|
|
@@ -283,7 +283,9 @@ class Stage8SimilarityAnalyzer:
|
|
|
max_workers: int = 5,
|
|
|
min_similarity: float = 0.0,
|
|
|
output_dir: str = "output_v2",
|
|
|
- target_features: Optional[List[str]] = None
|
|
|
+ target_features: Optional[List[str]] = None,
|
|
|
+ stage6_path: str = 'output_v2/stage6_with_evaluations.json',
|
|
|
+ update_stage6: bool = True
|
|
|
):
|
|
|
"""
|
|
|
初始化 Stage 8 分析器
|
|
|
@@ -295,6 +297,8 @@ class Stage8SimilarityAnalyzer:
|
|
|
min_similarity: 最小相似度阈值（默认 0.0，保留所有特征）
|
|
|
output_dir: 输出目录
|
|
|
target_features: 指定要处理的原始特征列表（None = 处理所有特征）
|
|
|
+ stage6_path: Stage 6 数据文件路径（用于计算综合得分）
|
|
|
+ update_stage6: 是否计算并更新 Stage 6 的综合得分（默认 True）
|
|
|
"""
|
|
|
self.weight_embedding = weight_embedding
|
|
|
self.weight_semantic = weight_semantic
|
|
|
@@ -302,6 +306,8 @@ class Stage8SimilarityAnalyzer:
|
|
|
self.min_similarity = min_similarity
|
|
|
self.output_dir = output_dir
|
|
|
self.target_features = target_features
|
|
|
+ self.stage6_path = stage6_path
|
|
|
+ self.update_stage6 = update_stage6
|
|
|
|
|
|
# 验证权重
|
|
|
total_weight = weight_embedding + weight_semantic
|
|
|
@@ -503,8 +509,199 @@ class Stage8SimilarityAnalyzer:
|
|
|
|
|
|
logger.info(f" 结果已保存: {output_path}")
|
|
|
|
|
|
+ # 计算并更新综合得分P
|
|
|
+ if self.update_stage6:
|
|
|
+ logger.info("\n" + "=" * 60)
|
|
|
+ logger.info("开始计算综合得分P并更新Stage 6数据...")
|
|
|
+ logger.info("=" * 60)
|
|
|
+ self._calculate_and_update_comprehensive_scores(results)
|
|
|
+
|
|
|
return final_result
|
|
|
|
|
|
+ def _calculate_and_update_comprehensive_scores(self, stage8_results: List[Dict]):
|
|
|
+ """
|
|
|
+ 计算综合得分P并更新Stage 6数据
|
|
|
+
|
|
|
+ Args:
|
|
|
+ stage8_results: Stage 8 的结果列表
|
|
|
+ """
|
|
|
+ try:
|
|
|
+ # 1. 加载 Stage 6 数据
|
|
|
+ logger.info(f" 加载 Stage 6 数据: {self.stage6_path}")
|
|
|
+ if not os.path.exists(self.stage6_path):
|
|
|
+ logger.error(f" Stage 6 文件不存在: {self.stage6_path}")
|
|
|
+ return
|
|
|
+
|
|
|
+ with open(self.stage6_path, 'r', encoding='utf-8') as f:
|
|
|
+ stage6_data = json.load(f)
|
|
|
+
|
|
|
+ # 2. 构建 Stage 8 映射 (note_id → max_similarity)
|
|
|
+ logger.info(" 构建相似度映射...")
|
|
|
+ similarity_map = {}
|
|
|
+ for result in stage8_results:
|
|
|
+ note_id = result['note_id']
|
|
|
+ max_similarity = result['similarity_statistics']['max_similarity']
|
|
|
+ similarity_map[note_id] = max_similarity
|
|
|
+
|
|
|
+ logger.info(f" 相似度映射条目数: {len(similarity_map)}")
|
|
|
+
|
|
|
+ # 3. 遍历 Stage 6 中的所有原始特征和搜索词，计算 P 值
|
|
|
+ # Stage 6 数据是一个列表，每个元素是一个原始特征
|
|
|
+ updated_count = 0
|
|
|
+ total_searches = 0
|
|
|
+
|
|
|
+ logger.info(f" 开始遍历 {len(stage6_data)} 个原始特征...")
|
|
|
+
|
|
|
+ for feature_item in stage6_data:
|
|
|
+ original_feature = feature_item.get('原始特征名称', '')
|
|
|
+ logger.info(f"\n 处理原始特征: {original_feature}")
|
|
|
+
|
|
|
+ # 遍历每个分组
|
|
|
+ for group in feature_item.get('组合评估结果_分组', []):
|
|
|
+ source_word = group.get('source_word', '')
|
|
|
+
|
|
|
+ # 遍历该分组的所有搜索词
|
|
|
+ for search_item in group.get('top10_searches', []):
|
|
|
+ search_word = search_item.get('search_word', '')
|
|
|
+ total_searches += 1
|
|
|
+
|
|
|
+ logger.info(f" 处理搜索词: {search_word} (来源: {source_word})")
|
|
|
+
|
|
|
+ # 计算该搜索词的综合得分
|
|
|
+ p_score, p_detail = self._calculate_single_query_score(
|
|
|
+ search_item,
|
|
|
+ similarity_map
|
|
|
+ )
|
|
|
+
|
|
|
+ # 更新搜索词数据
|
|
|
+ if p_score is not None:
|
|
|
+ search_item['comprehensive_score'] = round(p_score, 3)
|
|
|
+ search_item['comprehensive_score_detail'] = p_detail
|
|
|
+ updated_count += 1
|
|
|
+ logger.info(f" 综合得分P = {p_score:.3f} (M={p_detail['M']}, N={p_detail['N']})")
|
|
|
+ else:
|
|
|
+ logger.warning(f" 无法计算综合得分（可能缺少数据）")
|
|
|
+
|
|
|
+ # 4. 保存更新后的 Stage 6 数据
|
|
|
+ logger.info(f"\n 保存更新后的 Stage 6 数据...")
|
|
|
+ logger.info(f" 已更新 {updated_count}/{total_searches} 个搜索词")
|
|
|
+
|
|
|
+ with open(self.stage6_path, 'w', encoding='utf-8') as f:
|
|
|
+ json.dump(stage6_data, f, ensure_ascii=False, indent=2)
|
|
|
+
|
|
|
+ logger.info(f" 更新完成: {self.stage6_path}")
|
|
|
+
|
|
|
+ except Exception as e:
|
|
|
+ logger.error(f" 计算综合得分失败: {e}", exc_info=True)
|
|
|
+
|
|
|
+ def _calculate_single_query_score(
|
|
|
+ self,
|
|
|
+ query: Dict,
|
|
|
+ similarity_map: Dict[str, float]
|
|
|
+ ) -> tuple[Optional[float], Optional[Dict]]:
|
|
|
+ """
|
|
|
+ 计算单个查询的综合得分P
|
|
|
+
|
|
|
+ Args:
|
|
|
+ query: Stage 6 中的单个查询对象
|
|
|
+ similarity_map: note_id → max_similarity 的映射
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ (P值, 详细计算信息) 或 (None, None)
|
|
|
+ """
|
|
|
+ # 获取总帖子数 N
|
|
|
+ evaluation_with_filter = query.get('evaluation_with_filter', {})
|
|
|
+ N = evaluation_with_filter.get('total_notes', 0)
|
|
|
+
|
|
|
+ if N == 0:
|
|
|
+ logger.warning(f" 查询总帖子数为0，无法计算P值")
|
|
|
+ return None, None
|
|
|
+
|
|
|
+ # 获取笔记评估数据和原始笔记数据
|
|
|
+ notes_evaluation = evaluation_with_filter.get('notes_evaluation', [])
|
|
|
+ search_result = query.get('search_result', {})
|
|
|
+ notes_data = search_result.get('data', {}).get('data', [])
|
|
|
+
|
|
|
+ if not notes_evaluation or not notes_data:
|
|
|
+ logger.warning(f" 缺少评估数据或笔记数据")
|
|
|
+ return 0.0, {
|
|
|
+ 'N': N,
|
|
|
+ 'M': 0,
|
|
|
+ 'total_contribution': 0.0,
|
|
|
+ 'complete_matches': []
|
|
|
+ }
|
|
|
+
|
|
|
+ # 获取完全匹配的帖子列表 (综合得分 >= 0.8)
|
|
|
+ complete_matches_data = []
|
|
|
+ for note_eval in notes_evaluation:
|
|
|
+ score = note_eval.get('综合得分', 0)
|
|
|
+ if score >= 0.8:
|
|
|
+ note_index = note_eval.get('note_index', -1)
|
|
|
+ if 0 <= note_index < len(notes_data):
|
|
|
+ # 从原始数据中获取note_id
|
|
|
+ note_id = notes_data[note_index].get('id', '')
|
|
|
+ note_card = notes_data[note_index].get('note_card', {})
|
|
|
+ note_title = note_card.get('display_title', '')
|
|
|
+
|
|
|
+ complete_matches_data.append({
|
|
|
+ 'note_id': note_id,
|
|
|
+ 'note_title': note_title,
|
|
|
+ 'evaluation_score': score,
|
|
|
+ 'note_index': note_index
|
|
|
+ })
|
|
|
+
|
|
|
+ M = len(complete_matches_data)
|
|
|
+ logger.info(f" 完全匹配数: M = {M}/{N}")
|
|
|
+
|
|
|
+ if M == 0:
|
|
|
+ # 没有完全匹配，P = 0
|
|
|
+ return 0.0, {
|
|
|
+ 'N': N,
|
|
|
+ 'M': 0,
|
|
|
+ 'total_contribution': 0.0,
|
|
|
+ 'complete_matches': []
|
|
|
+ }
|
|
|
+
|
|
|
+ # 计算每个完全匹配的贡献 a×b
|
|
|
+ contributions = []
|
|
|
+ total_contribution = 0.0
|
|
|
+
|
|
|
+ for match in complete_matches_data:
|
|
|
+ note_id = match['note_id']
|
|
|
+ evaluation_score = match['evaluation_score'] # a 值
|
|
|
+
|
|
|
+ # 从 similarity_map 获取 b 值
|
|
|
+ max_similarity = similarity_map.get(note_id, 0) # b 值
|
|
|
+
|
|
|
+ # 计算贡献
|
|
|
+ contribution = evaluation_score * max_similarity
|
|
|
+ total_contribution += contribution
|
|
|
+
|
|
|
+ # 保存详细信息
|
|
|
+ contributions.append({
|
|
|
+ 'note_id': note_id,
|
|
|
+ 'note_title': match['note_title'],
|
|
|
+ 'evaluation_score': round(evaluation_score, 3),
|
|
|
+ 'max_similarity': round(max_similarity, 3),
|
|
|
+ 'contribution': round(contribution, 3)
|
|
|
+ })
|
|
|
+
|
|
|
+ # 计算综合得分 P = Σ(a×b) / N
|
|
|
+ P = total_contribution / N
|
|
|
+
|
|
|
+ # 按贡献降序排序
|
|
|
+ contributions.sort(key=lambda x: x['contribution'], reverse=True)
|
|
|
+
|
|
|
+ # 构建详细信息
|
|
|
+ detail = {
|
|
|
+ 'N': N,
|
|
|
+ 'M': M,
|
|
|
+ 'total_contribution': round(total_contribution, 3),
|
|
|
+ 'complete_matches': contributions
|
|
|
+ }
|
|
|
+
|
|
|
+ return P, detail
|
|
|
+
|
|
|
def run(
|
|
|
self,
|
|
|
stage7_results: Dict,
|
刘立冬