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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import json
import os
from pathlib import Path
def load_scores_for_model(model_results_dir: Path):
scores_by_id = {}
raw_results_file = model_results_dir / "raw_results.jsonl"
if not raw_results_file.exists():
print(f"警告: 未找到模型 {model_results_dir.name} 的结果文件: {raw_results_file}")
return scores_by_id
print(f" 正在从 {model_results_dir.name}/raw_results.jsonl 加载分数...")
with open(raw_results_file, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
try:
data = json.loads(line.strip())
article_id = str(data.get('id'))
if not article_id:
print(f" 警告: {model_results_dir.name} 第 {i+1} 行缺少ID，已跳过。")
continue
overall_score_raw = data.get('overall_score', 0.0)
overall_score_scaled = overall_score_raw * 100
comprehensiveness_score_raw = data.get('comprehensiveness', 0.0)
insight_score_raw = data.get('insight', 0.0)
instruction_score_raw = data.get('instruction_following', 0.0)
readability_score_raw = data.get('readability', 0.0)
scores_by_id[article_id] = {
'overall_score': f"{overall_score_scaled:.2f}",
'comprehensiveness_score': f"{comprehensiveness_score_raw * 100:.2f}",
'insight_score': f"{insight_score_raw * 100:.2f}",
'instruction_following_score': f"{instruction_score_raw * 100:.2f}",
'readability_score': f"{readability_score_raw * 100:.2f}"
}
except json.JSONDecodeError as e:
print(f" 错误: 解析JSON时出错 (文件: {model_results_dir.name}, 行: {i+1}): {e}")
except Exception as e:
print(f" 错误: 处理数据时出错 (文件: {model_results_dir.name}, 行: {i+1}): {e}")
print(f" 为模型 {model_results_dir.name} 加载了 {len(scores_by_id)}篇文章的分数")
return scores_by_id
def merge_jsonl_files():
project_root = Path(__file__).resolve().parent.parent
raw_data_dir = project_root / "data" / "raw_data"
raw_results_dir = project_root / "data" / "raw_results"
output_file = project_root / "data" / "data_viewer.jsonl"
input_files = list(raw_data_dir.glob("*.jsonl"))
print(f"在 {raw_data_dir} 中找到 {len(input_files)} 个模型JSONL文件")
if not input_files:
print("未找到任何原始数据文件，已退出。")
return
with open(output_file, 'w', encoding='utf-8') as f:
pass
all_merged_data = []
for raw_data_file in input_files:
model_name = raw_data_file.stem
print(f"正在处理原始数据文件: {raw_data_file.name} (模型: {model_name})")
model_results_dir = raw_results_dir / model_name
if not model_results_dir.exists():
print(f" 警告: 未找到模型 {model_name} 对应的结果文件夹: {model_results_dir}")
continue
scores_for_current_model = load_scores_for_model(model_results_dir)
processed_articles_count = 0
with open(raw_data_file, 'r', encoding='utf-8') as f_raw:
for i, line in enumerate(f_raw):
try:
article_data = json.loads(line.strip())
article_id = str(article_data.get('id'))
if not article_id:
print(f" 警告: {raw_data_file.name} 第 {i+1} 行缺少ID，已跳过。")
continue
article_scores = scores_for_current_model.get(article_id, {})
if not article_scores:
print(f" 警告: 模型 {model_name} 的文章ID {article_id} 未在结果文件中找到分数。")
merged_item = {
'model_name': model_name,
'id': article_id,
'prompt': article_data.get('prompt'),
'article': article_data.get('article'),
'overall_score': article_scores.get('overall_score'),
'comprehensiveness_score': article_scores.get('comprehensiveness_score'),
'insight_score': article_scores.get('insight_score'),
'instruction_following_score': article_scores.get('instruction_following_score'),
'readability_score': article_scores.get('readability_score')
}
all_merged_data.append(merged_item)
processed_articles_count += 1
except json.JSONDecodeError as e:
print(f" 错误: 解析原始数据JSON时出错 (文件: {raw_data_file.name}, 行: {i+1}): {e}")
except Exception as e:
print(f" 错误: 处理原始数据时出错 (文件: {raw_data_file.name}, 行: {i+1}): {e}")
print(f" 为模型 {model_name} 处理了 {processed_articles_count} 篇文章数据。")
with open(output_file, 'w', encoding='utf-8') as f_out:
for item in all_merged_data:
f_out.write(json.dumps(item, ensure_ascii=False) + '\n')
print(f"\n成功合并并保存到: {output_file}, 共 {len(all_merged_data)} 条记录")
if __name__ == "__main__":
merge_jsonl_files()
print("所有文件处理完成！") |