from dataclasses import dataclass from breed_health_info import breed_health_info from breed_noise_info import breed_noise_info import traceback @dataclass class UserPreferences: """使用者偏好設定的資料結構""" living_space: str # "apartment", "house_small", "house_large" yard_access: str # "no_yard", "shared_yard", "private_yard" exercise_time: int # minutes per day exercise_type: str # "light_walks", "moderate_activity", "active_training" grooming_commitment: str # "low", "medium", "high" experience_level: str # "beginner", "intermediate", "advanced" time_availability: str # "limited", "moderate", "flexible" has_children: bool children_age: str # "toddler", "school_age", "teenager" noise_tolerance: str # "low", "medium", "high" space_for_play: bool other_pets: bool climate: str # "cold", "moderate", "hot" health_sensitivity: str = "medium" barking_acceptance: str = None def __post_init__(self): """在初始化後運行，用於設置派生值""" if self.barking_acceptance is None: self.barking_acceptance = self.noise_tolerance # @staticmethod # def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float: # """計算品種額外加分""" # bonus = 0.0 # temperament = breed_info.get('Temperament', '').lower() # # 1. 壽命加分（最高0.05） # try: # lifespan = breed_info.get('Lifespan', '10-12 years') # years = [int(x) for x in lifespan.split('-')[0].split()[0:1]] # longevity_bonus = min(0.05, (max(years) - 10) * 0.01) # bonus += longevity_bonus # except: # pass # # 2. 性格特徵加分（最高0.15） # positive_traits = { # 'friendly': 0.05, # 'gentle': 0.05, # 'patient': 0.05, # 'intelligent': 0.04, # 'adaptable': 0.04, # 'affectionate': 0.04, # 'easy-going': 0.03, # 'calm': 0.03 # } # negative_traits = { # 'aggressive': -0.08, # 'stubborn': -0.06, # 'dominant': -0.06, # 'aloof': -0.04, # 'nervous': -0.05, # 'protective': -0.04 # } # personality_score = sum(value for trait, value in positive_traits.items() if trait in temperament) # personality_score += sum(value for trait, value in negative_traits.items() if trait in temperament) # bonus += max(-0.15, min(0.15, personality_score)) # # 3. 適應性加分（最高0.1） # adaptability_bonus = 0.0 # if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment": # adaptability_bonus += 0.05 # if 'adaptable' in temperament or 'versatile' in temperament: # adaptability_bonus += 0.05 # bonus += min(0.1, adaptability_bonus) # # 4. 家庭相容性（最高0.1） # if user_prefs.has_children: # family_traits = { # 'good with children': 0.06, # 'patient': 0.05, # 'gentle': 0.05, # 'tolerant': 0.04, # 'playful': 0.03 # } # unfriendly_traits = { # 'aggressive': -0.08, # 'nervous': -0.07, # 'protective': -0.06, # 'territorial': -0.05 # } # # 年齡評估這樣能更細緻 # age_adjustments = { # 'toddler': {'bonus_mult': 0.7, 'penalty_mult': 1.3}, # 'school_age': {'bonus_mult': 1.0, 'penalty_mult': 1.0}, # 'teenager': {'bonus_mult': 1.2, 'penalty_mult': 0.8} # } # adj = age_adjustments.get(user_prefs.children_age, # {'bonus_mult': 1.0, 'penalty_mult': 1.0}) # family_bonus = sum(value for trait, value in family_traits.items() # if trait in temperament) * adj['bonus_mult'] # family_penalty = sum(value for trait, value in unfriendly_traits.items() # if trait in temperament) * adj['penalty_mult'] # bonus += min(0.15, max(-0.2, family_bonus + family_penalty)) # # 5. 專門技能加分（最高0.1） # skill_bonus = 0.0 # special_abilities = { # 'working': 0.03, # 'herding': 0.03, # 'hunting': 0.03, # 'tracking': 0.03, # 'agility': 0.02 # } # for ability, value in special_abilities.items(): # if ability in temperament.lower(): # skill_bonus += value # bonus += min(0.1, skill_bonus) # return min(0.5, max(-0.25, bonus)) @staticmethod def calculate_breed_bonus(breed_info: dict, user_prefs: UserPreferences) -> float: """ 計算品種的額外加分，評估品種的特殊特徵對使用者需求的適配性。 這個函數考慮四個主要面向： 1. 壽命評估：考慮飼養的長期承諾 2. 性格特徵評估：評估品種性格與使用者需求的匹配度 3. 環境適應性：評估品種在特定生活環境中的表現 4. 家庭相容性：特別關注品種與家庭成員的互動 """ bonus = 0.0 temperament = breed_info.get('Temperament', '').lower() # 壽命評估 - 重新設計以反映更實際的考量 try: lifespan = breed_info.get('Lifespan', '10-12 years') years = [int(x) for x in lifespan.split('-')[0].split()[0:1]] avg_years = float(years[0]) # 根據壽命長短給予不同程度的獎勵或懲罰 if avg_years < 8: bonus -= 0.08 # 短壽命可能帶來情感負擔 elif avg_years < 10: bonus -= 0.04 # 稍短壽命輕微降低評分 elif avg_years > 13: bonus += 0.06 # 長壽命適度加分 elif avg_years > 15: bonus += 0.08 # 特別長壽的品種獲得更多加分 except: pass # 性格特徵評估 - 擴充並細化評分標準 positive_traits = { 'friendly': 0.08, # 提高友善性的重要性 'gentle': 0.08, # 溫和性格更受歡迎 'patient': 0.07, # 耐心是重要特質 'intelligent': 0.06, # 聰明但不過分重要 'adaptable': 0.06, # 適應性佳的特質 'affectionate': 0.06, # 親密性很重要 'easy-going': 0.05, # 容易相處的性格 'calm': 0.05 # 冷靜的特質 } negative_traits = { 'aggressive': -0.15, # 嚴重懲罰攻擊性 'stubborn': -0.10, # 固執性格不易處理 'dominant': -0.10, # 支配性可能造成問題 'aloof': -0.08, # 冷漠性格影響互動 'nervous': -0.08, # 緊張性格需要更多關注 'protective': -0.06 # 過度保護可能有風險 } # 性格評分計算 - 加入累積效應 personality_score = 0 positive_count = 0 negative_count = 0 for trait, value in positive_traits.items(): if trait in temperament: personality_score += value positive_count += 1 for trait, value in negative_traits.items(): if trait in temperament: personality_score += value negative_count += 1 # 多重特徵的累積效應 if positive_count > 2: personality_score *= (1 + (positive_count - 2) * 0.1) if negative_count > 1: personality_score *= (1 - (negative_count - 1) * 0.15) bonus += max(-0.25, min(0.25, personality_score)) # 適應性評估 - 根據具體環境給予更細緻的評分 adaptability_bonus = 0.0 if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment": adaptability_bonus += 0.08 # 小型犬更適合公寓 # 環境適應性評估 if 'adaptable' in temperament or 'versatile' in temperament: if user_prefs.living_space == "apartment": adaptability_bonus += 0.10 # 適應性在公寓環境更重要 else: adaptability_bonus += 0.05 # 其他環境仍有加分 # 氣候適應性 description = breed_info.get('Description', '').lower() climate = user_prefs.climate if climate == 'hot': if 'heat tolerant' in description or 'warm climate' in description: adaptability_bonus += 0.08 elif 'thick coat' in description or 'cold climate' in description: adaptability_bonus -= 0.10 elif climate == 'cold': if 'thick coat' in description or 'cold climate' in description: adaptability_bonus += 0.08 elif 'heat tolerant' in description or 'short coat' in description: adaptability_bonus -= 0.10 bonus += min(0.15, adaptability_bonus) # 家庭相容性評估 - 特別關注有孩童的家庭 if user_prefs.has_children: family_traits = { 'good with children': 0.12, # 提高與孩童相處的重要性 'patient': 0.10, 'gentle': 0.10, 'tolerant': 0.08, 'playful': 0.06 } unfriendly_traits = { 'aggressive': -0.15, # 加重攻擊性的懲罰 'nervous': -0.12, # 緊張特質可能有風險 'protective': -0.10, # 過度保護性需要注意 'territorial': -0.08 # 地域性可能造成問題 } # 根據孩童年齡調整評分權重 age_adjustments = { 'toddler': { 'bonus_mult': 0.6, # 降低正面特質的獎勵 'penalty_mult': 1.5 # 加重負面特質的懲罰 }, 'school_age': { 'bonus_mult': 1.0, 'penalty_mult': 1.0 }, 'teenager': { 'bonus_mult': 1.2, # 提高正面特質的獎勵 'penalty_mult': 0.8 # 降低負面特質的懲罰 } } adj = age_adjustments.get(user_prefs.children_age, {'bonus_mult': 1.0, 'penalty_mult': 1.0}) # 計算家庭相容性分數 family_score = 0 for trait, value in family_traits.items(): if trait in temperament: family_score += value * adj['bonus_mult'] for trait, value in unfriendly_traits.items(): if trait in temperament: family_score += value * adj['penalty_mult'] bonus += min(0.20, max(-0.30, family_score)) # 確保總體加分在合理範圍內，但允許更大的變化 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 def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences) -> dict: """計算品種與使用者條件的相容性分數的優化版本""" try: print(f"Processing breed: {breed_info.get('Breed', 'Unknown')}") print(f"Breed info keys: {breed_info.keys()}") if 'Size' not in breed_info: print("Missing Size information") raise KeyError("Size information missing") # def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float: # """空間分數計算""" # # 基礎空間需求矩陣 # base_scores = { # "Small": {"apartment": 0.95, "house_small": 1.0, "house_large": 0.90}, # "Medium": {"apartment": 0.60, "house_small": 0.90, "house_large": 1.0}, # "Large": {"apartment": 0.30, "house_small": 0.75, "house_large": 1.0}, # "Giant": {"apartment": 0.15, "house_small": 0.55, "house_large": 1.0} # } # # 取得基礎分數 # base_score = base_scores.get(size, base_scores["Medium"])[living_space] # # 運動需求調整 # exercise_adjustments = { # "Very High": -0.15 if living_space == "apartment" else 0, # "High": -0.10 if living_space == "apartment" else 0, # "Moderate": 0, # "Low": 0.05 if living_space == "apartment" else 0 # } # adjustments = exercise_adjustments.get(exercise_needs.strip(), 0) # # 院子獎勵 # if has_yard and size in ["Large", "Giant"]: # adjustments += 0.10 # elif has_yard: # adjustments += 0.05 # return min(1.0, max(0.1, base_score + adjustments)) def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float: # 重新設計基礎分數矩陣 base_scores = { "Small": { "apartment": 1.0, # 小型犬最適合公寓 "house_small": 0.95, # 在大房子反而稍微降分 "house_large": 0.85 # 可能浪費空間 }, "Medium": { "apartment": 0.45, # 中型犬在公寓明顯受限 "house_small": 0.85, "house_large": 1.0 }, "Large": { "apartment": 0.15, # 大型犬在公寓極不適合 "house_small": 0.60, # 在小房子仍然受限 "house_large": 1.0 }, "Giant": { "apartment": 0.1, # 更嚴格的限制 "house_small": 0.45, "house_large": 1.0 } } # 取得基礎分數 base_score = base_scores.get(size, base_scores["Medium"])[living_space] # 運動需求調整更明顯 exercise_adjustments = { "Very High": { "apartment": -0.25, # 在公寓更嚴重的懲罰 "house_small": -0.15, "house_large": -0.05 }, "High": { "apartment": -0.20, "house_small": -0.10, "house_large": 0 }, "Moderate": { "apartment": -0.10, "house_small": -0.05, "house_large": 0 }, "Low": { "apartment": 0.05, "house_small": 0, "house_large": 0 } } # 根據空間類型獲取對應的運動調整 adjustment = exercise_adjustments.get(exercise_needs, exercise_adjustments["Moderate"])[living_space] # 院子獎勵也要根據犬種大小調整 yard_bonus = 0 if has_yard: if size in ["Large", "Giant"]: yard_bonus = 0.20 if living_space != "apartment" else 0.10 elif size == "Medium": yard_bonus = 0.15 if living_space != "apartment" else 0.08 else: yard_bonus = 0.10 if living_space != "apartment" else 0.05 final_score = base_score + adjustment + yard_bonus return min(1.0, max(0.1, final_score)) def calculate_exercise_score(breed_needs: str, user_time: int) -> float: """運動需求計算""" exercise_needs = { 'VERY HIGH': {'min': 120, 'ideal': 150, 'max': 180}, 'HIGH': {'min': 90, 'ideal': 120, 'max': 150}, 'MODERATE': {'min': 45, 'ideal': 60, 'max': 90}, 'LOW': {'min': 20, 'ideal': 30, 'max': 45}, 'VARIES': {'min': 30, 'ideal': 60, 'max': 90} } breed_need = exercise_needs.get(breed_needs.strip().upper(), exercise_needs['MODERATE']) # 計算匹配度 if user_time >= breed_need['ideal']: if user_time > breed_need['max']: return 0.9 # 稍微降分，因為可能過度運動 return 1.0 elif user_time >= breed_need['min']: return 0.8 + (user_time - breed_need['min']) / (breed_need['ideal'] - breed_need['min']) * 0.2 else: return max(0.3, 0.8 * (user_time / breed_need['min'])) # def calculate_grooming_score(breed_needs: str, user_commitment: str, breed_size: str) -> float: # """美容需求計算""" # # 基礎分數矩陣 # base_scores = { # "High": {"low": 0.3, "medium": 0.7, "high": 1.0}, # "Moderate": {"low": 0.5, "medium": 0.9, "high": 1.0}, # "Low": {"low": 1.0, "medium": 0.95, "high": 0.8} # } # # 取得基礎分數 # base_score = base_scores.get(breed_needs, base_scores["Moderate"])[user_commitment] # # 體型影響調整 # size_adjustments = { # "Large": {"low": -0.2, "medium": -0.1, "high": 0}, # "Giant": {"low": -0.3, "medium": -0.15, "high": 0}, # } # if breed_size in size_adjustments: # adjustment = size_adjustments[breed_size].get(user_commitment, 0) # base_score = max(0.2, base_score + adjustment) # return base_score def calculate_grooming_score(breed_needs: str, user_commitment: str, breed_size: str) -> float: """ 計算美容需求分數，強化美容維護需求與使用者承諾度的匹配評估。 這個函數特別注意品種大小對美容工作的影響，以及不同程度的美容需求對時間投入的要求。 """ # 重新設計基礎分數矩陣，讓美容需求的差異更加明顯 base_scores = { "High": { "low": 0.20, # 高需求對低承諾極不合適，顯著降低初始分數 "medium": 0.65, # 中等承諾仍有挑戰 "high": 1.0 # 高承諾最適合 }, "Moderate": { "low": 0.45, # 中等需求對低承諾有困難 "medium": 0.85, # 較好的匹配 "high": 0.95 # 高承諾會有餘力 }, "Low": { "low": 0.90, # 低需求對低承諾很合適 "medium": 0.85, # 略微降低以反映可能過度投入 "high": 0.80 # 可能造成資源浪費 } } # 取得基礎分數 base_score = base_scores.get(breed_needs, base_scores["Moderate"])[user_commitment] # 根據品種大小調整美容工作量 size_adjustments = { "Giant": { "low": -0.35, # 大型犬的美容工作量顯著增加 "medium": -0.20, "high": -0.10 }, "Large": { "low": -0.25, "medium": -0.15, "high": -0.05 }, "Medium": { "low": -0.15, "medium": -0.10, "high": 0 }, "Small": { "low": -0.10, "medium": -0.05, "high": 0 } } # 應用體型調整 size_adjustment = size_adjustments.get(breed_size, size_adjustments["Medium"])[user_commitment] current_score = base_score + size_adjustment # 特殊毛髮類型的額外調整 def get_coat_adjustment(breed_description: str, commitment: str) -> float: """ 評估特殊毛髮類型所需的額外維護工作 """ adjustments = 0 # 長毛品種需要更多維護 if 'long coat' in breed_description.lower(): coat_penalties = { 'low': -0.20, 'medium': -0.15, 'high': -0.05 } adjustments += coat_penalties[commitment] # 雙層毛的品種掉毛量更大 if 'double coat' in breed_description.lower(): double_coat_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } adjustments += double_coat_penalties[commitment] # 捲毛品種需要定期專業修剪 if 'curly' in breed_description.lower(): curly_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } adjustments += curly_penalties[commitment] return adjustments # 季節性考量 def get_seasonal_adjustment(breed_description: str, commitment: str) -> float: """ 評估季節性掉毛對美容需求的影響 """ if 'seasonal shedding' in breed_description.lower(): seasonal_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } return seasonal_penalties[commitment] return 0 # 專業美容需求評估 def get_professional_grooming_adjustment(breed_description: str, commitment: str) -> float: """ 評估需要專業美容服務的影響 """ if 'professional grooming' in breed_description.lower(): grooming_penalties = { 'low': -0.20, 'medium': -0.15, 'high': -0.05 } return grooming_penalties[commitment] return 0 # 應用所有額外調整 # 由於這些是示例調整，實際使用時需要根據品種描述信息進行調整 coat_adjustment = get_coat_adjustment("", user_commitment) seasonal_adjustment = get_seasonal_adjustment("", user_commitment) professional_adjustment = get_professional_grooming_adjustment("", user_commitment) final_score = current_score + coat_adjustment + seasonal_adjustment + professional_adjustment # 確保分數在有意義的範圍內，但允許更大的差異 return max(0.1, min(1.0, final_score)) # def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float: # """ # 計算使用者經驗與品種需求的匹配分數 # 參數說明: # care_level: 品種的照顧難度 ("High", "Moderate", "Low") # user_experience: 使用者經驗等級 ("beginner", "intermediate", "advanced") # temperament: 品種的性格特徵描述 # 返回: # float: 0.2-1.0 之間的匹配分數 # """ # # 基礎分數矩陣 - 更大的分數差異來反映經驗重要性 # base_scores = { # "High": { # "beginner": 0.12, # 降低起始分，反映高難度品種對新手的挑戰 # "intermediate": 0.65, # 中級玩家可以應付，但仍有改善空間 # "advanced": 1.0 # 資深者能完全勝任 # }, # "Moderate": { # "beginner": 0.35, # 適中難度對新手來說仍具挑戰 # "intermediate": 0.82, # 中級玩家有很好的勝任能力 # "advanced": 1.0 # 資深者完全勝任 # }, # "Low": { # "beginner": 0.72, # 低難度品種適合新手 # "intermediate": 0.92, # 中級玩家幾乎完全勝任 # "advanced": 1.0 # 資深者完全勝任 # } # } # # 取得基礎分數 # score = base_scores.get(care_level, base_scores["Moderate"])[user_experience] # # 性格特徵評估 - 根據經驗等級調整權重 # temperament_lower = temperament.lower() # temperament_adjustments = 0.0 # if user_experience == "beginner": # # 新手不適合的特徵 - 更嚴格的懲罰 # difficult_traits = { # 'stubborn': -0.15, # 加重固執的懲罰 # 'independent': -0.12, # 加重獨立性的懲罰 # 'dominant': -0.12, # 加重支配性的懲罰 # 'strong-willed': -0.10, # 加重強勢的懲罰 # 'protective': -0.08, # 加重保護性的懲罰 # 'aloof': -0.08, # 加重冷漠的懲罰 # 'energetic': -0.06 # 輕微懲罰高能量 # } # # 新手友善的特徵 - 提供更多獎勵 # easy_traits = { # 'gentle': 0.08, # 增加溫和的獎勵 # 'friendly': 0.08, # 增加友善的獎勵 # 'eager to please': 0.08, # 增加順從的獎勵 # 'patient': 0.06, # 獎勵耐心 # 'adaptable': 0.06, # 獎勵適應性 # 'calm': 0.05 # 獎勵冷靜 # } # # 計算特徵調整 # for trait, penalty in difficult_traits.items(): # if trait in temperament_lower: # temperament_adjustments += penalty * 1.2 # 加重新手的懲罰 # for trait, bonus in easy_traits.items(): # if trait in temperament_lower: # temperament_adjustments += bonus # # 品種特殊調整 # if any(term in temperament_lower for term in ['terrier', 'working', 'guard']): # temperament_adjustments -= 0.12 # 加重對特定類型品種的懲罰 # elif user_experience == "intermediate": # # 中級玩家的調整更加平衡 # moderate_traits = { # 'intelligent': 0.05, # 獎勵聰明 # 'athletic': 0.04, # 獎勵運動能力 # 'versatile': 0.04, # 獎勵多功能性 # 'stubborn': -0.06, # 輕微懲罰固執 # 'independent': -0.05, # 輕微懲罰獨立性 # 'protective': -0.04 # 輕微懲罰保護性 # } # for trait, adjustment in moderate_traits.items(): # if trait in temperament_lower: # temperament_adjustments += adjustment # else: # advanced # # 資深玩家能夠應對挑戰性特徵 # advanced_traits = { # 'stubborn': 0.04, # 反轉為優勢 # 'independent': 0.04, # 反轉為優勢 # 'intelligent': 0.05, # 獎勵聰明 # 'protective': 0.04, # 獎勵保護性 # 'strong-willed': 0.03 # 獎勵強勢 # } # for trait, bonus in advanced_traits.items(): # if trait in temperament_lower: # temperament_adjustments += bonus # # 確保最終分數在合理範圍內 # final_score = max(0.2, min(1.0, score + temperament_adjustments)) # return final_score def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float: """ 計算使用者經驗與品種需求的匹配分數，加強經驗等級的影響力 重要改進： 1. 擴大基礎分數差異 2. 加重困難特徵的懲罰 3. 更細緻的品種特性評估 """ # 基礎分數矩陣 - 大幅擴大不同經驗等級的分數差異 base_scores = { "High": { "beginner": 0.10, # 降低起始分，高難度品種對新手幾乎不推薦 "intermediate": 0.60, # 中級玩家仍需謹慎 "advanced": 1.0 # 資深者能完全勝任 }, "Moderate": { "beginner": 0.35, # 適中難度對新手仍具挑戰 "intermediate": 0.80, # 中級玩家較適合 "advanced": 1.0 # 資深者完全勝任 }, "Low": { "beginner": 0.90, # 新手友善品種 "intermediate": 0.95, # 中級玩家幾乎完全勝任 "advanced": 1.0 # 資深者完全勝任 } } # 取得基礎分數 score = base_scores.get(care_level, base_scores["Moderate"])[user_experience] temperament_lower = temperament.lower() temperament_adjustments = 0.0 # 根據經驗等級設定不同的特徵評估標準 if user_experience == "beginner": # 新手不適合的特徵 - 更嚴格的懲罰 difficult_traits = { 'stubborn': -0.30, # 固執性格嚴重影響新手 'independent': -0.25, # 獨立性高的品種不適合新手 'dominant': -0.25, # 支配性強的品種需要經驗處理 'strong-willed': -0.20, # 強勢性格需要技巧管理 'protective': -0.20, # 保護性強需要適當訓練 'aloof': -0.15, # 冷漠性格需要耐心培養 'energetic': -0.15, # 活潑好動需要經驗引導 'aggressive': -0.35 # 攻擊傾向極不適合新手 } # 新手友善的特徵 - 適度的獎勵 easy_traits = { 'gentle': 0.05, # 溫和性格適合新手 'friendly': 0.05, # 友善性格容易相處 'eager to please': 0.08, # 願意服從較容易訓練 'patient': 0.05, # 耐心的特質有助於建立關係 'adaptable': 0.05, # 適應性強較容易照顧 'calm': 0.06 # 冷靜的性格較好掌握 } # 計算特徵調整 for trait, penalty in difficult_traits.items(): if trait in temperament_lower: temperament_adjustments += penalty for trait, bonus in easy_traits.items(): if trait in temperament_lower: temperament_adjustments += bonus # 品種類型特殊評估 if 'terrier' in temperament_lower: temperament_adjustments -= 0.20 # 梗類犬種通常不適合新手 elif 'working' in temperament_lower: temperament_adjustments -= 0.25 # 工作犬需要經驗豐富的主人 elif 'guard' in temperament_lower: temperament_adjustments -= 0.25 # 護衛犬需要專業訓練 elif user_experience == "intermediate": # 中級玩家的特徵評估 moderate_traits = { 'stubborn': -0.15, # 仍然需要注意，但懲罰較輕 'independent': -0.10, 'intelligent': 0.08, # 聰明的特質可以好好發揮 'athletic': 0.06, # 運動能力可以適當訓練 'versatile': 0.07, # 多功能性可以開發 'protective': -0.08 # 保護性仍需注意 } for trait, adjustment in moderate_traits.items(): if trait in temperament_lower: temperament_adjustments += adjustment else: # advanced # 資深玩家能夠應對挑戰性特徵 advanced_traits = { 'stubborn': 0.05, # 困難特徵反而成為優勢 'independent': 0.05, 'intelligent': 0.10, 'protective': 0.05, 'strong-willed': 0.05 } for trait, bonus in advanced_traits.items(): if trait in temperament_lower: temperament_adjustments += bonus # 確保最終分數範圍更大，讓差異更明顯 final_score = max(0.05, min(1.0, score + temperament_adjustments)) return final_score # def calculate_health_score(breed_name: str) -> float: # """計算品種健康分數""" # if breed_name not in breed_health_info: # return 0.5 # health_notes = breed_health_info[breed_name]['health_notes'].lower() # # 嚴重健康問題（降低0.15分） # severe_conditions = [ # 'hip dysplasia', # 'heart disease', # 'progressive retinal atrophy', # 'bloat', # 'epilepsy', # 'degenerative myelopathy', # 'von willebrand disease' # ] # # 中度健康問題（降低0.1分） # moderate_conditions = [ # 'allergies', # 'eye problems', # 'joint problems', # 'hypothyroidism', # 'ear infections', # 'skin issues' # ] # # 輕微健康問題（降低0.05分） # minor_conditions = [ # 'dental issues', # 'weight gain tendency', # 'minor allergies', # 'seasonal allergies' # ] # # 計算基礎健康分數 # health_score = 1.0 # # 根據問題嚴重程度扣分 # severe_count = sum(1 for condition in severe_conditions if condition in health_notes) # moderate_count = sum(1 for condition in moderate_conditions if condition in health_notes) # minor_count = sum(1 for condition in minor_conditions if condition in health_notes) # health_score -= (severe_count * 0.15) # health_score -= (moderate_count * 0.1) # health_score -= (minor_count * 0.05) # # 壽命影響 # try: # lifespan = breed_health_info[breed_name].get('average_lifespan', '10-12') # years = float(lifespan.split('-')[0]) # if years < 8: # health_score *= 0.9 # elif years > 13: # health_score *= 1.1 # except: # pass # # 特殊健康優勢 # if 'generally healthy' in health_notes or 'hardy breed' in health_notes: # health_score *= 1.1 # return max(0.2, min(1.0, health_score)) def calculate_health_score(breed_name: str, user_prefs: UserPreferences) -> float: """ 計算品種健康分數，加強健康問題的影響力和與使用者敏感度的連結 重要改進： 1. 根據使用者的健康敏感度調整分數 2. 更嚴格的健康問題評估 3. 考慮多重健康問題的累積效應 4. 加入遺傳疾病的特別考量 """ if breed_name not in breed_health_info: return 0.5 health_notes = breed_health_info[breed_name]['health_notes'].lower() # 嚴重健康問題 - 加重扣分 severe_conditions = { 'hip dysplasia': -0.25, # 髖關節發育不良，影響生活品質 'heart disease': -0.25, # 心臟疾病，需要長期治療 'progressive retinal atrophy': -0.20, # 進行性視網膜萎縮，導致失明 'bloat': -0.22, # 胃扭轉，致命風險 'epilepsy': -0.20, # 癲癇，需要長期藥物控制 'degenerative myelopathy': -0.20, # 脊髓退化，影響行動能力 'von willebrand disease': -0.18 # 血液凝固障礙 } # 中度健康問題 - 適度扣分 moderate_conditions = { 'allergies': -0.12, # 過敏問題，需要持續關注 'eye problems': -0.15, # 眼睛問題，可能需要手術 'joint problems': -0.15, # 關節問題，影響運動能力 'hypothyroidism': -0.12, # 甲狀腺功能低下，需要藥物治療 'ear infections': -0.10, # 耳道感染，需要定期清理 'skin issues': -0.12 # 皮膚問題，需要特殊護理 } # 輕微健康問題 - 輕微扣分 minor_conditions = { 'dental issues': -0.08, # 牙齒問題，需要定期護理 'weight gain tendency': -0.08, # 易胖體質，需要控制飲食 'minor allergies': -0.06, # 輕微過敏，可控制 'seasonal allergies': -0.06 # 季節性過敏 } # 計算基礎健康分數 health_score = 1.0 # 健康問題累積效應計算 condition_counts = { 'severe': 0, 'moderate': 0, 'minor': 0 } # 計算各等級健康問題的數量和影響 for condition, penalty in severe_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['severe'] += 1 for condition, penalty in moderate_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['moderate'] += 1 for condition, penalty in minor_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['minor'] += 1 # 多重問題的額外懲罰（累積效應） if condition_counts['severe'] > 1: health_score *= (0.85 ** (condition_counts['severe'] - 1)) if condition_counts['moderate'] > 2: health_score *= (0.90 ** (condition_counts['moderate'] - 2)) # 根據使用者健康敏感度調整分數 sensitivity_multipliers = { 'low': 1.1, # 較不在意健康問題 'medium': 1.0, # 標準評估 'high': 0.85 # 非常注重健康問題 } health_score *= sensitivity_multipliers.get(user_prefs.health_sensitivity, 1.0) # 壽命影響評估 try: lifespan = breed_health_info[breed_name].get('average_lifespan', '10-12') years = float(lifespan.split('-')[0]) if years < 8: health_score *= 0.85 # 短壽命顯著降低分數 elif years < 10: health_score *= 0.92 # 較短壽命輕微降低分數 elif years > 13: health_score *= 1.1 # 長壽命適度加分 except: pass # 特殊健康優勢 if 'generally healthy' in health_notes or 'hardy breed' in health_notes: health_score *= 1.15 elif 'robust health' in health_notes or 'few health issues' in health_notes: health_score *= 1.1 # 確保分數在合理範圍內，但允許更大的分數差異 return max(0.1, min(1.0, health_score)) # def calculate_noise_score(breed_name: str, user_noise_tolerance: str) -> float: # """計算品種噪音分數""" # if breed_name not in breed_noise_info: # return 0.5 # noise_info = breed_noise_info[breed_name] # noise_level = noise_info['noise_level'].lower() # noise_notes = noise_info['noise_notes'].lower() # # 基礎噪音分數矩陣 # base_scores = { # 'low': {'low': 1.0, 'medium': 0.9, 'high': 0.8}, # 'medium': {'low': 0.7, 'medium': 1.0, 'high': 0.9}, # 'high': {'low': 0.4, 'medium': 0.7, 'high': 1.0}, # 'varies': {'low': 0.6, 'medium': 0.8, 'high': 0.9} # } # # 獲取基礎分數 # base_score = base_scores.get(noise_level, {'low': 0.7, 'medium': 0.8, 'high': 0.6})[user_noise_tolerance] # # 吠叫原因評估 # barking_reasons_penalty = 0 # problematic_triggers = [ # ('separation anxiety', -0.15), # ('excessive barking', -0.12), # ('territorial', -0.08), # ('alert barking', -0.05), # ('attention seeking', -0.05) # ] # for trigger, penalty in problematic_triggers: # if trigger in noise_notes: # barking_reasons_penalty += penalty # # 可訓練性補償 # trainability_bonus = 0 # if 'responds well to training' in noise_notes: # trainability_bonus = 0.1 # elif 'can be trained' in noise_notes: # trainability_bonus = 0.05 # # 特殊情況 # special_adjustments = 0 # if 'rarely barks' in noise_notes: # special_adjustments += 0.1 # if 'howls' in noise_notes and user_noise_tolerance == 'low': # special_adjustments -= 0.1 # final_score = base_score + barking_reasons_penalty + trainability_bonus + special_adjustments # return max(0.2, min(1.0, final_score)) def calculate_noise_score(breed_name: str, user_prefs: UserPreferences) -> float: """ 計算品種噪音分數，特別加強噪音程度與生活環境的關聯性評估 """ if breed_name not in breed_noise_info: return 0.5 noise_info = breed_noise_info[breed_name] noise_level = noise_info['noise_level'].lower() noise_notes = noise_info['noise_notes'].lower() # 重新設計基礎噪音分數矩陣，考慮不同情境下的接受度 base_scores = { 'low': { 'low': 1.0, # 安靜的狗對低容忍完美匹配 'medium': 0.95, # 安靜的狗對一般容忍很好 'high': 0.90 # 安靜的狗對高容忍當然可以 }, 'medium': { 'low': 0.60, # 一般吠叫對低容忍較困難 'medium': 0.90, # 一般吠叫對一般容忍可接受 'high': 0.95 # 一般吠叫對高容忍很好 }, 'high': { 'low': 0.25, # 愛叫的狗對低容忍極不適合 'medium': 0.65, # 愛叫的狗對一般容忍有挑戰 'high': 0.90 # 愛叫的狗對高容忍可以接受 }, 'varies': { 'low': 0.50, # 不確定的情況對低容忍風險較大 'medium': 0.75, # 不確定的情況對一般容忍可嘗試 'high': 0.85 # 不確定的情況對高容忍問題較小 } } # 取得基礎分數 base_score = base_scores.get(noise_level, {'low': 0.6, 'medium': 0.75, 'high': 0.85})[user_prefs.noise_tolerance] # 吠叫原因評估，根據環境調整懲罰程度 barking_penalties = { 'separation anxiety': { 'apartment': -0.30, # 在公寓對鄰居影響更大 'house_small': -0.25, 'house_large': -0.20 }, 'excessive barking': { 'apartment': -0.25, 'house_small': -0.20, 'house_large': -0.15 }, 'territorial': { 'apartment': -0.20, # 在公寓更容易被觸發 'house_small': -0.15, 'house_large': -0.10 }, 'alert barking': { 'apartment': -0.15, # 公寓環境刺激較多 'house_small': -0.10, 'house_large': -0.08 }, 'attention seeking': { 'apartment': -0.15, 'house_small': -0.12, 'house_large': -0.10 } } # 計算環境相關的吠叫懲罰 living_space = user_prefs.living_space barking_penalty = 0 for trigger, penalties in barking_penalties.items(): if trigger in noise_notes: barking_penalty += penalties.get(living_space, -0.15) # 特殊情況評估 special_adjustments = 0 if user_prefs.has_children: # 孩童年齡相關調整 child_age_adjustments = { 'toddler': { 'high': -0.20, # 幼童對吵鬧更敏感 'medium': -0.15, 'low': -0.05 }, 'school_age': { 'high': -0.15, 'medium': -0.10, 'low': -0.05 }, 'teenager': { 'high': -0.10, 'medium': -0.05, 'low': -0.02 } } # 根據孩童年齡和噪音等級調整 age_adj = child_age_adjustments.get(user_prefs.children_age, child_age_adjustments['school_age']) special_adjustments += age_adj.get(noise_level, -0.10) # 訓練性補償評估 trainability_bonus = 0 if 'responds well to training' in noise_notes: trainability_bonus = 0.12 elif 'can be trained' in noise_notes: trainability_bonus = 0.08 elif 'difficult to train' in noise_notes: trainability_bonus = 0.02 # 夜間吠叫特別考量 if 'night barking' in noise_notes or 'howls' in noise_notes: if user_prefs.living_space == 'apartment': special_adjustments -= 0.15 elif user_prefs.living_space == 'house_small': special_adjustments -= 0.10 else: special_adjustments -= 0.05 # 計算最終分數，確保更大的分數範圍 final_score = base_score + barking_penalty + special_adjustments + trainability_bonus return max(0.1, min(1.0, final_score)) # # 計算所有基礎分數 # scores = { # 'space': 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( # breed_info.get('Exercise Needs', 'Moderate'), # user_prefs.exercise_time # ), # 'grooming': calculate_grooming_score( # breed_info.get('Grooming Needs', 'Moderate'), # user_prefs.grooming_commitment.lower(), # breed_info['Size'] # ), # 'experience': 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) # } # # 優化權重配置 # weights = { # 'space': 0.28, # 'exercise': 0.18, # 'grooming': 0.12, # 'experience': 0.22, # 'health': 0.12, # 'noise': 0.08 # } # # 計算加權總分 # weighted_score = sum(score * weights[category] for category, score in scores.items()) # def amplify_score(score): # """ # 優化分數放大函數，確保分數範圍合理且結果一致 # """ # # 基礎調整 # adjusted = (score - 0.35) * 1.8 # # 使用 3.2 次方使曲線更平滑 # amplified = pow(adjusted, 3.2) / 5.8 + score # # 特別處理高分區間，確保不超過95% # if amplified > 0.90: # # 壓縮高分區間，確保最高到95% # amplified = 0.90 + (amplified - 0.90) * 0.5 # # 確保最終分數在合理範圍內（0.55-0.95） # final_score = max(0.55, min(0.95, amplified)) # # 四捨五入到小數點後第三位 # return round(final_score, 3) # final_score = amplify_score(weighted_score) # # 四捨五入所有分數 # scores = {k: round(v, 4) for k, v in scores.items()} # scores['overall'] = round(final_score, 4) # return scores # except Exception as e: # print(f"Error details: {str(e)}") # print(f"breed_info: {breed_info}") # # 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( breed_info['Size'], user_prefs.living_space, user_prefs.space_for_play, breed_info.get('Exercise Needs', 'Moderate') ), 'exercise': calculate_exercise_score( breed_info.get('Exercise Needs', 'Moderate'), user_prefs.exercise_time ), 'grooming': calculate_grooming_score( breed_info.get('Grooming Needs', 'Moderate'), user_prefs.grooming_commitment.lower(), breed_info['Size'] ), 'experience': 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) } # 2. 優化權重配置 - 根據使用者情況動態調整權重 base_weights = { 'space': 0.28, 'exercise': 0.18, 'grooming': 0.12, 'experience': 0.22, 'health': 0.12, 'noise': 0.08 } # 根據特殊情況調整權重 weights = base_weights.copy() # 有孩童時的權重調整 if user_prefs.has_children: if user_prefs.children_age == 'toddler': weights['experience'] *= 1.4 # 幼童需要更有經驗的配對 weights['noise'] *= 1.3 # 噪音影響更重要 weights['health'] *= 1.2 # 健康因素更關鍵 elif user_prefs.children_age == 'school_age': weights['experience'] *= 1.2 weights['noise'] *= 1.2 # 居住環境的權重調整 if user_prefs.living_space == 'apartment': weights['space'] *= 1.3 # 空間限制更重要 weights['noise'] *= 1.2 # 噪音影響更顯著 # 重新正規化權重 total_weight = sum(weights.values()) weights = {k: v/total_weight for k, v in weights.items()} # 3. 計算加權總分 weighted_score = sum(score * weights[category] for category, score in scores.items()) # 4. 改進的分數放大函數 def amplify_score(score): """ 改進的分數放大函數，提供更大的分數差異。 主要改進： 1. 調整基礎計算參數以產生更明顯的差異 2. 根據分數區間使用不同的放大策略 3. 擴大最終分數範圍 """ # 基礎調整，擴大差異 adjusted = (score - 0.4) * 2.0 # 從0.35調整到0.4，倍數從1.8提高到2.0 # 使用更陡峭的曲線，從3.2降到2.8使曲線不會過於極端 amplified = pow(adjusted, 2.8) / 4.5 + score # 分數區間處理 if score < 0.65: # 低分區間 amplified *= 0.85 # 進一步降低不適合的配對 elif score > 0.85: # 高分區間 amplified = 0.85 + (amplified - 0.85) * 0.4 # 高分區間的緩和壓縮 # 擴大分數範圍到0.45-0.95，原本是0.55-0.95 final_score = max(0.45, min(0.95, amplified)) # 四捨五入到小數點後第三位 return round(final_score, 3) # 5. 計算最終分數並應用關鍵條件檢查 final_score = amplify_score(weighted_score) # 針對特殊情況的最終調整 if user_prefs.has_children and scores['experience'] < 0.4: final_score *= 0.8 # 有孩童但經驗分數過低時大幅降低 if user_prefs.living_space == 'apartment' and scores['noise'] < 0.3: final_score *= 0.75 # 住公寓但噪音分數極低時顯著降低 # 6. 準備返回結果 scores = {k: round(v, 4) for k, v in scores.items()} scores['overall'] = round(final_score, 4) return scores except Exception as e: print(f"Error details: {str(e)}") print(f"breed_info: {breed_info}") return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']}