.0, sum(value for trait, value in trainable_traits.items() 
+#                                          if trait in temperament))
+    
+#     # 3. 能量水平評估
+#     exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
+#     energy_levels = {
+#         'VERY HIGH': 1.0,
+#         'HIGH': 0.8,
+#         'MODERATE': 0.6,
+#         'LOW': 0.4,
+#         'VARIES': 0.6
+#     }
+#     factors['energy_level'] = energy_levels.get(exercise_needs, 0.6)
+    
+#     # 4. 美容需求評估
+#     grooming_needs = breed_info.get('Grooming Needs', 'MODERATE').upper()
+#     grooming_levels = {
+#         'HIGH': 1.0,
+#         'MODERATE': 0.6,
+#         'LOW': 0.3
+#     }
+#     coat_penalty = 0.2 if any(term in breed_info.get('Description', '').lower() 
+#                              for term in ['long coat', 'double coat']) else 0
+#     factors['grooming_needs'] = min(1.0, grooming_levels.get(grooming_needs, 0.6) + coat_penalty)
+    
+#     # 5. 社交需求評估
+#     social_traits = ['friendly', 'social', 'affectionate', 'people-oriented']
+#     antisocial_traits = ['independent', 'aloof', 'reserved']
+    
+#     social_score = sum(0.25 for trait in social_traits if trait in temperament)
+#     antisocial_score = sum(-0.2 for trait in antisocial_traits if trait in temperament)
+#     factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score))
+    
+#     # 6. 氣候適應性評估
+#     climate_terms = {
+#         'cold': ['thick coat', 'winter', 'cold climate'],
+#         'hot': ['short coat', 'warm climate', 'heat tolerant'],
+#         'moderate': ['adaptable', 'all climate']
+#     }
+    
+#     climate_matches = sum(1 for term in climate_terms[user_prefs.climate] 
+#                         if term in breed_info.get('Description', '').lower())
+#     factors['weather_adaptability'] = min(1.0, climate_matches * 0.3 + 0.4)  # 基礎分0.4
+
+#     return factors
+
+
 @staticmethod
 def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict:
-    """計算額外的評估因素"""
+    """
+    計算額外的評估因素，結合品種特性與使用者需求的全面評估系統
+    
+    此函數整合了：
+    1. 多功能性評估 - 品種的多樣化能力
+    2. 訓練性評估 - 學習和服從能力
+    3. 能量水平評估 - 活力和運動需求
+    4. 美容需求評估 - 護理和維護需求
+    5. 社交需求評估 - 與人互動的需求程度
+    6. 氣候適應性 - 對環境的適應能力
+    7. 運動類型匹配 - 與使用者運動習慣的契合度
+    8. 生活方式適配 - 與使用者日常生活的匹配度
+    """
     factors = {
         'versatility': 0.0,        # 多功能性
         'trainability': 0.0,       # 可訓練度
         'energy_level': 0.0,       # 能量水平
         'grooming_needs': 0.0,     # 美容需求
         'social_needs': 0.0,       # 社交需求
-        'weather_adaptability': 0.0 # 氣候適應性
+        'weather_adaptability': 0.0,# 氣候適應性
+        'exercise_match': 0.0,     # 運動匹配度
+        'lifestyle_fit': 0.0       # 生活方式適配度
     }
     
     temperament = breed_info.get('Temperament', '').lower()
+    description = breed_info.get('Description', '').lower()
     size = breed_info.get('Size', 'Medium')
     
-    # 1. 多功能性評估
-    versatile_traits = ['intelligent', 'adaptable', 'trainable', 'athletic']
-    working_roles = ['working', 'herding', 'hunting', 'sporting', 'companion']
+    # 1. 多功能性評估 - 加強品種用途評估
+    versatile_traits = {
+        'intelligent': 0.25,
+        'adaptable': 0.25,
+        'trainable': 0.20,
+        'athletic': 0.15,
+        'versatile': 0.15
+    }
     
-    trait_score = sum(0.2 for trait in versatile_traits if trait in temperament)
-    role_score = sum(0.2 for role in working_roles if role in breed_info.get('Description', '').lower())
+    working_roles = {
+        'working': 0.20,
+        'herding': 0.15,
+        'hunting': 0.15,
+        'sporting': 0.15,
+        'companion': 0.10
+    }
+    
+    # 計算特質分數
+    trait_score = sum(value for trait, value in versatile_traits.items() 
+                     if trait in temperament)
+    
+    # 計算角色分數
+    role_score = sum(value for role, value in working_roles.items() 
+                    if role in description)
+    
+    # 根據使用者需求調整多功能性評分
+    purpose_traits = {
+        'light_walks': ['calm', 'gentle', 'easy-going'],
+        'moderate_activity': ['adaptable', 'balanced', 'versatile'],
+        'active_training': ['intelligent', 'trainable', 'working']
+    }
+    
+    if user_prefs.exercise_type in purpose_traits:
+        matching_traits = sum(1 for trait in purpose_traits[user_prefs.exercise_type] 
+                            if trait in temperament)
+        trait_score += matching_traits * 0.15
     
     factors['versatility'] = min(1.0, trait_score + role_score)
     
-    # 2. 可訓練度評估
+    # 2. 訓練性評估 - 考慮使用者經驗
     trainable_traits = {
         'intelligent': 0.3,
         'eager to please': 0.3,
         'trainable': 0.2,
-        'quick learner': 0.2
+        'quick learner': 0.2,
+        'obedient': 0.2
+    }
+    
+    base_trainability = sum(value for trait, value in trainable_traits.items() 
+                          if trait in temperament)
+    
+    # 根據使用者經驗調整訓練性評分
+    experience_multipliers = {
+        'beginner': 1.2,    # 新手更需要容易訓練的狗
+        'intermediate': 1.0,
+        'advanced': 0.8     # 專家能處理較難訓練的狗
     }
-    factors['trainability'] = min(1.0, sum(value for trait, value in trainable_traits.items() 
-                                         if trait in temperament))
     
-    # 3. 能量水平評估
+    factors['trainability'] = min(1.0, base_trainability * 
+                                experience_multipliers.get(user_prefs.experience_level, 1.0))
+    
+    # 3. 能量水平評估 - 強化運動需求匹配
     exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
     energy_levels = {
-        'VERY HIGH': 1.0,
-        'HIGH': 0.8,
-        'MODERATE': 0.6,
-        'LOW': 0.4,
-        'VARIES': 0.6
+        'VERY HIGH': {
+            'score': 1.0,
+            'min_exercise': 120,
+            'ideal_exercise': 150
+        },
+        'HIGH': {
+            'score': 0.8,
+            'min_exercise': 90,
+            'ideal_exercise': 120
+        },
+        'MODERATE': {
+            'score': 0.6,
+            'min_exercise': 60,
+            'ideal_exercise': 90
+        },
+        'LOW': {
+            'score': 0.4,
+            'min_exercise': 30,
+            'ideal_exercise': 60
+        }
     }
-    factors['energy_level'] = energy_levels.get(exercise_needs, 0.6)
     
-    # 4. 美容需求評估
+    breed_energy = energy_levels.get(exercise_needs, energy_levels['MODERATE'])
+    
+    # 計算運動時間匹配度
+    if user_prefs.exercise_time >= breed_energy['ideal_exercise']:
+        energy_score = breed_energy['score']
+    else:
+        # 如果運動時間不足，按比例降低分數
+        deficit_ratio = max(0.4, user_prefs.exercise_time / breed_energy['ideal_exercise'])
+        energy_score = breed_energy['score'] * deficit_ratio
+    
+    factors['energy_level'] = energy_score
+    
+    # 4. 美容需求評估 - 加入更多毛髮類型考量
     grooming_needs = breed_info.get('Grooming Needs', 'MODERATE').upper()
     grooming_levels = {
         'HIGH': 1.0,
         'MODERATE': 0.6,
         'LOW': 0.3
     }
-    coat_penalty = 0.2 if any(term in breed_info.get('Description', '').lower() 
-                             for term in ['long coat', 'double coat']) else 0
-    factors['grooming_needs'] = min(1.0, grooming_levels.get(grooming_needs, 0.6) + coat_penalty)
     
-    # 5. 社交需求評估
-    social_traits = ['friendly', 'social', 'affectionate', 'people-oriented']
-    antisocial_traits = ['independent', 'aloof', 'reserved']
+    # 特殊毛髮類型評估
+    coat_adjustments = 0
+    if 'long coat' in description:
+        coat_adjustments += 0.2
+    if 'double coat' in description:
+        coat_adjustments += 0.15
+    if 'curly' in description:
+        coat_adjustments += 0.15
+        
+    # 根據使用者承諾度調整
+    commitment_multipliers = {
+        'low': 1.5,     # 低承諾度時加重美容需求的影響
+        'medium': 1.0,
+        'high': 0.8     # 高承諾度時降低美容需求的影響
+    }
+    
+    base_grooming = grooming_levels.get(grooming_needs, 0.6) + coat_adjustments
+    factors['grooming_needs'] = min(1.0, base_grooming * 
+                                  commitment_multipliers.get(user_prefs.grooming_commitment, 1.0))
+    
+    # 5. 社交需求評估 - 加強家庭情況考量
+    social_traits = {
+        'friendly': 0.25,
+        'social': 0.25,
+        'affectionate': 0.20,
+        'people-oriented': 0.20
+    }
+    
+    antisocial_traits = {
+        'independent': -0.20,
+        'aloof': -0.20,
+        'reserved': -0.15
+    }
+    
+    social_score = sum(value for trait, value in social_traits.items() 
+                      if trait in temperament)
+    antisocial_score = sum(value for trait, value in antisocial_traits.items() 
+                          if trait in temperament)
+    
+    # 家庭情況調整
+    if user_prefs.has_children:
+        child_friendly_bonus = 0.2 if 'good with children' in temperament else 0
+        social_score += child_friendly_bonus
     
-    social_score = sum(0.25 for trait in social_traits if trait in temperament)
-    antisocial_score = sum(-0.2 for trait in antisocial_traits if trait in temperament)
     factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score))
     
-    # 6. 氣候適應性評估
-    climate_terms = {
-        'cold': ['thick coat', 'winter', 'cold climate'],
-        'hot': ['short coat', 'warm climate', 'heat tolerant'],
-        'moderate': ['adaptable', 'all climate']
+    # 6. 氣候適應性評估 - 更細緻的環境適應評估
+    climate_traits = {
+        'cold': {
+            'positive': ['thick coat', 'winter', 'cold climate'],
+            'negative': ['short coat', 'heat sensitive']
+        },
+        'hot': {
+            'positive': ['short coat', 'heat tolerant', 'warm climate'],
+            'negative': ['thick coat', 'cold climate']
+        },
+        'moderate': {
+            'positive': ['adaptable', 'all climate'],
+            'negative': []
+        }
+    }
+    
+    climate_score = 0.4  # 基礎分數
+    if user_prefs.climate in climate_traits:
+        # 正面特質加分
+        climate_score += sum(0.2 for term in climate_traits[user_prefs.climate]['positive'] 
+                           if term in description)
+        # 負面特質減分
+        climate_score -= sum(0.2 for term in climate_traits[user_prefs.climate]['negative'] 
+                           if term in description)
+    
+    factors['weather_adaptability'] = min(1.0, max(0.0, climate_score))
+    
+    # 7. 運動類型匹配評估
+    exercise_type_traits = {
+        'light_walks': ['calm', 'gentle'],
+        'moderate_activity': ['adaptable', 'balanced'],
+        'active_training': ['athletic', 'energetic']
     }
     
-    climate_matches = sum(1 for term in climate_terms[user_prefs.climate] 
-                        if term in breed_info.get('Description', '').lower())
-    factors['weather_adaptability'] = min(1.0, climate_matches * 0.3 + 0.4)  # 基礎分0.4
-
+    if user_prefs.exercise_type in exercise_type_traits:
+        match_score = sum(0.25 for trait in exercise_type_traits[user_prefs.exercise_type] 
+                         if trait in temperament)
+        factors['exercise_match'] = min(1.0, match_score + 0.5)  # 基礎分0.5
+    
+    # 8. 生活方式適配評估
+    lifestyle_score = 0.5  # 基礎分數
+    
+    # 空間適配
+    if user_prefs.living_space == 'apartment':
+        if size == 'Small':
+            lifestyle_score += 0.2
+        elif size == 'Large':
+            lifestyle_score -= 0.2
+    elif user_prefs.living_space == 'house_large':
+        if size in ['Large', 'Giant']:
+            lifestyle_score += 0.2
+    
+    # 時間可用性適配
+    time_availability_bonus = {
+        'limited': -0.1,
+        'moderate': 0,
+        'flexible': 0.1
+    }
+    lifestyle_score += time_availability_bonus.get(user_prefs.time_availability, 0)
+    
+    factors['lifestyle_fit'] = min(1.0, max(0.0, lifestyle_score))
+    
     return factors
 
 
@@ -1217,12 +1464,12 @@ def calculate_final_weighted_score(scores: dict, user_prefs: UserPreferences,
                                  breed_info: dict, adaptability_bonus: float) -> float:
     """計算最終加權分數"""
     base_weights = {
-        'space': 0.25,
-        'exercise': 0.20,
+        'space': 0.30,      
+        'exercise': 0.25,   
         'grooming': 0.15,
-        'experience': 0.18,
-        'health': 0.12,
-        'noise': 0.10
+        'experience': 0.15, 
+        'health': 0.10,
+        'noise': 0.05     
     }
 
     # 根據居住環境動態調整權重
@@ -1243,6 +1490,13 @@ def calculate_final_weighted_score(scores: dict, user_prefs: UserPreferences,
         base_weights['exercise'] *= 1.2
         base_weights['experience'] *= 0.8
 
+    if user_prefs.exercise_time > 120:  # 高運動時間
+        base_weights['exercise'] *= 1.5
+        base_weights['space'] *= 1.3
+    elif user_prefs.exercise_time < 45:  # 低運動時間
+        base_weights['exercise'] *= 0.7
+        base_weights['health'] *= 1.2
+
     # 有���童時的特殊調整
     if user_prefs.has_children:
         base_weights['noise'] *= 1.3
@@ -1268,16 +1522,12 @@ def calculate_final_weighted_score(scores: dict, user_prefs: UserPreferences,
 
 def amplify_score_range(score: float) -> float:
     """擴大分數範圍，使差異更明顯"""
-    min_score = 0.45
-    max_score = 0.95
+    min_score = 0.40    # 降低最小分數
+    max_score = 0.98    # 提高最高分數
     
-    # 正規化到 0-1 範圍
+    # 使用更激進的非線性轉換
     normalized = (score - 0.5) / 0.5
+    amplified = math.pow(abs(normalized), 1.3) * math.copysign(1, normalized)
     
-    # 非線性轉換
-    amplified = math.pow(abs(normalized), 1.5) * math.copysign(1, normalized)
-    
-    # 映射回目標範圍
     final = min_score + (max_score - min_score) * ((amplified + 1) / 2)
-    
-    return min(max_score, max(min_score, final))
+    return max(min_score, min(max_score, final))