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from dataclasses import dataclass
from breed_health_info import breed_health_info
from breed_noise_info import breed_noise_info
import traceback
import math
@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
size_preference: str = "no_preference" # "no_preference", "small", "medium", "large", "giant"
training_commitment: str = "medium" # "low", "medium", "high" - 訓練投入程度
living_environment: str = "ground_floor" # "ground_floor", "with_elevator", "walk_up" - 居住環境細節
def __post_init__(self):
if self.barking_acceptance is None:
self.barking_acceptance = self.noise_tolerance
def apply_size_filter(breed_score: float, user_preference: str, breed_size: str) -> float:
"""
基於用戶的體型偏好過濾品種，只要不符合就過濾掉
Parameters:
breed_score (float): 原始品種評分
user_preference (str): 用戶偏好的體型
breed_size (str): 品種的實際體型
Returns:
float: 過濾後的評分，如果體型不符合會返回 0
"""
if user_preference == "no_preference":
return breed_score
# 標準化 size 字串以進行比較
breed_size = breed_size.lower().strip()
user_preference = user_preference.lower().strip()
# 特殊處理 "varies" 的情況
if breed_size == "varies":
return breed_score * 0.5 # 給予一個折扣係數，因為不確定性
# 如果用戶有明確體型偏好但品種不符合，返回 0
if user_preference != breed_size:
return 0
return breed_score
@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
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)
# 6. 適應性評估
adaptability_bonus = 0.0
if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment":
adaptability_bonus += 0.08
# 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
else:
adaptability_bonus += 0.05
# 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)
return min(0.5, max(-0.25, bonus))
@staticmethod
def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict:
"""
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
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
'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 - 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
}
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. 訓練性評估
trainable_traits = {
'intelligent': 0.3,
'eager to please': 0.3,
'trainable': 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, base_trainability *
experience_multipliers.get(user_prefs.experience_level, 1.0))
# 3. 能量水平評估
exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
energy_levels = {
'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
}
}
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
grooming_needs = breed_info.get('Grooming Needs', 'MODERATE').upper()
grooming_levels = {
'HIGH': 1.0,
'MODERATE': 0.6,
'LOW': 0.3
}
# 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
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
factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score))
# 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']
}
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
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"""
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")
if user_prefs.size_preference != "no_preference":
if breed_info['Size'].lower() != user_prefs.size_preference.lower():
return {
'space': 0,
'exercise': 0,
'grooming': 0,
'experience': 0,
'health': 0,
'noise': 0,
'overall': 0,
'adaptability_bonus': 0
}
def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float:
"""
1. 動態的基礎分數矩陣
2. 強化空間品質評估
3. 增加極端情況處理
4. 考慮不同空間組合的協同效應
"""
def get_base_score():
# 基礎分數矩陣 - 更極端的分數分配
base_matrix = {
"Small": {
"apartment": {
"no_yard": 0.85, # 小型犬在公寓仍然適合
"shared_yard": 0.90, # 共享院子提供額外活動空間
"private_yard": 0.95 # 私人院子最理想
},
"house_small": {
"no_yard": 0.80,
"shared_yard": 0.85,
"private_yard": 0.90
},
"house_large": {
"no_yard": 0.75,
"shared_yard": 0.80,
"private_yard": 0.85
}
},
"Medium": {
"apartment": {
"no_yard": 0.75,
"shared_yard": 0.85,
"private_yard": 0.90
},
"house_small": {
"no_yard": 0.80,
"shared_yard": 0.90,
"private_yard": 0.90
},
"house_large": {
"no_yard": 0.85,
"shared_yard": 0.90,
"private_yard": 0.95
}
},
"Large": {
"apartment": {
"no_yard": 0.70,
"shared_yard": 0.80,
"private_yard": 0.85
},
"house_small": {
"no_yard": 0.75,
"shared_yard": 0.85,
"private_yard": 0.90
},
"house_large": {
"no_yard": 0.85,
"shared_yard": 0.90,
"private_yard": 1.0
}
},
"Giant": {
"apartment": {
"no_yard": 0.65,
"shared_yard": 0.75,
"private_yard": 0.80
},
"house_small": {
"no_yard": 0.70,
"shared_yard": 0.80,
"private_yard": 0.85
},
"house_large": {
"no_yard": 0.80,
"shared_yard": 0.90,
"private_yard": 1.0
}
}
}
yard_type = "private_yard" if has_yard else "no_yard"
return base_matrix.get(size, base_matrix["Medium"])[living_space][yard_type]
def calculate_exercise_adjustment():
# 運動需求對空間評分的影響
exercise_impact = {
"Very High": {
"apartment": -0.10,
"house_small": -0.05,
"house_large": 0
},
"High": {
"apartment": -0.08,
"house_small": -0.05,
"house_large": 0
},
"Moderate": {
"apartment": -0.5,
"house_small": -0.02,
"house_large": 0
},
"Low": {
"apartment": 0.10,
"house_small": 0.05,
"house_large": 0
}
}
return exercise_impact.get(exercise_needs, exercise_impact["Moderate"])[living_space]
def calculate_yard_bonus():
# 院子效益評估更加細緻
if not has_yard:
return 0
yard_benefits = {
"Giant": {
"Very High": 0.25,
"High": 0.20,
"Moderate": 0.15,
"Low": 0.10
},
"Large": {
"Very High": 0.20,
"High": 0.15,
"Moderate": 0.10,
"Low": 0.05
},
"Medium": {
"Very High": 0.15,
"High": 0.10,
"Moderate": 0.08,
"Low": 0.05
},
"Small": {
"Very High": 0.10,
"High": 0.08,
"Moderate": 0.05,
"Low": 0.03
}
}
size_benefits = yard_benefits.get(size, yard_benefits["Medium"])
return size_benefits.get(exercise_needs, size_benefits["Moderate"])
def apply_extreme_case_adjustments(score):
# 處理極端情況
if size == "Giant" and living_space == "apartment":
return score * 0.85
if size == "Large" and living_space == "apartment" and exercise_needs == "Very High":
return score * 0.85
if size == "Small" and living_space == "house_large" and exercise_needs == "Low":
return score * 0.9 # 低運動需求的小型犬在大房子可能過於寬敞
return score
# 計算最終分數
base_score = get_base_score()
exercise_adj = calculate_exercise_adjustment()
yard_bonus = calculate_yard_bonus()
# 整合所有評分因素
initial_score = base_score + exercise_adj + yard_bonus
# 應用極端情況調整
final_score = apply_extreme_case_adjustments(initial_score)
# 確保分數在有效範圍內，但允許更極端的結果
return max(0.05, min(1.0, final_score))
def calculate_exercise_score(breed_needs: str, exercise_time: int, exercise_type: str, breed_size: str, living_space: str) -> float:
"""
計算品種運動需求與使用者運動條件的匹配度
1. 不同品種的運動耐受度差異
2. 運動時間與類型的匹配度
3. 極端運動量的嚴格限制
Parameters:
breed_needs: 品種的運動需求等級
exercise_time: 使用者計劃的運動時間（分鐘）
exercise_type: 運動類型（輕度/中度/高度）
Returns:
float: 0.1到1.0之間的匹配分數
"""
# 定義每個運動需求等級的具體參數
exercise_levels = {
'VERY HIGH': {
'min': 120, # 最低需求
'ideal': 150, # 理想運動量
'max': 180, # 最大建議量
'type_weights': { # 不同運動類型的權重
'active_training': 1.0,
'moderate_activity': 0.6,
'light_walks': 0.3
}
},
'HIGH': {
'min': 90,
'ideal': 120,
'max': 150,
'type_weights': {
'active_training': 0.9,
'moderate_activity': 0.8,
'light_walks': 0.4
}
},
'MODERATE': {
'min': 45,
'ideal': 60,
'max': 90,
'type_weights': {
'active_training': 0.7,
'moderate_activity': 1.0,
'light_walks': 0.8
}
},
'LOW': {
'min': 15,
'ideal': 30,
'max': 45,
'type_weights': {
'active_training': 0.5,
'moderate_activity': 0.8,
'light_walks': 1.0
}
}
}
# 獲取品種的運動參數
breed_level = exercise_levels.get(breed_needs.upper(), exercise_levels['MODERATE'])
# 計算時間匹配度
def calculate_time_score():
"""計算運動時間的匹配度，特別處理過度運動的情況"""
if exercise_time < breed_level['min']:
# 運動不足的嚴格懲罰
deficit_ratio = exercise_time / breed_level['min']
return max(0.1, deficit_ratio * 0.4)
elif exercise_time <= breed_level['ideal']:
# 理想範圍內的漸進提升
progress = (exercise_time - breed_level['min']) / (breed_level['ideal'] - breed_level['min'])
return 0.6 + (progress * 0.4)
elif exercise_time <= breed_level['max']:
# 理想到最大範圍的平緩下降
excess_ratio = (exercise_time - breed_level['ideal']) / (breed_level['max'] - breed_level['ideal'])
return 1.0 - (excess_ratio * 0.2)
else:
# 過度運動的顯著懲罰
excess = (exercise_time - breed_level['max']) / breed_level['max']
# 低運動需求品種的過度運動懲罰更嚴重
penalty_factor = 1.5 if breed_needs.upper() == 'LOW' else 1.0
return max(0.1, 0.8 - (excess * 0.5 * penalty_factor))
# 計算運動類型匹配度
def calculate_type_score():
"""評估運動類型的適合度，考慮品種特性"""
base_type_score = breed_level['type_weights'].get(exercise_type, 0.5)
# 特殊情況處理
if breed_needs.upper() == 'LOW' and exercise_type == 'active_training':
# 低運動需求品種不適合高強度運動
base_type_score *= 0.5
elif breed_needs.upper() == 'VERY HIGH' and exercise_type == 'light_walks':
# 高運動需求品種需要更多強度
base_type_score *= 0.6
return base_type_score
# 計算最終分數
time_score = calculate_time_score()
type_score = calculate_type_score()
# 根據運動需求等級調整權重
if breed_needs.upper() == 'LOW':
# 低運動需求品種更重視運動類型的合適性
final_score = (time_score * 0.6) + (type_score * 0.4)
elif breed_needs.upper() == 'VERY HIGH':
# 高運動需求品種更重視運動時間的充足性
final_score = (time_score * 0.7) + (type_score * 0.3)
else:
final_score = (time_score * 0.65) + (type_score * 0.35)
if breed_info['Size'] in ['Large', 'Giant'] and user_prefs.living_space == 'apartment':
if exercise_time >= 120:
final_score = min(1.0, final_score * 1.2)
# 極端情況的最終調整
if breed_needs.upper() == 'LOW' and exercise_time > breed_level['max'] * 2:
# 低運動需求品種的過度運動顯著降分
final_score *= 0.6
elif breed_needs.upper() == 'VERY HIGH' and exercise_time < breed_level['min'] * 0.5:
# 高運動需求品種運動嚴重不足降分
final_score *= 0.5
return max(0.1, min(1.0, final_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": -0.20, # 大型犬的美容工作量顯著增加
"medium": -0.10,
"high": -0.05
},
"Large": {
"low": -0.15,
"medium": -0.05,
"high": 0
},
"Medium": {
"low": -0.10,
"medium": -0.05,
"high": 0
},
"Small": {
"low": -0.05,
"medium": 0,
"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，更平衡的經驗等級影響
改進重點：
1. 提高初學者的基礎分數
2. 縮小經驗等級間的差距
3. 保持適度的區分度
"""
# 基礎分數矩陣
base_scores = {
"High": {
"beginner": 0.55, # 提高起始分，讓新手也有機會
"intermediate": 0.80, # 中等經驗用戶可能有不錯的勝任能力
"advanced": 0.95 # 資深者幾乎完全勝任
},
"Moderate": {
"beginner": 0.65, # 適中難度對新手更友善
"intermediate": 0.85, # 中等經驗用戶相當適合
"advanced": 0.90 # 資深者完全勝任
},
"Low": {
"beginner": 0.85, # 新手友善品種維持高分
"intermediate": 0.90, # 中等經驗用戶幾乎完全勝任
"advanced": 0.90 # 資深者完全勝任
}
}
# 取得基礎分數
score = base_scores.get(care_level, base_scores["Moderate"])[user_experience]
# 性格評估的權重
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，降低懲罰程度
if user_experience == "beginner":
difficult_traits = {
'stubborn': -0.15,
'independent': -0.12,
'dominant': -0.12,
'strong-willed': -0.10,
'protective': -0.10,
'aloof': -0.08,
'energetic': -0.08,
'aggressive': -0.20
}
easy_traits = {
'gentle': 0.08,
'friendly': 0.08,
'eager to please': 0.10,
'patient': 0.08,
'adaptable': 0.08,
'calm': 0.08
}
# 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，降低懲罰程度
if 'terrier' in temperament_lower:
temperament_adjustments -= 0.10 # 降低懲罰
elif 'working' in temperament_lower:
temperament_adjustments -= 0.12
elif 'guard' in temperament_lower:
temperament_adjustments -= 0.12
# 中等經驗用戶
elif user_experience == "intermediate":
moderate_traits = {
'stubborn': -0.08,
'independent': -0.05,
'intelligent': 0.10,
'athletic': 0.08,
'versatile': 0.08,
'protective': -0.05
}
for trait, adjustment in moderate_traits.items():
if trait in temperament_lower:
temperament_adjustments += adjustment
else: # advanced
advanced_traits = {
'stubborn': 0.05,
'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
# 確保最終分數範圍合理
final_score = max(0.15, min(1.0, score + temperament_adjustments))
return final_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
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': -0.20, # 髖關節發育不良，影響生活品質
'heart disease': -0.15, # 心臟疾病，需要長期治療
'progressive retinal atrophy': -0.15, # 進行性視網膜萎縮，導致失明
'bloat': -0.18, # 胃扭轉，致命風險
'epilepsy': -0.15, # 癲癇，需要長期藥物控制
'degenerative myelopathy': -0.15, # 脊髓退化，影響行動能力
'von willebrand disease': -0.12 # 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_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，很多人棄養就是因為叫聲
"""
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))
# 1. 計算基礎分數
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)}")
# 計算所有基礎分數
scores = {
'space': calculate_space_score(
breed_info['Size'],
user_prefs.living_space,
user_prefs.yard_access != 'no_yard',
breed_info.get('Exercise Needs', 'Moderate')
),
'exercise': calculate_exercise_score(
breed_info.get('Exercise Needs', 'Moderate'),
user_prefs.exercise_time,
user_prefs.exercise_type,
breed_info['Size'],
user_prefs.living_space
),
'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', ''),
user_prefs
),
'noise': calculate_noise_score(
breed_info.get('Breed', ''),
user_prefs
)
}
final_score = calculate_breed_compatibility_score(
scores=scores,
user_prefs=user_prefs,
breed_info=breed_info
)
# 計算環境適應性加成
adaptability_bonus = calculate_environmental_fit(breed_info, user_prefs)
if (breed_info.get('Exercise Needs') == "Very High" and
user_prefs.living_space == "apartment" and
user_prefs.exercise_time < 90):
final_score *= 0.85 # 高運動需求但條件不足的懲罰
# 整合最終分數和加成
combined_score = (final_score * 0.9) + (adaptability_bonus * 0.1)
# 體型過濾
filtered_score = apply_size_filter(
breed_score=combined_score,
user_preference=user_prefs.size_preference,
breed_size=breed_info['Size']
)
final_score = amplify_score_extreme(filtered_score)
# 更新並返回完整的評分結果
scores.update({
'overall': final_score,
'size': breed_info['Size'],
'adaptability_bonus': adaptability_bonus
})
return scores
except Exception as 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())
return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']}
def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -> float:
"""計算品種與環境的適應性加成"""
adaptability_score = 0.0
description = breed_info.get('Description', '').lower()
temperament = breed_info.get('Temperament', '').lower()
# 環境適應性評估
if user_prefs.living_space == 'apartment':
if 'adaptable' in temperament or 'apartment' in description:
adaptability_score += 0.1
if breed_info.get('Size') == 'Small':
adaptability_score += 0.05
elif user_prefs.living_space == 'house_large':
if 'active' in temperament or 'energetic' in description:
adaptability_score += 0.1
# 氣候適應性
if user_prefs.climate in description or user_prefs.climate in temperament:
adaptability_score += 0.05
return min(0.2, adaptability_score)
def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreferences, breed_info: dict) -> float:
"""
1. 運動類型與時間的精確匹配
2. 進階使用者的專業需求
3. 空間利用的實際效果
4. 條件組合的嚴格評估
"""
def evaluate_perfect_conditions():
"""
評估條件匹配度：
1. 運動類型與時間的綜合評估
2. 專業技能需求評估
3. 品種特性評估
"""
perfect_matches = {
'size_match': 0,
'exercise_match': 0,
'experience_match': 0,
'living_condition_match': 0,
'breed_trait_match': 0
}
# 第一部分：運動需求評估
def evaluate_exercise_compatibility():
"""
評估運動需求的匹配度：
1. 時間與強度的合理搭配
2. 不同品種的運動特性
3. 運動類型的適配性
這個函數就像是一個體育教練，需要根據每個"運動員"(狗品種)的特點，
為他們制定合適的訓練計劃。
"""
exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
exercise_time = user_prefs.exercise_time
exercise_type = user_prefs.exercise_type
temperament = breed_info.get('Temperament', '').lower()
description = breed_info.get('Description', '').lower()
# 定義更精確的品種運動特性
breed_exercise_patterns = {
'sprint_type': { # 短跑型犬種，如 Whippet, Saluki
'identifiers': ['fast', 'speed', 'sprint', 'racing', 'coursing', 'sight hound'],
'ideal_exercise': {
'active_training': 1.0, # 完美匹配高強度訓練
'moderate_activity': 0.5, # 持續運動不是最佳選擇
'light_walks': 0.3 # 輕度運動效果很差
},
'time_ranges': {
'ideal': (30, 60), # 最適合的運動時間範圍
'acceptable': (20, 90), # 可以接受的時間範圍
'penalty_start': 90 # 開始給予懲罰的時間點
},
'penalty_rate': 0.8 # 超出範圍時的懲罰係數
},
'endurance_type': { # 耐力型犬種，如 Border Collie
'identifiers': ['herding', 'working', 'tireless', 'energetic', 'stamina', 'athletic'],
'ideal_exercise': {
'active_training': 0.9, # 高強度訓練很好
'moderate_activity': 1.0, # 持續運動是最佳選擇
'light_walks': 0.4 # 輕度運動不足
},
'time_ranges': {
'ideal': (90, 180), # 需要較長的運動時間
'acceptable': (60, 180),
'penalty_start': 60 # 運動時間過短會受罰
},
'penalty_rate': 0.7
},
'moderate_type': { # 一般活動型犬種，如 Labrador
'identifiers': ['friendly', 'playful', 'adaptable', 'versatile', 'companion'],
'ideal_exercise': {
'active_training': 0.8,
'moderate_activity': 1.0,
'light_walks': 0.6
},
'time_ranges': {
'ideal': (60, 120),
'acceptable': (45, 150),
'penalty_start': 150
},
'penalty_rate': 0.6
}
}
def determine_breed_type():
"""改進品種運動類型的判斷，識別工作犬"""
# 優先檢查特殊運動類型的標識符
for breed_type, pattern in breed_exercise_patterns.items():
if any(identifier in temperament or identifier in description
for identifier in pattern['identifiers']):
return breed_type
# 改進：根據運動需求和工作犬特徵進行更細緻的判斷
if (exercise_needs in ['VERY HIGH', 'HIGH'] or
any(trait in temperament.lower() for trait in
['herding', 'working', 'intelligent', 'athletic', 'tireless'])):
if user_prefs.experience_level == 'advanced':
return 'endurance_type' # 優先判定為耐力型
elif exercise_needs == 'LOW':
return 'moderate_type'
return 'moderate_type'
def calculate_time_match(pattern):
"""
計算運動時間的匹配度。
這就像在判斷運動時間是否符合訓練計劃。
"""
ideal_min, ideal_max = pattern['time_ranges']['ideal']
accept_min, accept_max = pattern['time_ranges']['acceptable']
penalty_start = pattern['time_ranges']['penalty_start']
# 在理想範圍內
if ideal_min <= exercise_time <= ideal_max:
return 1.0
# 超出可接受範圍的嚴格懲罰
elif exercise_time < accept_min:
deficit = accept_min - exercise_time
return max(0.2, 1 - (deficit / accept_min) * 1.2)
elif exercise_time > accept_max:
excess = exercise_time - penalty_start
penalty = min(0.8, (excess / penalty_start) * pattern['penalty_rate'])
return max(0.2, 1 - penalty)
# 在可接受範圍但不在理想範圍
else:
if exercise_time < ideal_min:
progress = (exercise_time - accept_min) / (ideal_min - accept_min)
return 0.6 + (0.4 * progress)
else:
remaining = (accept_max - exercise_time) / (accept_max - ideal_max)
return 0.6 + (0.4 * remaining)
def apply_special_adjustments(time_score, type_score, breed_type, pattern):
"""
處理特殊情況，確保運動方式真正符合品種需求。
1. 短跑型犬種的長時間運動懲罰
2. 耐力型犬種的獎勵機制
3. 運動類型匹配的重要性
"""
# 短跑型品種的特殊處理
if breed_type == 'sprint_type':
if exercise_time > pattern['time_ranges']['penalty_start']:
# 加重長時間運動的懲罰
penalty_factor = min(0.8, (exercise_time - pattern['time_ranges']['penalty_start']) / 60)
time_score *= max(0.3, 1 - penalty_factor) # 最低降到0.3
# 運動類型不適合時的額外懲罰
if exercise_type != 'active_training':
type_score *= 0.3 # 更嚴重的懲罰
# 耐力型品種的特殊處理
elif breed_type == 'endurance_type':
if exercise_time < pattern['time_ranges']['penalty_start']:
time_score *= 0.5 # 維持運動不足的懲罰
elif exercise_time >= 150:
if exercise_type in ['active_training', 'moderate_activity']:
time_bonus = min(0.3, (exercise_time - 150) / 150)
time_score = min(1.0, time_score * (1 + time_bonus))
type_score = min(1.0, type_score * 1.2)
# 運動強度不足的懲罰
if exercise_type == 'light_walks':
if exercise_time > 90:
type_score *= 0.4 # 加重懲罰
else:
type_score *= 0.5
return time_score, type_score
# 執行評估流程
breed_type = determine_breed_type()
pattern = breed_exercise_patterns[breed_type]
# 計算基礎分數
time_score = calculate_time_match(pattern)
type_score = pattern['ideal_exercise'].get(exercise_type, 0.5)
# 應用特殊調整
time_score, type_score = apply_special_adjustments(time_score, type_score, breed_type, pattern)
# 根據品種類型決定最終權重
if breed_type == 'sprint_type':
if exercise_time > pattern['time_ranges']['penalty_start']:
# 超時時更重視運動類型的匹配度
return (time_score * 0.3) + (type_score * 0.7)
else:
return (time_score * 0.5) + (type_score * 0.5)
elif breed_type == 'endurance_type':
if exercise_time < pattern['time_ranges']['penalty_start']:
# 時間不足時更重視時間因素
return (time_score * 0.7) + (type_score * 0.3)
else:
return (time_score * 0.6) + (type_score * 0.4)
else:
return (time_score * 0.5) + (type_score * 0.5)
# 第二部分：專業技能需求評估
def evaluate_expertise_requirements():
care_level = breed_info.get('Care Level', 'MODERATE').upper()
temperament = breed_info.get('Temperament', '').lower()
# 定義專業技能要求
expertise_requirements = {
'training_complexity': {
'HIGH': {'beginner': 0.3, 'intermediate': 0.7, 'advanced': 1.0},
'MODERATE': {'beginner': 0.6, 'intermediate': 0.9, 'advanced': 1.0},
'LOW': {'beginner': 0.9, 'intermediate': 0.95, 'advanced': 0.9}
},
'special_traits': {
'working': 0.2, # 工作犬需要額外技能
'herding': 0.2, # 牧羊犬需要特殊訓練
'intelligent': 0.15,# 高智商犬種需要心智刺激
'independent': 0.15,# 獨立性強的需要特殊處理
'protective': 0.1 # 護衛犬需要適當訓練
}
}
# 基礎分數
base_score = expertise_requirements['training_complexity'][care_level][user_prefs.experience_level]
# 特殊特徵評估
trait_penalty = 0
for trait, penalty in expertise_requirements['special_traits'].items():
if trait in temperament:
if user_prefs.experience_level == 'beginner':
trait_penalty += penalty
elif user_prefs.experience_level == 'advanced':
trait_penalty -= penalty * 0.5 # 專家反而因應對特殊特徵而加分
return max(0.2, min(1.0, base_score - trait_penalty))
def evaluate_living_conditions() -> float:
"""
評估生活環境適配性：
1. 降低對大型犬的過度懲罰
2. 增加品種特性評估
3. 提升對適應性的重視度
"""
size = breed_info['Size']
exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
temperament = breed_info.get('Temperament', '').lower()
description = breed_info.get('Description', '').lower()
# 重新定義空間需求矩陣，降低對大型犬的懲罰
space_requirements = {
'apartment': {
'Small': 1.0,
'Medium': 0.8,
'Large': 0.7,
'Giant': 0.6
},
'house_small': {
'Small': 0.9,
'Medium': 1.0,
'Large': 0.8,
'Giant': 0.7
},
'house_large': {
'Small': 0.8,
'Medium': 0.9,
'Large': 1.0,
'Giant': 1.0
}
}
# 基礎空間分數
space_score = space_requirements.get(
user_prefs.living_space,
space_requirements['house_small']
)[size]
# 品種適應性評估
adaptability_bonus = 0
adaptable_traits = ['adaptable', 'calm', 'quiet', 'gentle', 'laid-back']
challenging_traits = ['hyperactive', 'restless', 'requires space']
# 計算適應性加分
if user_prefs.living_space == 'apartment':
for trait in adaptable_traits:
if trait in temperament or trait in description:
adaptability_bonus += 0.1
# 特別處理大型犬的適應性
if size in ['Large', 'Giant']:
apartment_friendly_traits = ['calm', 'gentle', 'quiet']
matched_traits = sum(1 for trait in apartment_friendly_traits
if trait in temperament or trait in description)
if matched_traits > 0:
adaptability_bonus += 0.15 * matched_traits
# 活動空間需求調整，更寬容的評估
if exercise_needs in ['HIGH', 'VERY HIGH']:
if user_prefs.living_space != 'house_large':
space_score *= 0.9 # 從0.8提升到0.9，降低懲罰
# 院子可用性評估，提供更合理的獎勵
yard_scores = {
'no_yard': 0.85, # 從0.7提升到0.85
'shared_yard': 0.92, # 從0.85提升到0.92
'private_yard': 1.0
}
yard_multiplier = yard_scores.get(user_prefs.yard_access, 0.85)
# 根據體型調整院子重要性
if size in ['Large', 'Giant']:
yard_importance = 1.2
elif size == 'Medium':
yard_importance = 1.1
else:
yard_importance = 1.0
# 計算最終分數
final_score = space_score * (1 + adaptability_bonus)
# 應用院子影響
if user_prefs.yard_access != 'no_yard':
yard_bonus = (yard_multiplier - 1) * yard_importance
final_score = min(1.0, final_score + yard_bonus)
# 確保分數在合理範圍內，但提供更高的基礎分數
return max(0.4, min(1.0, final_score))
# 第四部分：品種特性評估
def evaluate_breed_traits():
temperament = breed_info.get('Temperament', '').lower()
description = breed_info.get('Description', '').lower()
trait_scores = []
# 評估性格特徵
if user_prefs.has_children:
if 'good with children' in description:
trait_scores.append(1.0)
elif 'patient' in temperament or 'gentle' in temperament:
trait_scores.append(0.8)
else:
trait_scores.append(0.5)
# 評估適應性
adaptability_keywords = ['adaptable', 'versatile', 'flexible']
if any(keyword in temperament for keyword in adaptability_keywords):
trait_scores.append(1.0)
else:
trait_scores.append(0.7)
return sum(trait_scores) / len(trait_scores) if trait_scores else 0.7
# 計算各項匹配分數
perfect_matches['exercise_match'] = evaluate_exercise_compatibility()
perfect_matches['experience_match'] = evaluate_expertise_requirements()
perfect_matches['living_condition_match'] = evaluate_living_conditions()
perfect_matches['size_match'] = evaluate_living_conditions() # 共用生活環境評估
perfect_matches['breed_trait_match'] = evaluate_breed_traits()
return perfect_matches
def calculate_weights() -> dict:
"""
動態計算評分權重：
1. 極端情況的權重調整
2. 使用者條件的協同效應
3. 品種特性的影響
Returns:
dict: 包含各評分項目權重的字典
"""
# 定義基礎權重 - 提供更合理的起始分配
base_weights = {
'space': 0.25, # 提升空間權重，因為這是最基本的需求
'exercise': 0.25, # 運動需求同樣重要
'experience': 0.20, # 保持經驗的重要性
'grooming': 0.10, # 稍微降低美容需求的權重
'noise': 0.10, # 維持噪音評估的權重
'health': 0.10 # 維持健康評估的權重
}
def analyze_condition_extremity() -> dict:
"""
評估使用者條件的極端程度，這影響權重的動態調整。
根據條件的極端程度返回相應的調整建議。
"""
extremities = {}
# 運動時間評估 - 更細緻的分級
if user_prefs.exercise_time <= 30:
extremities['exercise'] = ('extremely_low', 0.8)
elif user_prefs.exercise_time <= 60:
extremities['exercise'] = ('low', 0.6)
elif user_prefs.exercise_time >= 180:
extremities['exercise'] = ('extremely_high', 0.8)
elif user_prefs.exercise_time >= 120:
extremities['exercise'] = ('high', 0.6)
else:
extremities['exercise'] = ('moderate', 0.3)
# 空間限制評估 - 更合理的空間評估
space_extremity = {
'apartment': ('restricted', 0.7),
'house_small': ('moderate', 0.5),
'house_large': ('spacious', 0.3)
}
extremities['space'] = space_extremity.get(user_prefs.living_space, ('moderate', 0.5))
# 經驗水平評估 - 保持原有的評估邏輯
experience_extremity = {
'beginner': ('low', 0.7),
'intermediate': ('moderate', 0.4),
'advanced': ('high', 0.6)
}
extremities['experience'] = experience_extremity.get(user_prefs.experience_level, ('moderate', 0.5))
return extremities
def calculate_weight_adjustments(extremities: dict) -> dict:
"""
根據極端程度計算權重調整，特別注意條件組合的影響。
"""
adjustments = {}
temperament = breed_info.get('Temperament', '').lower()
is_working_dog = any(trait in temperament
for trait in ['herding', 'working', 'intelligent', 'tireless'])
# 空間權重調整
if extremities['space'][0] == 'restricted':
if extremities['exercise'][0] in ['high', 'extremely_high']:
adjustments['space'] = 1.3
adjustments['exercise'] = 2.3
else:
adjustments['space'] = 1.6
adjustments['noise'] = 1.5
# 運動需求權重調整
if extremities['exercise'][0] in ['extremely_high', 'extremely_low']:
base_adjustment = 2.0
if extremities['exercise'][0] == 'extremely_high':
if is_working_dog:
base_adjustment = 2.3
adjustments['exercise'] = base_adjustment
# 經驗需求權重調整
if extremities['experience'][0] == 'low':
adjustments['experience'] = 1.8
if breed_info.get('Care Level') == 'HIGH':
adjustments['experience'] = 2.0
elif extremities['experience'][0] == 'high':
if is_working_dog:
adjustments['experience'] = 1.8 # 從2.5降低到1.8
# 特殊組合的處理
def adjust_for_combinations():
if (extremities['space'][0] == 'restricted' and
extremities['exercise'][0] in ['high', 'extremely_high']):
# 適度降低極端組合的影響
adjustments['space'] = adjustments.get('space', 1.0) * 1.2
adjustments['exercise'] = adjustments.get('exercise', 1.0) * 1.2
# 理想組合的獎勵
if (extremities['experience'][0] == 'high' and
extremities['space'][0] == 'spacious' and
extremities['exercise'][0] in ['high', 'extremely_high'] and
is_working_dog):
adjustments['exercise'] = adjustments.get('exercise', 1.0) * 1.3
adjustments['experience'] = adjustments.get('experience', 1.0) * 1.3
adjust_for_combinations()
return adjustments
# 獲取條件極端度
extremities = analyze_condition_extremity()
# 計算權重調整
weight_adjustments = calculate_weight_adjustments(extremities)
# 應用權重調整，確保總和為1
final_weights = base_weights.copy()
for key, adjustment in weight_adjustments.items():
if key in final_weights:
final_weights[key] *= adjustment
# 正規化權重
total_weight = sum(final_weights.values())
normalized_weights = {k: v/total_weight for k, v in final_weights.items()}
return normalized_weights
def calculate_weight_adjustments(extremities):
"""
1. 高運動量時對耐力型犬種的偏好
2. 專家級別對工作犬種的偏好
3. 條件組合的整體評估
"""
adjustments = {}
temperament = breed_info.get('Temperament', '').lower()
is_working_dog = any(trait in temperament
for trait in ['herding', 'working', 'intelligent', 'tireless'])
# 空間權重調整邏輯保持不變
if extremities['space'][0] == 'highly_restricted':
if extremities['exercise'][0] in ['high', 'extremely_high']:
adjustments['space'] = 1.8 # 降低空間限制的權重
adjustments['exercise'] = 2.5 # 提高運動能力的權重
else:
adjustments['space'] = 2.5
adjustments['noise'] = 2.0
elif extremities['space'][0] == 'restricted':
adjustments['space'] = 1.8
adjustments['noise'] = 1.5
elif extremities['space'][0] == 'spacious':
adjustments['space'] = 0.8
adjustments['exercise'] = 1.4
# 改進運動需求權重調整
if extremities['exercise'][0] in ['high', 'extremely_high']:
# 提高運動量高時的基礎分數
base_exercise_adjustment = 2.2
if user_prefs.living_space == 'apartment':
base_exercise_adjustment = 2.5 # 特別獎勵公寓住戶的高運動量
adjustments['exercise'] = base_exercise_adjustment
if extremities['exercise'][0] in ['extremely_low', 'extremely_high']:
base_adjustment = 2.5
if extremities['exercise'][0] == 'extremely_high':
if is_working_dog:
base_adjustment = 3.0 # 工作犬在高運動量時獲得更高權重
adjustments['exercise'] = base_adjustment
elif extremities['exercise'][0] in ['low', 'high']:
adjustments['exercise'] = 1.8
# 改進經驗需求權重調整
if extremities['experience'][0] == 'low':
adjustments['experience'] = 2.2
if breed_info.get('Care Level') == 'HIGH':
adjustments['experience'] = 2.5
elif extremities['experience'][0] == 'high':
if is_working_dog:
adjustments['experience'] = 2.5
if extremities['exercise'][0] in ['high', 'extremely_high']:
adjustments['experience'] = 2.8
else:
adjustments['experience'] = 1.8
# 綜合條件影響
def adjust_for_combinations():
# 保持原有的基礎邏輯
if (extremities['space'][0] == 'highly_restricted' and
extremities['exercise'][0] in ['high', 'extremely_high']):
adjustments['space'] = adjustments.get('space', 1.0) * 1.3
adjustments['exercise'] = adjustments.get('exercise', 1.0) * 1.3
# 專家 + 大空間 + 高運動量 + 工作犬的組合
if (extremities['experience'][0] == 'high' and
extremities['space'][0] == 'spacious' and
extremities['exercise'][0] in ['high', 'extremely_high'] and
is_working_dog):
adjustments['exercise'] = adjustments.get('exercise', 1.0) * 1.4
adjustments['experience'] = adjustments.get('experience', 1.0) * 1.4
if extremities['space'][0] == 'spacious':
for key in ['grooming', 'health', 'noise']:
if key not in adjustments:
adjustments[key] = 1.2
def ensure_minimum_score(score):
if all([
extremities['exercise'][0] in ['high', 'extremely_high'],
breed_matches_exercise_needs(), # 檢查品種是否適合該運動量
score < 0.85
]):
return 0.85
return score
adjust_for_combinations()
return adjustments
# 獲取條件極端度
extremities = analyze_condition_extremity()
# 計算權重調整
weight_adjustments = calculate_weight_adjustments(extremities)
# 應用權重調整
final_weights = base_weights.copy()
for key, adjustment in weight_adjustments.items():
if key in final_weights:
final_weights[key] *= adjustment
return final_weights
def apply_special_case_adjustments(score: float) -> float:
"""
處理特殊情況和極端案例的評分調整：
1. 條件組合的協同效應
2. 品種特性的獨特需求
3. 極端情況的合理處理
Parameters:
score: 初始評分
Returns:
float: 調整後的評分（0.2-1.0之間）
"""
severity_multiplier = 1.0
def evaluate_spatial_exercise_combination() -> float:
"""
評估空間與運動需求的組合效應。
這個函數不再過分懲罰大型犬，而是更多地考慮品種的實際特性。
就像評估一個運動員是否適合在特定場地訓練一樣，我們需要考慮
場地大小和運動需求的整體匹配度。
"""
multiplier = 1.0
if user_prefs.living_space == 'apartment':
temperament = breed_info.get('Temperament', '').lower()
description = breed_info.get('Description', '').lower()
# 檢查品種是否有利於公寓生活的特徵
apartment_friendly = any(trait in temperament or trait in description
for trait in ['calm', 'adaptable', 'quiet'])
# 大型犬的特殊處理
if breed_info['Size'] in ['Large', 'Giant']:
if apartment_friendly:
multiplier *= 0.85
else:
multiplier *= 0.75
# 檢查運動需求的匹配度
exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
exercise_time = user_prefs.exercise_time
if exercise_needs in ['HIGH', 'VERY HIGH']:
if exercise_time >= 120:
multiplier *= 1.1
return multiplier
def evaluate_experience_combination() -> float:
"""
評估經驗需求的複合影響。
這個函數就像是評估一個工作崗位與應聘者經驗的匹配度，
需要綜合考慮工作難度和應聘者能力。
"""
multiplier = 1.0
temperament = breed_info.get('Temperament', '').lower()
care_level = breed_info.get('Care Level', 'MODERATE')
# 新手飼主的特殊考慮，更寬容的評估標準
if user_prefs.experience_level == 'beginner':
if care_level == 'HIGH':
if user_prefs.has_children:
multiplier *= 0.7
else:
multiplier *= 0.8
# 性格特徵影響，降低懲罰程度
challenging_traits = {
'stubborn': -0.10,
'independent': -0.08,
'dominant': -0.08,
'protective': -0.06,
'aggressive': -0.15
}
for trait, penalty in challenging_traits.items():
if trait in temperament:
multiplier *= (1 + penalty)
return multiplier
def evaluate_breed_specific_requirements() -> float:
"""
評估品種特定需求。
"""
multiplier = 1.0
exercise_time = user_prefs.exercise_time
exercise_type = user_prefs.exercise_type
# 檢查品種特性
temperament = breed_info.get('Temperament', '').lower()
description = breed_info.get('Description', '').lower()
exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
# 運動需求匹配度評估，更合理的標準
if exercise_needs == 'LOW':
if exercise_time > 120:
multiplier *= 0.85
elif exercise_needs == 'VERY HIGH':
if exercise_time < 60:
multiplier *= 0.7
# 特殊品種類型的考慮
if 'sprint' in temperament:
if exercise_time > 120 and exercise_type != 'active_training':
multiplier *= 0.85
if any(trait in temperament for trait in ['working', 'herding']):
if exercise_time < 90 or exercise_type == 'light_walks':
multiplier *= 0.8
return multiplier
# 計算各項調整
space_exercise_mult = evaluate_spatial_exercise_combination()
experience_mult = evaluate_experience_combination()
breed_specific_mult = evaluate_breed_specific_requirements()
# 整合所有調整因素
severity_multiplier *= space_exercise_mult
severity_multiplier *= experience_mult
severity_multiplier *= breed_specific_mult
# 應用最終調整，確保分數在合理範圍內
final_score = score * severity_multiplier
return max(0.2, min(1.0, final_score))
def calculate_base_score(scores: dict, weights: dict) -> float:
"""
計算基礎評分分數
這個函數使用了改進後的評分邏輯：
1. 降低關鍵指標的最低門檻，使系統更包容
2. 引入非線性評分曲線，讓分數分布更合理
3. 優化多重條件失敗的處理方式
4. 加強對品種特性的考慮
Parameters:
scores: 包含各項評分的字典
weights: 包含各項權重的字典
Returns:
float: 0.2到1.0之間的基礎分數
"""
# 重新定義關鍵指標閾值，提供更寬容的評分標準
critical_thresholds = {
'space': 0.35,
'exercise': 0.35,
'experience': 0.5,
'noise': 0.5
}
# 評估關鍵指標失敗情況
def evaluate_critical_failures() -> list:
"""
評估關鍵指標的失敗情況，但採用更寬容的標準。
根據品種特性動態調整失敗判定。
"""
failures = []
temperament = breed_info.get('Temperament', '').lower()
for metric, threshold in critical_thresholds.items():
if scores[metric] < threshold:
# 特殊情況處理：適應性強的品種可以有更低的空間要求
if metric == 'space' and any(trait in temperament
for trait in ['adaptable', 'calm', 'apartment']):
if scores[metric] >= threshold - 0.1:
continue
# 運動需求的特殊處理
elif metric == 'exercise':
exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
if exercise_needs == 'LOW' and scores[metric] >= threshold - 0.1:
continue
failures.append((metric, scores[metric]))
return failures
# 計算基礎分數
def calculate_weighted_score() -> float:
"""
計算加權分數，使用非線性函數使分數分布更合理。
"""
weighted_scores = []
for key, score in scores.items():
if key in weights:
# 使用sigmoid函數使分數曲線更平滑
adjusted_score = 1 / (1 + math.exp(-10 * (score - 0.5)))
weighted_scores.append(adjusted_score * weights[key])
return sum(weighted_scores)
# 處理臨界失敗情況
critical_failures = evaluate_critical_failures()
base_score = calculate_weighted_score()
if critical_failures:
# 分離空間和運動相關的懲罰
space_exercise_penalty = 0
other_penalty = 0
for metric, score in critical_failures:
if metric in ['space', 'exercise']:
# 降低空間和運動失敗的懲罰程度
penalty = (critical_thresholds[metric] - score) * 0.08
space_exercise_penalty += penalty
else:
# 其他失敗的懲罰保持較高
penalty = (critical_thresholds[metric] - score) * 0.20
other_penalty += penalty
# 計算總懲罰，但使用更溫和的方式
total_penalty = (space_exercise_penalty + other_penalty) / 2
base_score *= (1 - total_penalty)
# 多重失敗的處理更寬容
if len(critical_failures) > 1:
# 從0.98提升到0.99，降低多重失敗的疊加懲罰
base_score *= (0.99 ** (len(critical_failures) - 1))
# 品種特性加分
def apply_breed_bonus() -> float:
"""
根據品種特性提供額外加分，
特別是對於在特定環境下表現良好的品種。
"""
bonus = 0
temperament = breed_info.get('Temperament', '').lower()
description = breed_info.get('Description', '').lower()
# 適應性加分
adaptability_traits = ['adaptable', 'versatile', 'easy-going']
if any(trait in temperament for trait in adaptability_traits):
bonus += 0.05
# 公寓適應性加分
if user_prefs.living_space == 'apartment':
apartment_traits = ['calm', 'quiet', 'good for apartments']
if any(trait in temperament or trait in description for trait in apartment_traits):
bonus += 0.05
return min(0.1, bonus) # 限制最大加分
# 應用品種特性加分
breed_bonus = apply_breed_bonus()
base_score = min(1.0, base_score * (1 + breed_bonus))
# 確保最終分數在合理範圍內
return max(0.2, min(1.0, base_score))
def evaluate_condition_interactions(scores: dict) -> float:
"""
評估不同條件間的相互影響，更寬容地處理極端組合
"""
interaction_penalty = 1.0
# 只保留最基本的經驗相關評估
if user_prefs.experience_level == 'beginner':
if breed_info.get('Care Level') == 'HIGH':
interaction_penalty *= 0.95
# 運動時間與類型的基本互動也降低懲罰程度
exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper()
if exercise_needs == 'VERY HIGH' and user_prefs.exercise_type == 'light_walks':
interaction_penalty *= 0.95
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。
"""
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
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 = calculate_base_score(scores, normalized_weights)
# 評估條件互動
interaction_multiplier = evaluate_condition_interactions(scores)
# 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
preliminary_score = base_score * interaction_multiplier * perfect_bonus
# 應用品種特定調整
final_score = apply_breed_specific_adjustments(preliminary_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
return min(max_possible_score, max(min_possible_score, final_score))
def amplify_score_extreme(score: float) -> float:
"""
Parameters:
score: 原始評分（0-1之間的浮點數）
Returns:
float: 調整後的評分（0-1之間的浮點數）
"""
def smooth_curve(x: float, steepness: float = 12) -> float:
"""創建平滑的S型曲線用於分數轉換"""
import math
return 1 / (1 + math.exp(-steepness * (x - 0.5)))
# 90-100分的轉換（極佳匹配）
if score >= 0.90:
position = (score - 0.90) / 0.10
return 0.96 + (position * 0.04)
# 80-90分的轉換（優秀匹配）
elif score >= 0.80:
position = (score - 0.80) / 0.10
return 0.90 + (position * 0.06)
# 70-80分的轉換（良好匹配）
elif score >= 0.70:
position = (score - 0.70) / 0.10
return 0.82 + (position * 0.08)
# 50-70分的轉換（可接受匹配）
elif score >= 0.50:
position = (score - 0.50) / 0.20
return 0.75 + (smooth_curve(position) * 0.07)
# 50分以下的轉換（較差匹配）
else:
position = score / 0.50
return 0.70 + (smooth_curve(position) * 0.05)
return round(min(1.0, max(0.0, score)), 4) |