增强数据分析工具和工作流检查功能
- 优化数据对比分析工具的准确性和性能 - 完善评估指标分析的算法和统计功能 - 改进医疗工作流分析的深度和覆盖范围 - 增强工作流完整性检查的全面性 - 新增工作流文件清理工具提升维护效率 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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@ -45,7 +45,7 @@ COLORS = {
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QUALITY_DIMENSIONS = [
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'clinical_inquiry',
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'communication_quality',
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'multi_round_consistency',
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'information_completeness',
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'overall_professionalism'
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]
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@ -64,7 +64,7 @@ DIMENSION_NAMES = {
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'clinical_inquiry': 'CI',
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'diagnostic_reasoning': 'DR',
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'communication_quality': 'CQ',
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'multi_round_consistency': 'MRC',
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'information_completeness': 'IC',
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'overall_professionalism': 'OP',
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'present_illness_similarity': 'PHI Similarity',
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'past_history_similarity': 'HP Similarity',
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@ -134,8 +134,13 @@ class DataQualityComparisonAnalyzer:
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# 处理评估分数
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for dimension in EVALUATION_DIMENSIONS:
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if dimension in evaluation_scores:
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score_info = evaluation_scores[dimension]
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# 向后兼容性处理：将旧的 multi_round_consistency 映射到新的 information_completeness
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actual_dimension = dimension
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if dimension == 'information_completeness' and dimension not in evaluation_scores and 'multi_round_consistency' in evaluation_scores:
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actual_dimension = 'multi_round_consistency'
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if actual_dimension in evaluation_scores:
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score_info = evaluation_scores[actual_dimension]
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if isinstance(score_info, dict) and 'score' in score_info:
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score = score_info['score']
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elif isinstance(score_info, (int, float)):
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@ -536,7 +541,7 @@ def main():
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has_significant = False
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# 定义需要显示的维度顺序（四个质量指标 + 三个相似度指标）
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target_dimensions = ['clinical_inquiry', 'multi_round_consistency', 'present_illness_similarity', 'past_history_similarity', 'chief_complaint_similarity']
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target_dimensions = ['clinical_inquiry', 'information_completeness', 'present_illness_similarity', 'past_history_similarity', 'chief_complaint_similarity']
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for dimension in target_dimensions:
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if dimension in statistics['quality_statistics']['statistical_tests']:
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@ -81,7 +81,7 @@ def extract_evaluate_scores(workflow: List[Dict]) -> List[Dict]:
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# 检查是否包含评估分数
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if any(key in output_data for key in [
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'clinical_inquiry', 'communication_quality',
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'multi_round_consistency', 'overall_professionalism',
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'information_completeness', 'overall_professionalism',
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'present_illness_similarity', 'past_history_similarity',
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'chief_complaint_similarity'
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]):
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@ -110,7 +110,7 @@ def calculate_metrics_by_step(workflow_data: List[List[Dict]]) -> Dict[str, List
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metrics_data = {
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'clinical_inquiry': [[] for _ in range(max_steps)],
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'communication_quality': [[] for _ in range(max_steps)],
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'multi_round_consistency': [[] for _ in range(max_steps)],
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'information_completeness': [[] for _ in range(max_steps)],
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'overall_professionalism': [[] for _ in range(max_steps)],
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'present_illness_similarity': [[] for _ in range(max_steps)],
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'past_history_similarity': [[] for _ in range(max_steps)],
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@ -124,8 +124,13 @@ def calculate_metrics_by_step(workflow_data: List[List[Dict]]) -> Dict[str, List
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for step_idx, score_data in enumerate(evaluate_scores):
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# 提取各维度分数
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for metric in metrics_data.keys():
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if metric in score_data and isinstance(score_data[metric], dict):
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score = score_data[metric].get('score', 0.0)
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# 向后兼容性处理：将旧的 multi_round_consistency 映射到新的 information_completeness
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actual_metric = metric
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if metric == 'information_completeness' and metric not in score_data and 'multi_round_consistency' in score_data:
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actual_metric = 'multi_round_consistency'
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if actual_metric in score_data and isinstance(score_data[actual_metric], dict):
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score = score_data[actual_metric].get('score', 0.0)
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metrics_data[metric][step_idx].append(score)
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# 计算平均值
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@ -10,7 +10,7 @@ import os
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from collections import defaultdict
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import matplotlib.pyplot as plt
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from typing import Dict, List
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from file_filter_utils import filter_complete_files, print_filter_summary
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from file_filter_utils import load_incomplete_files
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class MedicalWorkflowAnalyzer:
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@ -30,23 +30,20 @@ class MedicalWorkflowAnalyzer:
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self.step_statistics = defaultdict(int)
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def load_workflow_data(self) -> None:
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"""加载所有工作流数据文件"""
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"""加载所有工作流数据文件（包括完成和未完成的）"""
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if not os.path.exists(self.results_dir):
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print(f"结果目录不存在: {self.results_dir}")
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return
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# 获取所有jsonl文件
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all_files = [os.path.join(self.results_dir, f) for f in os.listdir(self.results_dir)
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if f.endswith('.jsonl')]
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all_files = [f for f in os.listdir(self.results_dir) if f.endswith('.jsonl')]
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# 过滤出完成的文件
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filtered_files = filter_complete_files(all_files, self.output_dir)
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print_filter_summary(self.output_dir)
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# 获取未完成文件列表
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incomplete_files = load_incomplete_files(self.output_dir)
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print(f"找到 {len(all_files)} 个数据文件，将处理 {len(filtered_files)} 个完成的文件")
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print(f"找到 {len(all_files)} 个数据文件，将处理所有文件（包括未完成的）")
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for filepath in sorted(filtered_files):
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filename = os.path.basename(filepath)
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for filename in sorted(all_files):
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filepath = os.path.join(self.results_dir, filename)
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try:
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with open(filepath, 'r', encoding='utf-8') as f:
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@ -62,19 +59,28 @@ class MedicalWorkflowAnalyzer:
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continue
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if case_data:
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# 检查是否为未完成的文件
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is_incomplete = filename in incomplete_files
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self.workflow_data.append({
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'filename': filename,
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'data': case_data
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'data': case_data,
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'is_incomplete': is_incomplete
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})
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except Exception as e:
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print(f"读取文件 {filename} 失败: {e}")
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complete_count = len([case for case in self.workflow_data if not case.get('is_incomplete', False)])
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incomplete_count = len([case for case in self.workflow_data if case.get('is_incomplete', False)])
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print(f"成功加载 {len(self.workflow_data)} 个病例的数据")
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print(f" - 完成的病例: {complete_count} 个")
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print(f" - 未完成的病例: {incomplete_count} 个")
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def analyze_workflow_steps(self) -> Dict[str, List[int]]:
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"""
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分析每个病例完成triage、hpi、ph三个阶段所需的step数量
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包括未完成的样本（用-1表示未完成状态）
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Returns:
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Dict包含每个阶段所需的step数量列表
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@ -90,6 +96,7 @@ class MedicalWorkflowAnalyzer:
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for case_info in self.workflow_data:
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case_data = case_info['data']
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is_incomplete = case_info.get('is_incomplete', False)
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# 按阶段分组step
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triage_steps = set()
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@ -97,6 +104,19 @@ class MedicalWorkflowAnalyzer:
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ph_steps = set()
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all_steps = set()
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# 如果是未完成的样本，检查任务完成状态
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incomplete_phases = set()
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if is_incomplete:
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# 查找倒数第二行的task_completion_summary
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for entry in reversed(case_data):
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if 'task_completion_summary' in entry:
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phases = entry.get('task_completion_summary', {}).get('phases', {})
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for phase_name in ['triage', 'hpi', 'ph']:
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phase_info = phases.get(phase_name, {})
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if not phase_info.get('is_completed', False):
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incomplete_phases.add(phase_name)
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break
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for entry in case_data:
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if entry.get('event_type') == 'step_start' and 'current_phase' in entry:
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step_num = entry.get('step_number', 0)
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@ -111,18 +131,18 @@ class MedicalWorkflowAnalyzer:
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elif phase == 'ph':
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ph_steps.add(step_num)
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# 计算每个阶段的step数量
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triage_count = len(triage_steps)
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hpi_count = len(hpi_steps)
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ph_count = len(ph_steps)
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# 计算每个阶段的step数量，对于未完成的阶段使用-1
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triage_count = -1 if 'triage' in incomplete_phases else len(triage_steps)
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hpi_count = -1 if 'hpi' in incomplete_phases else len(hpi_steps)
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ph_count = -1 if 'ph' in incomplete_phases else len(ph_steps)
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final_step = max(all_steps) if all_steps else 0
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# 只添加有数据的阶段
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if triage_count > 0:
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# 添加数据（包括-1表示的未完成状态）
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if triage_count != 0: # 包括-1和正数
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stage_steps['triage'].append(triage_count)
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if hpi_count > 0:
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if hpi_count != 0: # 包括-1和正数
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stage_steps['hpi'].append(hpi_count)
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if ph_count > 0:
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if ph_count != 0: # 包括-1和正数
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stage_steps['ph'].append(ph_count)
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if final_step > 0:
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stage_steps['final_step'].append(final_step)
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@ -156,7 +176,7 @@ class MedicalWorkflowAnalyzer:
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def plot_step_distribution_subplots(self, stage_stats: Dict[str, Dict[int, int]],
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output_file: str = "step_distribution_subplots.png") -> None:
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"""
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绘制四个子图的step数量分布柱形图
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绘制四个子图的step数量分布柱形图（包括未完成的数据）
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Args:
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stage_stats: 各阶段的step数量统计数据
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@ -166,9 +186,10 @@ class MedicalWorkflowAnalyzer:
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print("没有数据可供绘制")
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return
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# 设置英文显示
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plt.rcParams['font.family'] = 'DejaVu Sans'
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plt.rcParams['axes.unicode_minus'] = False
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# 设置字体支持中文
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import matplotlib
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matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'WenQuanYi Micro Hei', 'sans-serif']
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matplotlib.rcParams['axes.unicode_minus'] = False
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# 创建四个子图
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fig, axes = plt.subplots(2, 2, figsize=(16, 12))
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@ -190,15 +211,34 @@ class MedicalWorkflowAnalyzer:
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ax = axes[row, col]
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if stage in stage_stats and stage_stats[stage]:
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steps = sorted(stage_stats[stage].keys())
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counts = [stage_stats[stage][step] for step in steps]
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# 分离完成和未完成的数据
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completed_data = {k: v for k, v in stage_stats[stage].items() if k != -1}
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incomplete_count = stage_stats[stage].get(-1, 0)
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# 准备x轴数据和标签
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if completed_data:
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steps = sorted(completed_data.keys())
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counts = [completed_data[step] for step in steps]
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x_labels = [str(step) for step in steps]
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else:
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steps = []
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counts = []
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x_labels = []
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# 如果有未完成数据，添加到最后
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if incomplete_count > 0:
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steps.append(len(steps)) # 位置索引
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counts.append(incomplete_count)
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x_labels.append('未完成')
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if steps and counts:
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# 绘制柱形图
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bars = ax.bar(steps, counts, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4'][stages_order.index(stage) % 4],
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bars = ax.bar(range(len(steps)), counts,
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color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4'][stages_order.index(stage) % 4],
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alpha=0.7, edgecolor='black', linewidth=0.5)
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# 在柱形上标注数值
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for bar, count in zip(bars, counts):
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for i, (bar, count) in enumerate(zip(bars, counts)):
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height = bar.get_height()
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ax.text(bar.get_x() + bar.get_width()/2., height + max(counts)*0.01,
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f'{count}', ha='center', va='bottom', fontsize=9, fontweight='bold')
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@ -209,20 +249,32 @@ class MedicalWorkflowAnalyzer:
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ax.set_ylabel('Number of Cases', fontsize=10)
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ax.grid(True, alpha=0.3, linestyle='--')
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# 设置x轴刻度
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if steps:
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ax.set_xticks(steps)
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ax.set_xticklabels(steps, rotation=45)
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# 设置x轴刻度和标签
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ax.set_xticks(range(len(steps)))
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ax.set_xticklabels(x_labels, rotation=45)
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# 添加统计信息文本
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if counts:
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mean_val = sum(s*c for s, c in zip(steps, counts)) / sum(counts)
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max_val = max(steps)
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min_val = min(steps)
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# 添加统计信息文本（只针对完成的数据）
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if completed_data:
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completed_steps = list(completed_data.keys())
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completed_counts = list(completed_data.values())
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mean_val = sum(s*c for s, c in zip(completed_steps, completed_counts)) / sum(completed_counts)
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max_val = max(completed_steps)
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min_val = min(completed_steps)
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stats_text = f'Completed Mean: {mean_val:.1f}\nCompleted Range: {min_val}-{max_val}'
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if incomplete_count > 0:
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stats_text += f'\nIncomplete: {incomplete_count}'
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stats_text = f'Mean: {mean_val:.1f}\nRange: {min_val}-{max_val}'
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ax.text(0.02, 0.98, stats_text, transform=ax.transAxes, fontsize=9,
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
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elif incomplete_count > 0:
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stats_text = f'All Incomplete: {incomplete_count}'
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ax.text(0.02, 0.98, stats_text, transform=ax.transAxes, fontsize=9,
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
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else:
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ax.text(0.5, 0.5, 'No Data Available', ha='center', va='center',
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transform=ax.transAxes, fontsize=12)
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ax.set_title(f'{subplot_titles[stage]}\n(n=0)', fontsize=12, fontweight='bold')
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else:
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ax.text(0.5, 0.5, 'No Data Available', ha='center', va='center',
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transform=ax.transAxes, fontsize=12)
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@ -242,7 +294,7 @@ class MedicalWorkflowAnalyzer:
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print(f"Four-subplot chart saved to: {output_path}")
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def print_statistics_summary(self, stage_steps: Dict[str, List[int]]) -> None:
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"""打印统计摘要"""
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"""打印统计摘要（包括未完成数据）"""
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print("\n=== Medical Workflow Step Statistics Summary ===")
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# 英文阶段名称映射
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@ -256,12 +308,25 @@ class MedicalWorkflowAnalyzer:
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for stage, steps in stage_steps.items():
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stage_name = stage_names.get(stage, stage.upper())
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if steps:
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# 分离完成和未完成的数据
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completed_steps = [s for s in steps if s != -1]
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incomplete_count = steps.count(-1)
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print(f"\n{stage_name}:")
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print(f" Total Cases: {len(steps)}")
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print(f" Mean Steps: {sum(steps)/len(steps):.2f}")
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print(f" Min Steps: {min(steps)}")
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print(f" Max Steps: {max(steps)}")
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print(f" Step Distribution: {dict(sorted({s: steps.count(s) for s in set(steps)}.items()))}")
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if completed_steps:
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print(f" Mean Steps: {sum(completed_steps)/len(completed_steps):.2f}")
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print(f" Min Steps: {min(completed_steps)}")
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print(f" Max Steps: {max(completed_steps)}")
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# 构建分布字典
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distribution = dict(sorted({s: completed_steps.count(s) for s in set(completed_steps)}.items()))
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if incomplete_count > 0:
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distribution['未完成'] = incomplete_count
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print(f" Step Distribution: {distribution}")
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else:
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print(f" All cases incomplete: {incomplete_count}")
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else:
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print(f"\n{stage_name}: No Data")
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@ -41,19 +41,32 @@ class WorkflowCompletenessChecker:
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"""
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try:
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with open(filepath, 'r', encoding='utf-8') as f:
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# 读取最后一行
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lines = f.readlines()
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if not lines:
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if len(lines) < 2: # 需要至少两行：倒数第二行和最后一行
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return False
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last_line = lines[-1].strip()
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if not last_line:
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# 检查倒数第二行的task_completion_summary
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second_to_last_line = lines[-2].strip()
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if not second_to_last_line:
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return False
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# 解析最后一行JSON
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try:
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last_event = json.loads(last_line)
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return last_event.get('event_type') == 'workflow_complete'
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second_to_last_event = json.loads(second_to_last_line)
|
||||
# 检查是否有task_completion_summary字段
|
||||
task_summary = second_to_last_event.get('task_completion_summary', {})
|
||||
if not task_summary:
|
||||
return False
|
||||
|
||||
# 检查三个阶段的完成状态
|
||||
phases = task_summary.get('phases', {})
|
||||
required_phases = ['triage', 'hpi', 'ph']
|
||||
|
||||
for phase in required_phases:
|
||||
phase_info = phases.get(phase, {})
|
||||
if not phase_info.get('is_completed', False):
|
||||
return False
|
||||
|
||||
return True
|
||||
except json.JSONDecodeError:
|
||||
return False
|
||||
|
||||
|
||||
188
analysis/workflow_file_cleaner.py
Normal file
188
analysis/workflow_file_cleaner.py
Normal file
@ -0,0 +1,188 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
工作流文件清理器
|
||||
检测指定目录中的所有JSONL文件，删除不完整的工作流记录文件
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import glob
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List
|
||||
import argparse
|
||||
import logging
|
||||
|
||||
# 配置日志
|
||||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class WorkflowFileCleaner:
|
||||
"""工作流文件清理器"""
|
||||
|
||||
def __init__(self, directory: str, dry_run: bool = False):
|
||||
"""
|
||||
初始化清理器
|
||||
|
||||
Args:
|
||||
directory: 要检查的目录路径
|
||||
dry_run: 是否为试运行模式（不实际删除文件）
|
||||
"""
|
||||
self.directory = Path(directory)
|
||||
self.dry_run = dry_run
|
||||
self.stats = {
|
||||
'total_files': 0,
|
||||
'complete_files': 0,
|
||||
'incomplete_files': 0,
|
||||
'deleted_files': [],
|
||||
'error_files': []
|
||||
}
|
||||
|
||||
def check_workflow_completion(self, jsonl_file: str) -> bool:
|
||||
"""
|
||||
检查工作流是否完整
|
||||
|
||||
Args:
|
||||
jsonl_file: JSONL文件路径
|
||||
|
||||
Returns:
|
||||
bool: True表示工作流完整，False表示不完整
|
||||
"""
|
||||
try:
|
||||
with open(jsonl_file, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
if not lines:
|
||||
logger.warning(f"文件为空: {jsonl_file}")
|
||||
return False
|
||||
|
||||
# 获取最后一行
|
||||
last_line = lines[-1].strip()
|
||||
if not last_line:
|
||||
logger.warning(f"文件最后一行为空: {jsonl_file}")
|
||||
return False
|
||||
|
||||
try:
|
||||
last_event = json.loads(last_line)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"解析最后一行JSON失败 {jsonl_file}: {e}")
|
||||
return False
|
||||
|
||||
# 检查是否包含workflow_complete事件
|
||||
if last_event.get('event_type') != 'workflow_complete':
|
||||
logger.info(f"工作流未完成 - 缺少workflow_complete事件: {jsonl_file}")
|
||||
return False
|
||||
|
||||
# 检查final_summary中的phases完成状态
|
||||
final_summary = last_event.get('final_summary', {})
|
||||
phases = final_summary.get('phases', {})
|
||||
|
||||
required_phases = ['triage', 'hpi', 'ph']
|
||||
for phase in required_phases:
|
||||
phase_info = phases.get(phase, {})
|
||||
is_completed = phase_info.get('is_completed', False)
|
||||
completion_rate = phase_info.get('completion_rate', 0.0)
|
||||
|
||||
if not is_completed or completion_rate != 1.0:
|
||||
logger.info(f"工作流未完成 - 阶段 {phase} 未完成: {jsonl_file}")
|
||||
return False
|
||||
|
||||
logger.info(f"工作流完整: {jsonl_file}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"检查文件时发生错误 {jsonl_file}: {e}")
|
||||
return False
|
||||
|
||||
def scan_and_clean_files(self) -> None:
|
||||
"""扫描目录中的所有JSONL文件并清理不完整的文件"""
|
||||
if not self.directory.exists():
|
||||
logger.error(f"目录不存在: {self.directory}")
|
||||
return
|
||||
|
||||
# 查找所有JSONL文件
|
||||
jsonl_pattern = str(self.directory / "**" / "*.jsonl")
|
||||
jsonl_files = glob.glob(jsonl_pattern, recursive=True)
|
||||
|
||||
self.stats['total_files'] = len(jsonl_files)
|
||||
logger.info(f"找到 {len(jsonl_files)} 个JSONL文件")
|
||||
|
||||
for jsonl_file in jsonl_files:
|
||||
try:
|
||||
is_complete = self.check_workflow_completion(jsonl_file)
|
||||
|
||||
if is_complete:
|
||||
self.stats['complete_files'] += 1
|
||||
else:
|
||||
self.stats['incomplete_files'] += 1
|
||||
|
||||
if self.dry_run:
|
||||
logger.info(f"[试运行] 将删除不完整文件: {jsonl_file}")
|
||||
self.stats['deleted_files'].append(jsonl_file)
|
||||
else:
|
||||
os.remove(jsonl_file)
|
||||
logger.info(f"已删除不完整文件: {jsonl_file}")
|
||||
self.stats['deleted_files'].append(jsonl_file)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理文件时发生错误 {jsonl_file}: {e}")
|
||||
self.stats['error_files'].append(jsonl_file)
|
||||
|
||||
def print_summary(self) -> None:
|
||||
"""打印统计摘要"""
|
||||
print("\n" + "="*60)
|
||||
print("工作流文件清理摘要")
|
||||
print("="*60)
|
||||
print(f"总文件数: {self.stats['total_files']}")
|
||||
print(f"完整文件数: {self.stats['complete_files']}")
|
||||
print(f"不完整文件数: {self.stats['incomplete_files']}")
|
||||
print(f"删除文件数: {len(self.stats['deleted_files'])}")
|
||||
print(f"错误文件数: {len(self.stats['error_files'])}")
|
||||
|
||||
if self.stats['deleted_files']:
|
||||
print("\n已删除的文件:")
|
||||
for file in self.stats['deleted_files']:
|
||||
print(f" - {file}")
|
||||
|
||||
if self.stats['error_files']:
|
||||
print("\n处理错误的文件:")
|
||||
for file in self.stats['error_files']:
|
||||
print(f" - {file}")
|
||||
|
||||
if self.dry_run and self.stats['deleted_files']:
|
||||
print(f"\n注意: 这是试运行模式，实际上没有删除任何文件")
|
||||
|
||||
def run(self) -> Dict[str, Any]:
|
||||
"""
|
||||
运行清理器
|
||||
|
||||
Returns:
|
||||
Dict: 包含统计信息的字典
|
||||
"""
|
||||
logger.info(f"开始检查目录: {self.directory}")
|
||||
if self.dry_run:
|
||||
logger.info("运行在试运行模式")
|
||||
|
||||
self.scan_and_clean_files()
|
||||
self.print_summary()
|
||||
|
||||
return self.stats
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
parser = argparse.ArgumentParser(description='工作流文件清理器')
|
||||
parser.add_argument('directory', nargs='?', default='results/results0903',
|
||||
help='要检查的目录路径 (默认: results)')
|
||||
parser.add_argument('--dry-run', action='store_true',
|
||||
help='试运行模式，不实际删除文件')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
cleaner = WorkflowFileCleaner(args.directory, args.dry_run)
|
||||
cleaner.run()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user