# %%writefile multi_head_scorer.py 
import numpy as np
import json
from typing import Dict, List, Tuple, Optional, Any, Set
from dataclasses import dataclass, field
from abc import ABC, abstractmethod
import traceback
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from dog_database import get_dog_description
from breed_health_info import breed_health_info
from breed_noise_info import breed_noise_info
from query_understanding import QueryDimensions
from constraint_manager import FilterResult
from dynamic_weight_calculator import DynamicWeightCalculator, WeightAllocationResult
from adaptive_score_distribution import AdaptiveScoreDistribution, DistributionResult
from dimension_score_calculator import DimensionScoreCalculator

@dataclass
class DimensionalScores:
    """多維度評分結果"""
    semantic_scores: Dict[str, float] = field(default_factory=dict)
    attribute_scores: Dict[str, float] = field(default_factory=dict)
    fused_scores: Dict[str, float] = field(default_factory=dict)
    bidirectional_scores: Dict[str, float] = field(default_factory=dict)
    confidence_weights: Dict[str, float] = field(default_factory=dict)

@dataclass
class BreedScore:
    """品種總體評分結果"""
    breed_name: str
    final_score: float
    dimensional_breakdown: Dict[str, float] = field(default_factory=dict)
    semantic_component: float = 0.0
    attribute_component: float = 0.0
    bidirectional_bonus: float = 0.0
    confidence_score: float = 1.0
    explanation: Dict[str, Any] = field(default_factory=dict)

class ScoringHead(ABC):
    """抽象評分頭基類"""

    @abstractmethod
    def score_dimension(self, breed_info: Dict[str, Any],
                       dimensions: QueryDimensions,
                       dimension_type: str) -> float:
        """為特定維度評分"""
        pass

class SemanticScoringHead(ScoringHead):
    """語義評分頭"""

    def __init__(self, sbert_model: Optional[SentenceTransformer] = None):
        self.sbert_model = sbert_model
        self.dimension_embeddings = {}
        if self.sbert_model:
            self._build_dimension_embeddings()

    def _build_dimension_embeddings(self):
        """建立維度模板嵌入"""
        dimension_templates = {
            'spatial_apartment': "small apartment living, limited space, no yard, urban environment",
            'spatial_house': "house with yard, outdoor space, suburban living, large property",
            'activity_low': "low energy, minimal exercise needs, calm lifestyle, indoor activities",
            'activity_moderate': "moderate exercise, daily walks, balanced activity level",
            'activity_high': "high energy, vigorous exercise, outdoor sports, active lifestyle",
            'noise_low': "quiet, rarely barks, peaceful, suitable for noise-sensitive environments",
            'noise_moderate': "moderate barking, occasional vocalizations, average noise level",
            'noise_high': "vocal, frequent barking, alert dog, comfortable with noise",
            'size_small': "small compact breed, easy to handle, portable size",
            'size_medium': "medium sized dog, balanced proportions, moderate size",
            'size_large': "large impressive dog, substantial presence, bigger breed",
            'family_children': "child-friendly, gentle with kids, family-oriented, safe around children",
            'family_elderly': "calm companion, gentle nature, suitable for seniors, low maintenance",
            'maintenance_low': "low grooming needs, minimal care requirements, easy maintenance",
            'maintenance_moderate': "regular grooming, moderate care needs, standard maintenance",
            'maintenance_high': "high grooming requirements, professional care, intensive maintenance"
        }

        for key, template in dimension_templates.items():
            if self.sbert_model:
                embedding = self.sbert_model.encode(template, convert_to_tensor=False)
                self.dimension_embeddings[key] = embedding

    def score_dimension(self, breed_info: Dict[str, Any],
                       dimensions: QueryDimensions,
                       dimension_type: str) -> float:
        """語義維度評分"""
        if not self.sbert_model or dimension_type not in self.dimension_embeddings:
            return 0.5  # 預設中性分數

        try:
            # 建立品種描述
            breed_description = self._create_breed_description(breed_info, dimension_type)

            # 生成嵌入
            breed_embedding = self.sbert_model.encode(breed_description, convert_to_tensor=False)
            dimension_embedding = self.dimension_embeddings[dimension_type]

            # 計算相似度
            similarity = cosine_similarity([breed_embedding], [dimension_embedding])[0][0]

            # 正規化到 0-1 範圍
            normalized_score = (similarity + 1) / 2  # 從 [-1,1] 轉換到 [0,1]

            return max(0.0, min(1.0, normalized_score))

        except Exception as e:
            print(f"Error in semantic scoring for {dimension_type}: {str(e)}")
            return 0.5

    def _create_breed_description(self, breed_info: Dict[str, Any],
                                dimension_type: str) -> str:
        """為特定維度創建品種描述"""
        breed_name = breed_info.get('display_name', breed_info.get('breed_name', ''))

        if dimension_type.startswith('spatial_'):
            size = breed_info.get('size', 'medium')
            exercise = breed_info.get('exercise_needs', 'moderate')
            return f"{breed_name} is a {size} dog with {exercise} exercise needs"

        elif dimension_type.startswith('activity_'):
            exercise = breed_info.get('exercise_needs', 'moderate')
            temperament = breed_info.get('temperament', '')
            return f"{breed_name} has {exercise} exercise requirements and {temperament} temperament"

        elif dimension_type.startswith('noise_'):
            noise_level = breed_info.get('noise_level', 'moderate')
            temperament = breed_info.get('temperament', '')
            return f"{breed_name} has {noise_level} noise level and {temperament} nature"

        elif dimension_type.startswith('size_'):
            size = breed_info.get('size', 'medium')
            return f"{breed_name} is a {size} sized dog breed"

        elif dimension_type.startswith('family_'):
            children = breed_info.get('good_with_children', 'Yes')
            temperament = breed_info.get('temperament', '')
            return f"{breed_name} is {children} with children and has {temperament} temperament"

        elif dimension_type.startswith('maintenance_'):
            grooming = breed_info.get('grooming_needs', 'moderate')
            care_level = breed_info.get('care_level', 'moderate')
            return f"{breed_name} requires {grooming} grooming and {care_level} care level"

        return f"{breed_name} is a dog breed with various characteristics"

class AttributeScoringHead(ScoringHead):
    """
    屬性評分頭 - 使用DimensionScoreCalculator進行精確評分
    這個類別整合了dimension_score_calculator.py中的精確評分邏輯
    """

    def __init__(self):
        self.dimension_calculator = DimensionScoreCalculator()

    def score_dimension(self, breed_info: Dict[str, Any],
                       dimensions: QueryDimensions,
                       dimension_type: str) -> float:
        """屬性維度評分 - 使用精確的DimensionScoreCalculator"""
        try:
            if 'spatial' in dimension_type:
                return self._score_spatial_compatibility(breed_info, dimensions)
            elif 'activity' in dimension_type:
                return self._score_activity_compatibility(breed_info, dimensions)
            elif 'noise' in dimension_type:
                return self._score_noise_compatibility(breed_info, dimensions)
            elif 'size' in dimension_type:
                return self._score_size_compatibility(breed_info, dimensions)
            elif 'family' in dimension_type:
                return self._score_family_compatibility(breed_info, dimensions)
            elif 'maintenance' in dimension_type:
                return self._score_maintenance_compatibility(breed_info, dimensions)
            elif 'experience' in dimension_type:
                return self._score_experience_compatibility(breed_info, dimensions)
            elif 'health' in dimension_type:
                return self._score_health_compatibility(breed_info, dimensions)
            else:
                return 0.5

        except Exception as e:
            print(f"Error in attribute scoring for {dimension_type}: {str(e)}")
            return 0.5

    def _get_user_living_space(self, dimensions: QueryDimensions) -> str:
        """從dimensions提取居住空間類型"""
        if dimensions.spatial_constraints:
            for constraint in dimensions.spatial_constraints:
                if 'apartment' in constraint.lower():
                    return 'apartment'
                elif 'house' in constraint.lower():
                    if 'small' in constraint.lower():
                        return 'house_small'
                    return 'house_large'
        return 'house_small'

    def _get_user_has_yard(self, dimensions: QueryDimensions) -> bool:
        """從dimensions提取是否有院子"""
        if dimensions.spatial_constraints:
            for constraint in dimensions.spatial_constraints:
                if 'yard' in constraint.lower():
                    return True
        return False

    def _get_user_exercise_time(self, dimensions: QueryDimensions) -> int:
        """從dimensions提取運動時間"""
        if dimensions.activity_level:
            for level in dimensions.activity_level:
                if 'low' in level.lower():
                    return 30
                elif 'high' in level.lower():
                    return 120
        return 60

    def _get_user_exercise_type(self, dimensions: QueryDimensions) -> str:
        """從dimensions提取運動類型"""
        if dimensions.activity_level:
            for level in dimensions.activity_level:
                if 'high' in level.lower() or 'active' in level.lower():
                    return 'active_training'
                elif 'low' in level.lower():
                    return 'light_walks'
        return 'moderate_activity'

    def _get_user_grooming_commitment(self, dimensions: QueryDimensions) -> str:
        """從dimensions提取美容承諾度"""
        if dimensions.maintenance_level:
            for level in dimensions.maintenance_level:
                if 'low' in level.lower():
                    return 'low'
                elif 'high' in level.lower():
                    return 'high'
        return 'medium'

    def _get_user_experience_level(self, dimensions: QueryDimensions) -> str:
        """從dimensions提取經驗等級"""
        if dimensions.experience_level:
            for level in dimensions.experience_level:
                if 'beginner' in level.lower() or 'first' in level.lower():
                    return 'beginner'
                elif 'advanced' in level.lower() or 'expert' in level.lower():
                    return 'advanced'
        return 'intermediate'

    def _score_spatial_compatibility(self, breed_info: Dict[str, Any],
                                   dimensions: QueryDimensions) -> float:
        """空間相容性評分 - 使用DimensionScoreCalculator"""
        breed_size = breed_info.get('size', 'medium').capitalize()
        if breed_size.lower() in ['small', 'medium', 'large', 'giant']:
            breed_size = breed_size.capitalize()
        else:
            breed_size = 'Medium'

        living_space = self._get_user_living_space(dimensions)
        has_yard = self._get_user_has_yard(dimensions)
        exercise_needs = breed_info.get('exercise_needs', 'Moderate').capitalize()

        return self.dimension_calculator.calculate_space_score(
            size=breed_size,
            living_space=living_space,
            has_yard=has_yard,
            exercise_needs=exercise_needs
        )

    def _score_activity_compatibility(self, breed_info: Dict[str, Any],
                                    dimensions: QueryDimensions) -> float:
        """活動相容性評分 - 使用DimensionScoreCalculator"""
        breed_exercise = breed_info.get('exercise_needs', 'Moderate').capitalize()
        exercise_time = self._get_user_exercise_time(dimensions)
        exercise_type = self._get_user_exercise_type(dimensions)
        breed_size = breed_info.get('size', 'Medium').capitalize()
        living_space = self._get_user_living_space(dimensions)

        return self.dimension_calculator.calculate_exercise_score(
            breed_needs=breed_exercise,
            exercise_time=exercise_time,
            exercise_type=exercise_type,
            breed_size=breed_size,
            living_space=living_space
        )

    def _score_noise_compatibility(self, breed_info: Dict[str, Any],
                                 dimensions: QueryDimensions) -> float:
        """噪音相容性評分 - 使用DimensionScoreCalculator"""
        breed_name = breed_info.get('breed_name', '')
        noise_tolerance = 'medium'
        if dimensions.noise_preferences:
            for pref in dimensions.noise_preferences:
                if 'low' in pref.lower() or 'quiet' in pref.lower():
                    noise_tolerance = 'low'
                elif 'high' in pref.lower():
                    noise_tolerance = 'high'

        living_space = self._get_user_living_space(dimensions)
        has_children = 'children' in str(dimensions.family_context).lower()
        children_age = 'school_age'

        return self.dimension_calculator.calculate_noise_score(
            breed_name=breed_name,
            noise_tolerance=noise_tolerance,
            living_space=living_space,
            has_children=has_children,
            children_age=children_age
        )

    def _score_size_compatibility(self, breed_info: Dict[str, Any],
                                dimensions: QueryDimensions) -> float:
        """尺寸相容性評分"""
        if not dimensions.size_preferences:
            return 0.7

        breed_size = breed_info.get('size', 'medium').lower()
        total_score = 0.0

        size_compatibility = {
            ('small', 'small'): 1.0,
            ('small', 'medium'): 0.5,
            ('small', 'large'): 0.2,
            ('small', 'giant'): 0.1,
            ('medium', 'small'): 0.6,
            ('medium', 'medium'): 1.0,
            ('medium', 'large'): 0.7,
            ('medium', 'giant'): 0.4,
            ('large', 'small'): 0.3,
            ('large', 'medium'): 0.7,
            ('large', 'large'): 1.0,
            ('large', 'giant'): 0.9,
        }

        for size_pref in dimensions.size_preferences:
            key = (size_pref.lower(), breed_size)
            score = size_compatibility.get(key, 0.5)
            total_score += score

        return total_score / len(dimensions.size_preferences)

    def _score_family_compatibility(self, breed_info: Dict[str, Any],
                                  dimensions: QueryDimensions) -> float:
        """家庭相容性評分"""
        if not dimensions.family_context:
            return 0.7

        good_with_children = breed_info.get('good_with_children', 'Yes')
        temperament = breed_info.get('temperament', '').lower()

        total_score = 0.0
        score_count = 0

        for family_context in dimensions.family_context:
            if family_context == 'children':
                if good_with_children == 'Yes' or good_with_children == True:
                    # 進一步檢查temperament
                    if any(trait in temperament for trait in ['gentle', 'friendly', 'patient']):
                        total_score += 1.0
                    else:
                        total_score += 0.85
                elif good_with_children == 'No' or good_with_children == False:
                    total_score += 0.15
                else:
                    total_score += 0.5
                score_count += 1
            elif family_context == 'elderly':
                if any(trait in temperament for trait in ['gentle', 'calm', 'docile']):
                    total_score += 1.0
                elif any(trait in temperament for trait in ['energetic', 'hyperactive']):
                    total_score += 0.3
                else:
                    total_score += 0.7
                score_count += 1
            elif family_context == 'single':
                total_score += 0.8
                score_count += 1

        return total_score / max(1, score_count)

    def _score_maintenance_compatibility(self, breed_info: Dict[str, Any],
                                       dimensions: QueryDimensions) -> float:
        """維護相容性評分 - 使用DimensionScoreCalculator"""
        breed_grooming = breed_info.get('grooming_needs', 'Moderate').capitalize()
        user_commitment = self._get_user_grooming_commitment(dimensions)
        breed_size = breed_info.get('size', 'Medium').capitalize()
        breed_name = breed_info.get('breed_name', '')
        temperament = breed_info.get('temperament', '')

        return self.dimension_calculator.calculate_grooming_score(
            breed_needs=breed_grooming,
            user_commitment=user_commitment,
            breed_size=breed_size,
            breed_name=breed_name,
            temperament=temperament
        )

    def _score_experience_compatibility(self, breed_info: Dict[str, Any],
                                       dimensions: QueryDimensions) -> float:
        """經驗相容性評分 - 使用DimensionScoreCalculator"""
        care_level = breed_info.get('care_level', 'Moderate').capitalize()
        user_experience = self._get_user_experience_level(dimensions)
        temperament = breed_info.get('temperament', '')

        return self.dimension_calculator.calculate_experience_score(
            care_level=care_level,
            user_experience=user_experience,
            temperament=temperament
        )

    def _score_health_compatibility(self, breed_info: Dict[str, Any],
                                   dimensions: QueryDimensions) -> float:
        """健康相容性評分 - 使用DimensionScoreCalculator"""
        breed_name = breed_info.get('breed_name', '')
        health_sensitivity = 'medium'

        return self.dimension_calculator.calculate_health_score(
            breed_name=breed_name,
            health_sensitivity=health_sensitivity
        )

class MultiHeadScorer:
    """
    多頭評分系統
    結合語義和屬性評分，提供雙向相容性評估
    """

    def __init__(self, sbert_model: Optional[SentenceTransformer] = None):
        self.sbert_model = sbert_model
        self.semantic_head = SemanticScoringHead(sbert_model)
        self.attribute_head = AttributeScoringHead()
        self.dimension_weights = self._initialize_dimension_weights()
        self.head_fusion_weights = self._initialize_head_fusion_weights()
        self.weight_calculator = DynamicWeightCalculator()
        self.score_distributor = AdaptiveScoreDistribution()

    def _initialize_dimension_weights(self) -> Dict[str, float]:
        """初始化維度權重"""
        return {
            'activity_compatibility': 0.20,     # 生活方式匹配
            'noise_compatibility': 0.15,        # 居住和諧
            'spatial_compatibility': 0.12,      # 物理約束
            'family_compatibility': 0.12,       # 社交相容性
            'maintenance_compatibility': 0.15,  # 持續護理評估
            'experience_compatibility': 0.18,   # 經驗匹配（對新手非常重要）
            'health_compatibility': 0.08        # 健康考量
        }

    def _initialize_head_fusion_weights(self) -> Dict[str, Dict[str, float]]:
        """初始化頭融合權重"""
        return {
            'activity_compatibility': {'semantic': 0.3, 'attribute': 0.7},
            'noise_compatibility': {'semantic': 0.2, 'attribute': 0.8},
            'spatial_compatibility': {'semantic': 0.2, 'attribute': 0.8},
            'family_compatibility': {'semantic': 0.4, 'attribute': 0.6},
            'maintenance_compatibility': {'semantic': 0.2, 'attribute': 0.8},
            'experience_compatibility': {'semantic': 0.2, 'attribute': 0.8},  # 經驗主要看attribute
            'health_compatibility': {'semantic': 0.3, 'attribute': 0.7},
            'size_compatibility': {'semantic': 0.2, 'attribute': 0.8}
        }

    def score_breeds(self, candidate_breeds: Set[str],
                    dimensions: QueryDimensions) -> List[BreedScore]:
        """
        為候選品種評分

        Args:
            candidate_breeds: 通過約束篩選的候選品種
            dimensions: 查詢維度

        Returns:
            List[BreedScore]: 品種評分結果列表
        """
        try:
            breed_scores = []

            # 為每個品種計算分數
            for breed in candidate_breeds:
                breed_info = self._get_breed_info(breed)
                score_result = self._score_single_breed(breed_info, dimensions)
                breed_scores.append(score_result)

            # 按最終分數排序
            breed_scores.sort(key=lambda x: x.final_score, reverse=True)

            # 應用自適應分數分佈
            raw_scores = [(bs.breed_name, bs.final_score) for bs in breed_scores]
            distribution_result = self.score_distributor.distribute_scores(raw_scores)

            # 更新品種分數
            score_mapping = {breed: score for breed, score in distribution_result.final_scores}
            for breed_score in breed_scores:
                if breed_score.breed_name in score_mapping:
                    breed_score.final_score = score_mapping[breed_score.breed_name]

            # 重新排序
            breed_scores.sort(key=lambda x: x.final_score, reverse=True)

            return breed_scores

        except Exception as e:
            print(f"Error scoring breeds: {str(e)}")
            print(traceback.format_exc())
            return []

    def _get_breed_info(self, breed: str) -> Dict[str, Any]:
        """獲取品種資訊"""
        try:
            # 基本品種資訊
            breed_info = get_dog_description(breed) or {}

            # 健康資訊
            health_info = breed_health_info.get(breed, {})

            # 噪音資訊
            noise_info = breed_noise_info.get(breed, {})

            # 整合資訊
            return {
                'breed_name': breed,
                'display_name': breed.replace('_', ' '),
                'size': breed_info.get('Size', '').lower(),
                'exercise_needs': breed_info.get('Exercise Needs', '').lower(),
                'grooming_needs': breed_info.get('Grooming Needs', '').lower(),
                'temperament': breed_info.get('Temperament', '').lower(),
                'good_with_children': breed_info.get('Good with Children', 'Yes'),
                'care_level': breed_info.get('Care Level', '').lower(),
                'lifespan': breed_info.get('Lifespan', '10-12 years'),
                'noise_level': noise_info.get('noise_level', 'moderate').lower(),
                'description': breed_info.get('Description', ''),
                'raw_breed_info': breed_info,
                'raw_health_info': health_info,
                'raw_noise_info': noise_info
            }
        except Exception as e:
            print(f"Error getting breed info for {breed}: {str(e)}")
            return {
                'breed_name': breed,
                'display_name': breed.replace('_', ' ')
            }

    def _score_single_breed(self, breed_info: Dict[str, Any],
                          dimensions: QueryDimensions) -> BreedScore:
        """為單一品種評分"""
        try:
            dimensional_scores = {}
            semantic_total = 0.0
            attribute_total = 0.0

            # 動態權重分配（優先使用dimension_priorities）
            if dimensions.dimension_priorities:
                # 使用動態權重計算器
                user_mentions = self._extract_user_mentions(dimensions)
                weight_result = self.weight_calculator.calculate_dynamic_weights(
                    dimensions.dimension_priorities,
                    user_mentions,
                    use_contextual=True
                )
                adjusted_weights = weight_result.dynamic_weights
            else:
                # 降級到原有邏輯
                active_dimensions = self._get_active_dimensions(dimensions)
                adjusted_weights = self._adjust_dimension_weights(active_dimensions)

            # 為每個活躍維度評分
            for dimension, weight in adjusted_weights.items():
                # 語義評分
                semantic_score = self.semantic_head.score_dimension(
                    breed_info, dimensions, dimension
                )

                # 屬性評分
                attribute_score = self.attribute_head.score_dimension(
                    breed_info, dimensions, dimension
                )

                # 頭融合
                fusion_weights = self.head_fusion_weights.get(
                    dimension, {'semantic': 0.5, 'attribute': 0.5}
                )

                fused_score = (semantic_score * fusion_weights['semantic'] +
                              attribute_score * fusion_weights['attribute'])

                dimensional_scores[dimension] = fused_score
                semantic_total += semantic_score * weight
                attribute_total += attribute_score * weight

            # 雙向相容性評估
            bidirectional_bonus = self._calculate_bidirectional_bonus(
                breed_info, dimensions
            )

            # Apply size bias correction
            bias_correction = self._calculate_size_bias_correction(breed_info, dimensions)

            # 計算最終分數
            base_score = sum(score * adjusted_weights[dim]
                           for dim, score in dimensional_scores.items())

            # 關鍵維度低分懲罰機制
            # 當用戶明確提到某維度且該維度分數很低時，施加額外懲罰
            critical_penalty = self._calculate_critical_dimension_penalty(
                dimensional_scores, dimensions, adjusted_weights
            )

            # Apply corrections
            final_score = max(0.0, min(1.0, base_score + bidirectional_bonus + bias_correction + critical_penalty))

            # 信心度評估
            confidence_score = self._calculate_confidence(dimensions)

            return BreedScore(
                breed_name=breed_info.get('display_name', breed_info['breed_name']),
                final_score=final_score,
                dimensional_breakdown=dimensional_scores,
                semantic_component=semantic_total,
                attribute_component=attribute_total,
                bidirectional_bonus=bidirectional_bonus,
                confidence_score=confidence_score,
                explanation=self._generate_explanation(breed_info, dimensions, dimensional_scores)
            )

        except Exception as e:
            print(f"Error scoring breed {breed_info.get('breed_name', 'unknown')}: {str(e)}")
            return BreedScore(
                breed_name=breed_info.get('display_name', breed_info.get('breed_name', 'Unknown')),
                final_score=0.5,
                confidence_score=0.0
            )

    def _get_active_dimensions(self, dimensions: QueryDimensions) -> Set[str]:
        """獲取活躍的維度"""
        active = set()

        if dimensions.spatial_constraints:
            active.add('spatial_compatibility')
        if dimensions.activity_level:
            active.add('activity_compatibility')
        if dimensions.noise_preferences:
            active.add('noise_compatibility')
        if dimensions.size_preferences:
            active.add('size_compatibility')
        if dimensions.family_context:
            active.add('family_compatibility')
        if dimensions.maintenance_level:
            active.add('maintenance_compatibility')
        if hasattr(dimensions, 'experience_level') and dimensions.experience_level:
            active.add('experience_compatibility')

        return active

    def _extract_user_mentions(self, dimensions: QueryDimensions) -> Set[str]:
        """
        提取使用者明確提到的維度

        Args:
            dimensions: 查詢維度

        Returns:
            Set[str]: 使用者提到的維度集合
        """
        mentioned = set()

        if dimensions.spatial_constraints:
            mentioned.add('spatial_compatibility')
        if dimensions.activity_level:
            mentioned.add('activity_compatibility')
        if dimensions.noise_preferences:
            mentioned.add('noise_compatibility')
        if dimensions.size_preferences:
            mentioned.add('size_compatibility')
        if dimensions.family_context:
            mentioned.add('family_compatibility')
        if dimensions.maintenance_level:
            mentioned.add('maintenance_compatibility')
        if hasattr(dimensions, 'experience_level') and dimensions.experience_level:
            mentioned.add('experience_compatibility')

        return mentioned

    def _adjust_dimension_weights(self, active_dimensions: Set[str]) -> Dict[str, float]:
        """調整維度權重"""
        if not active_dimensions:
            return self.dimension_weights

        # 只為活躍維度分配權重
        active_weights = {dim: weight for dim, weight in self.dimension_weights.items()
                         if dim in active_dimensions}

        # 正規化權重總和為 1.0
        total_weight = sum(active_weights.values())
        if total_weight > 0:
            active_weights = {dim: weight / total_weight
                            for dim, weight in active_weights.items()}

        return active_weights

    def _calculate_critical_dimension_penalty(self,
                                             dimensional_scores: Dict[str, float],
                                             dimensions: QueryDimensions,
                                             weights: Dict[str, float]) -> float:
        """
        計算關鍵維度低分懲罰

        當用戶明確提到某維度（通過 dimension_priorities 或活躍維度）
        且該維度的分數低於閾值時，施加額外懲罰。

        這確保了「不合適」的品種不會因為其他維度的高分而排名過高。

        Args:
            dimensional_scores: 各維度的分數
            dimensions: 查詢維度
            weights: 當前使用的維度權重

        Returns:
            float: 懲罰值（負數）
        """
        total_penalty = 0.0

        # 定義低分閾值和懲罰係數
        LOW_SCORE_THRESHOLD = 0.55  # 低於此分數視為不匹配
        VERY_LOW_THRESHOLD = 0.40   # 極低分數
        PENALTY_MULTIPLIER = 0.25   # 基礎懲罰乘數（提高以增強效果）

        # 檢測用戶關心的維度
        user_priorities = {}

        # 從 dimension_priorities 獲取優先級
        if dimensions.dimension_priorities:
            for dim, priority in dimensions.dimension_priorities.items():
                # 映射維度名稱
                mapped_dim = self._map_priority_dimension(dim)
                if mapped_dim:
                    user_priorities[mapped_dim] = priority

        # 從活躍維度補充（確保提到的維度都被考慮）
        active_dims = self._get_active_dimensions(dimensions)
        for dim in active_dims:
            if dim not in user_priorities:
                user_priorities[dim] = 1.2  # 給予基本優先級

        # 對用戶關心的維度檢查低分情況
        for dim, priority in user_priorities.items():
            if dim in dimensional_scores:
                score = dimensional_scores[dim]

                # 只對低分維度施加懲罰
                if score < LOW_SCORE_THRESHOLD:
                    # 懲罰程度與以下因素成正比：
                    # 1. 分數有多低（距離閾值的差距）
                    # 2. 用戶對該維度的優先級
                    score_gap = LOW_SCORE_THRESHOLD - score
                    priority_factor = min(2.0, priority)  # 限制優先級影響

                    penalty = -score_gap * priority_factor * PENALTY_MULTIPLIER

                    # 極低分數額外懲罰
                    if score < VERY_LOW_THRESHOLD:
                        penalty *= 1.5

                    total_penalty += penalty

        return total_penalty

    def _map_priority_dimension(self, dim: str) -> str:
        """將 priority_detector 的維度名稱映射到 multi_head_scorer 使用的名稱"""
        mapping = {
            'noise': 'noise_compatibility',
            'size': 'spatial_compatibility',
            'exercise': 'activity_compatibility',
            'activity': 'activity_compatibility',
            'grooming': 'maintenance_compatibility',
            'maintenance': 'maintenance_compatibility',
            'family': 'family_compatibility',
            'experience': 'experience_compatibility',
            'health': 'health_compatibility',
            'spatial': 'spatial_compatibility',
            'space': 'spatial_compatibility'
        }
        return mapping.get(dim, dim if dim.endswith('_compatibility') else None)

    def _calculate_bidirectional_bonus(self, breed_info: Dict[str, Any],
                                     dimensions: QueryDimensions) -> float:
        """計算雙向相容性獎勵"""
        try:
            bonus = 0.0

            # 正向相容性：品種滿足用戶需求
            forward_compatibility = self._assess_forward_compatibility(breed_info, dimensions)

            # 反向相容性：用戶生活方式適合品種需求
            reverse_compatibility = self._assess_reverse_compatibility(breed_info, dimensions)

            # 雙向獎勵（較為保守）
            bonus = min(0.1, (forward_compatibility + reverse_compatibility) * 0.05)

            return bonus

        except Exception as e:
            print(f"Error calculating bidirectional bonus: {str(e)}")
            return 0.0

    def _assess_forward_compatibility(self, breed_info: Dict[str, Any],
                                    dimensions: QueryDimensions) -> float:
        """評估正向相容性"""
        compatibility = 0.0

        # 空間需求匹配
        if 'apartment' in dimensions.spatial_constraints:
            size = breed_info.get('size', '')
            if 'small' in size:
                compatibility += 0.3
            elif 'medium' in size:
                compatibility += 0.1

        # 活動需求匹配
        if 'low' in dimensions.activity_level:
            exercise = breed_info.get('exercise_needs', '')
            if 'low' in exercise:
                compatibility += 0.3
            elif 'moderate' in exercise:
                compatibility += 0.1

        return compatibility

    def _assess_reverse_compatibility(self, breed_info: Dict[str, Any],
                                    dimensions: QueryDimensions) -> float:
        """評估反向相容性"""
        compatibility = 0.0

        # 品種是否能在用戶環境中茁壯成長
        exercise_needs = breed_info.get('exercise_needs', '')

        if 'high' in exercise_needs:
            # 高運動需求品種需要確認用戶能提供足夠運動
            if ('high' in dimensions.activity_level or
                'house' in dimensions.spatial_constraints):
                compatibility += 0.2
            else:
                compatibility -= 0.2

        # 品種護理需求是否與用戶能力匹配
        grooming_needs = breed_info.get('grooming_needs', '')
        if 'high' in grooming_needs:
            if 'high' in dimensions.maintenance_level:
                compatibility += 0.1
            elif 'low' in dimensions.maintenance_level:
                compatibility -= 0.1

        return compatibility

    def _calculate_size_bias_correction(self, breed_info: Dict,
                                       dimensions: QueryDimensions) -> float:
        """Correct systematic bias toward larger breeds"""
        breed_size = breed_info.get('size', '').lower()

        # Default no bias correction
        correction = 0.0

        # Detect if user specified moderate/balanced preferences
        if any(term in dimensions.activity_level for term in ['moderate', 'balanced', 'average']):
            # Penalize extremes
            if breed_size in ['giant', 'toy']:
                correction = -0.1
            elif breed_size in ['large']:
                correction = -0.05

        # Boost medium breeds for moderate requirements
        if 'medium' in breed_size and 'balanced' in str(dimensions.activity_level):
            correction = 0.1

        return correction

    def _calculate_confidence(self, dimensions: QueryDimensions) -> float:
        """計算推薦信心度"""
        # 基於維度覆蓋率和信心分數計算
        dimension_count = sum([
            len(dimensions.spatial_constraints),
            len(dimensions.activity_level),
            len(dimensions.noise_preferences),
            len(dimensions.size_preferences),
            len(dimensions.family_context),
            len(dimensions.maintenance_level),
            len(dimensions.special_requirements)
        ])

        # 基礎信心度
        base_confidence = min(1.0, dimension_count * 0.15)

        # 品種提及獎勵
        breed_bonus = min(0.2, len(dimensions.breed_mentions) * 0.1)

        # 整體信心分數
        overall_confidence = dimensions.confidence_scores.get('overall', 0.5)

        return min(1.0, base_confidence + breed_bonus + overall_confidence * 0.3)

    def _generate_explanation(self, breed_info: Dict[str, Any],
                            dimensions: QueryDimensions,
                            dimensional_scores: Dict[str, float]) -> Dict[str, Any]:
        """生成評分解釋"""
        try:
            explanation = {
                'strengths': [],
                'considerations': [],
                'match_highlights': [],
                'score_breakdown': dimensional_scores
            }

            breed_name = breed_info.get('display_name', '')

            # 分析各維度表現
            for dimension, score in dimensional_scores.items():
                if score >= 0.8:
                    explanation['strengths'].append(self._get_strength_text(dimension, breed_info))
                elif score <= 0.3:
                    explanation['considerations'].append(self._get_consideration_text(dimension, breed_info))
                else:
                    explanation['match_highlights'].append(f"{dimension}: {score:.2f}")

            return explanation

        except Exception as e:
            print(f"Error generating explanation: {str(e)}")
            return {'strengths': [], 'considerations': [], 'match_highlights': []}

    def _get_strength_text(self, dimension: str, breed_info: Dict[str, Any]) -> str:
        """Get strength description"""
        breed_name = breed_info.get('display_name', '')

        if dimension == 'activity_compatibility':
            return f"{breed_name} has an activity level that matches your lifestyle very well"
        elif dimension == 'noise_compatibility':
            return f"{breed_name} has noise characteristics that fit your environment"
        elif dimension == 'spatial_compatibility':
            return f"{breed_name} is very suitable for your living space"
        elif dimension == 'family_compatibility':
            return f"{breed_name} performs well in a family environment"
        elif dimension == 'maintenance_compatibility':
            return f"{breed_name} has grooming needs that match your willingness to commit"
        else:
            return f"{breed_name} shows strong performance in {dimension}"

    def _get_consideration_text(self, dimension: str, breed_info: Dict[str, Any]) -> str:
        """Get consideration description"""
        breed_name = breed_info.get('display_name', '')

        if dimension == 'activity_compatibility':
            return f"{breed_name} may have exercise needs that differ from your lifestyle"
        elif dimension == 'noise_compatibility':
            return f"{breed_name} has noise characteristics that require special consideration"
        elif dimension == 'maintenance_compatibility':
            return f"{breed_name} has relatively high grooming requirements"
        else:
            return f"{breed_name} requires extra consideration in {dimension}"


def score_breed_candidates(candidate_breeds: Set[str],
                         dimensions: QueryDimensions,
                         sbert_model: Optional[SentenceTransformer] = None) -> List[BreedScore]:
    """
    便利函數：為候選品種評分

    Args:
        candidate_breeds: 候選品種集合
        dimensions: 查詢維度
        sbert_model: 可選的SBERT模型

    Returns:
        List[BreedScore]: 評分結果列表
    """
    scorer = MultiHeadScorer(sbert_model)
    return scorer.score_breeds(candidate_breeds, dimensions)