import torch import numpy as np from typing import List, Dict, Tuple, Optional from dataclasses import dataclass from breed_health_info import breed_health_info from breed_noise_info import breed_noise_info from dog_database import dog_data from scoring_calculation_system import UserPreferences from sentence_transformers import SentenceTransformer, util class SmartBreedMatcher: def __init__(self, dog_data: List[Tuple]): self.dog_data = dog_data self.model = SentenceTransformer('paraphrase-MiniLM-L6-v2') def _categorize_breeds(self) -> Dict: """自動將狗品種分類""" categories = { 'working_dogs': [], 'herding_dogs': [], 'hunting_dogs': [], 'companion_dogs': [], 'guard_dogs': [] } for breed_info in self.dog_data: description = breed_info[9].lower() temperament = breed_info[4].lower() # 根據描述和性格特徵自動分類 if any(word in description for word in ['herding', 'shepherd', 'cattle', 'flock']): categories['herding_dogs'].append(breed_info[1]) elif any(word in description for word in ['hunting', 'hunt', 'retriever', 'pointer']): categories['hunting_dogs'].append(breed_info[1]) elif any(word in description for word in ['companion', 'toy', 'family', 'lap']): categories['companion_dogs'].append(breed_info[1]) elif any(word in description for word in ['guard', 'protection', 'watchdog']): categories['guard_dogs'].append(breed_info[1]) elif any(word in description for word in ['working', 'draft', 'cart']): categories['working_dogs'].append(breed_info[1]) return categories def find_similar_breeds(self, breed_name: str, top_n: int = 5) -> List[Tuple[str, float]]: """找出與指定品種最相似的其他品種""" target_breed = next((breed for breed in self.dog_data if breed[1] == breed_name), None) if not target_breed: return [] # 獲取目標品種的特徵 target_features = { 'breed_name': target_breed[1], # 添加品種名稱 'size': target_breed[2], 'temperament': target_breed[4], 'exercise': target_breed[7], 'description': target_breed[9] } similarities = [] for breed in self.dog_data: if breed[1] != breed_name: breed_features = { 'breed_name': breed[1], # 添加品種名稱 'size': breed[2], 'temperament': breed[4], 'exercise': breed[7], 'description': breed[9] } similarity_score = self._calculate_breed_similarity(target_features, breed_features) similarities.append((breed[1], similarity_score)) return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_n] def _calculate_breed_similarity(self, breed1_features: Dict, breed2_features: Dict) -> float: """計算兩個品種之間的相似度，包含健康因素""" # 計算描述文本的相似度 desc1_embedding = self.model.encode(breed1_features['description']) desc2_embedding = self.model.encode(breed2_features['description']) description_similarity = float(util.pytorch_cos_sim(desc1_embedding, desc2_embedding)) # 基本特徵相似度 size_similarity = 1.0 if breed1_features['size'] == breed2_features['size'] else 0.5 exercise_similarity = 1.0 if breed1_features['exercise'] == breed2_features['exercise'] else 0.5 # 性格相似度 temp1_embedding = self.model.encode(breed1_features['temperament']) temp2_embedding = self.model.encode(breed2_features['temperament']) temperament_similarity = float(util.pytorch_cos_sim(temp1_embedding, temp2_embedding)) # 健康分數相似度 health_score1 = self._calculate_health_score(breed1_features['breed_name']) health_score2 = self._calculate_health_score(breed2_features['breed_name']) health_similarity = 1.0 - abs(health_score1 - health_score2) # 加權計算 weights = { 'description': 0.3, 'temperament': 0.25, 'exercise': 0.15, 'size': 0.1, 'health': 0.2 } final_similarity = ( description_similarity * weights['description'] + temperament_similarity * weights['temperament'] + exercise_similarity * weights['exercise'] + size_similarity * weights['size'] + health_similarity * weights['health'] ) return final_similarity def _calculate_breed_similarity(self, breed1_features: Dict, breed2_features: Dict) -> float: """計算兩個品種之間的相似度，包含健康和噪音因素""" # 計算描述文本的相似度 desc1_embedding = self.model.encode(breed1_features['description']) desc2_embedding = self.model.encode(breed2_features['description']) description_similarity = float(util.pytorch_cos_sim(desc1_embedding, desc2_embedding)) # 基本特徵相似度 size_similarity = 1.0 if breed1_features['size'] == breed2_features['size'] else 0.5 exercise_similarity = 1.0 if breed1_features['exercise'] == breed2_features['exercise'] else 0.5 # 性格相似度 temp1_embedding = self.model.encode(breed1_features['temperament']) temp2_embedding = self.model.encode(breed2_features['temperament']) temperament_similarity = float(util.pytorch_cos_sim(temp1_embedding, temp2_embedding)) # 健康分數相似度 health_score1 = self._calculate_health_score(breed1_features['breed_name']) health_score2 = self._calculate_health_score(breed2_features['breed_name']) health_similarity = 1.0 - abs(health_score1 - health_score2) # 噪音水平相似度 noise_similarity = self._calculate_noise_similarity( breed1_features['breed_name'], breed2_features['breed_name'] ) # 加權計算 weights = { 'description': 0.25, 'temperament': 0.20, 'exercise': 0.15, 'size': 0.10, 'health': 0.15, 'noise': 0.15 } final_similarity = ( description_similarity * weights['description'] + temperament_similarity * weights['temperament'] + exercise_similarity * weights['exercise'] + size_similarity * weights['size'] + health_similarity * weights['health'] + noise_similarity * weights['noise'] ) return final_similarity def _calculate_final_scores(self, breed_name: str, base_scores: Dict, smart_score: float, is_preferred: bool, similarity_score: float = 0.0) -> Dict: """ 計算最終分數，包含基礎分數和獎勵分數 Args: breed_name: 品種名稱 base_scores: 基礎評分 (空間、運動等) smart_score: 智能匹配分數 is_preferred: 是否為用戶指定品種 similarity_score: 與指定品種的相似度 (0-1) """ # 基礎權重 weights = { 'base': 0.6, # 基礎分數權重 'smart': 0.25, # 智能匹配權重 'bonus': 0.15 # 獎勵分數權重 } # 計算基礎分數 base_score = base_scores.get('overall', 0.7) # 計算獎勵分數 bonus_score = 0.0 if is_preferred: # 用戶指定品種獲得最高獎勵 bonus_score = 0.95 elif similarity_score > 0: # 相似品種獲得部分獎勵，但不超過80%的最高獎勵 bonus_score = min(0.8, similarity_score) * 0.95 # 計算最終分數 final_score = ( base_score * weights['base'] + smart_score * weights['smart'] + bonus_score * weights['bonus'] ) # 更新各項分數 scores = base_scores.copy() # 如果是用戶指定品種，稍微提升各項基礎分數，但保持合理範圍 if is_preferred: for key in scores: if key != 'overall': scores[key] = min(1.0, scores[key] * 1.1) # 最多提升10% # 為相似品種調整分數 elif similarity_score > 0: boost_factor = 1.0 + (similarity_score * 0.05) # 最多提升5% for key in scores: if key != 'overall': scores[key] = min(0.95, scores[key] * boost_factor) # 確保不超過95% return { 'final_score': round(final_score, 4), 'base_score': round(base_score, 4), 'bonus_score': round(bonus_score, 4), 'scores': {k: round(v, 4) for k, v in scores.items()} } def _calculate_health_score(self, breed_name: str) -> float: """計算品種的健康分數""" 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 severe_conditions = [ 'cancer', 'cardiomyopathy', 'epilepsy', 'dysplasia', 'bloat', 'progressive', 'syndrome' ] # 中等健康問題 moderate_conditions = [ 'allergies', 'infections', 'thyroid', 'luxation', 'skin problems', 'ear' ] 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) health_score = 1.0 health_score -= (severe_count * 0.1) health_score -= (moderate_count * 0.05) # 特殊條件調整（根據用戶偏好） if hasattr(self, 'user_preferences'): if self.user_preferences.has_children: if 'requires frequent' in health_notes or 'regular monitoring' in health_notes: health_score *= 0.9 if self.user_preferences.health_sensitivity == 'high': health_score *= 0.9 return max(0.3, min(1.0, health_score)) def _calculate_noise_similarity(self, breed1: str, breed2: str) -> float: """計算兩個品種的噪音相似度""" noise_levels = { 'Low': 1, 'Moderate': 2, 'High': 3, 'Unknown': 2 # 默認為中等 } noise1 = breed_noise_info.get(breed1, {}).get('noise_level', 'Unknown') noise2 = breed_noise_info.get(breed2, {}).get('noise_level', 'Unknown') # 獲取數值級別 level1 = noise_levels.get(noise1, 2) level2 = noise_levels.get(noise2, 2) # 計算差異並歸一化 difference = abs(level1 - level2) similarity = 1.0 - (difference / 2) # 最大差異是2，所以除以2來歸一化 return similarity def match_user_preference(self, description: str, top_n: int = 10) -> List[Dict]: """根據用戶描述匹配最適合的品種""" preferred_breed = self._detect_breed_preference(description) matches = [] if preferred_breed: similar_breeds = self.find_similar_breeds(preferred_breed, top_n=top_n) # 首先添加偏好品種 breed_info = next((breed for breed in self.dog_data if breed[1] == preferred_breed), None) if breed_info: health_score = self._calculate_health_score(preferred_breed) noise_info = breed_noise_info.get(preferred_breed, { "noise_level": "Unknown", "noise_notes": "No noise information available" }) # 偏好品種必定是最高分 matches.append({ 'breed': preferred_breed, 'score': 1.0, 'is_preferred': True, 'similarity': 1.0, 'health_score': health_score, 'noise_level': noise_info['noise_level'], 'reason': "Directly matched your preferred breed" }) # 添加相似品種 for breed_name, similarity in similar_breeds: if breed_name != preferred_breed: health_score = self._calculate_health_score(breed_name) noise_info = breed_noise_info.get(breed_name, { "noise_level": "Unknown", "noise_notes": "No noise information available" }) # 調整相似品種分數計算 base_similarity = similarity * 0.6 health_factor = health_score * 0.2 noise_factor = self._calculate_noise_similarity(preferred_breed, breed_name) * 0.2 # 確保相似品種分數不會超過偏好品種 final_score = min(0.95, base_similarity + health_factor + noise_factor) matches.append({ 'breed': breed_name, 'score': final_score, 'is_preferred': False, 'similarity': similarity, 'health_score': health_score, 'noise_level': noise_info['noise_level'], 'reason': f"Similar to {preferred_breed} in characteristics, health profile, and noise level" }) else: matches = self._general_matching(description, top_n) return sorted(matches, key=lambda x: (-int(x.get('is_preferred', False)), -x['score'], # 降序排列 x['breed']))[:top_n] def _detect_breed_preference(self, description: str) -> Optional[str]: """檢測用戶是否提到特定品種""" description_lower = description.lower() for breed_info in self.dog_data: breed_name = breed_info[1] normalized_breed = breed_name.lower().replace('_', ' ') if any(phrase in description_lower for phrase in [ f"love {normalized_breed}", f"like {normalized_breed}", f"prefer {normalized_breed}", f"want {normalized_breed}", normalized_breed ]): return breed_name return None