generate_report.py

import json
import os
from typing import Dict, List
from datetime import datetime

def get_model_names() -> List[str]:
    """从evaluation_results文件夹获取所有模型名称"""
    model_names = []
    for item in os.listdir('.'):
        if item.startswith('evaluation_results.') and os.path.isdir(item):
            model_name = item.replace('evaluation_results.', '')
            model_names.append(model_name)
    return sorted(model_names)  # 排序以保持顺序一致

def load_report(model_name: str) -> Dict:
    """加载模型的评测报告"""
    report_path = f"evaluation_results.{model_name}/evaluation_report.json"
    with open(report_path, 'r', encoding='utf-8') as f:
        return json.load(f)

def format_percentage(value: float) -> str:
    """格式化百分比显示"""
    return f"{value*100:.2f}%"

def generate_markdown_report():
    """生成markdown格式的评测报告"""
    # 动态获取模型列表
    models = get_model_names()
    
    # 加载所有报告
    reports = {model: load_report(model) for model in models}
    
    # 生成报告内容
    report = []
    
    # 标题和时间
    report.append("# 中国古诗词大模型评测报告")
    report.append(f"生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
    
    # 添加模型信息
    report.append(f"评测模型: {', '.join(models)}\n")
    
    # 1. 整体表现对比
    report.append("## 1. 整体表现对比")
    
    # 获取所有题型
    all_types = sorted(set(
        q_type
        for data in reports.values()
        for q_type in data['by_type'].keys()
    ))
    
    # 生成表头
    headers = ["模型"] + all_types + ["总计", "总正确率"]
    report.append("\n| " + " | ".join(headers) + " |")
    report.append("| " + " | ".join(["----" for _ in headers]) + " |")
    
    # 按总正确率排序
    sorted_models = sorted(reports.items(), 
                         key=lambda x: x[1]['overall']['accuracy'],
                         reverse=True)
    
    # 生成每个模型的数据行
    for model, data in sorted_models:
        row = [model]
        # 添加每个题型的数据
        for q_type in all_types:
            if q_type in data['by_type']:
                metrics = data['by_type'][q_type]
                row.append(f"{metrics['correct']}/{metrics['total']}")
            else:
                row.append("0/0")
        
        # 添加总计和总正确率
        overall = data['overall']
        row.append(f"{overall['correct']}/{overall['total']}")
        row.append(format_percentage(overall['accuracy']))
        
        report.append("| " + " | ".join(row) + " |")
    report.append("")
    
    # 2. 按题型分类表现
    report.append("## 2. 题型分类表现")
    report.append("\n| 模型 | 题型 | 总题数 | 正确数 | 准确率 |")
    report.append("| --- | --- | --- | --- | --- |")
    
    # 获取所有题型
    all_types = sorted(set(
        q_type
        for data in reports.values()
        for q_type in data['by_type'].keys()
    ))
    
    for q_type in all_types:
        type_results = []
        for model, data in reports.items():
            if q_type in data['by_type']:
                metrics = data['by_type'][q_type]
                type_results.append((model, metrics))
        
        # 按准确率排序
        sorted_results = sorted(type_results, 
                              key=lambda x: x[1]['accuracy'],
                              reverse=True)
        
        for model, metrics in sorted_results:
            report.append(f"| {model} | {q_type} | {metrics['total']} | {metrics['correct']} | {format_percentage(metrics['accuracy'])} |")
        report.append("| --- | --- | --- | --- | --- |")  # 添加分隔线
    report.append("")
    
    # 3. 难度分布表现
    report.append("## 3. 难度分布表现")
    report.append("\n| 模型 | 难度 | 总题数 | 正确数 | 准确率 |")
    report.append("| --- | --- | --- | --- | --- |")
    
    # 难度顺序
    difficulty_order = ['easy', 'medium', 'hard']
    
    for diff in difficulty_order:
        diff_results = []
        for model, data in reports.items():
            if diff in data['by_difficulty']:
                metrics = data['by_difficulty'][diff]
                diff_results.append((model, metrics))
        
        # 按准确率排序
        sorted_results = sorted(diff_results,
                              key=lambda x: x[1]['accuracy'],
                              reverse=True)
        
        for model, metrics in sorted_results:
            report.append(f"| {model} | {diff} | {metrics['total']} | {metrics['correct']} | {format_percentage(metrics['accuracy'])} |")
        report.append("| --- | --- | --- | --- | --- |")  # 添加分隔线
    report.append("")
    
    # 4. 朝代分布表现
    report.append("## 4. 朝代分布表现")
    report.append("\n| 模型 | 朝代 | 总题数 | 正确数 | 准确率 |")
    report.append("| --- | --- | --- | --- | --- |")
    
    # 获取所有朝代并排序
    all_dynasties = sorted(set(
        dynasty if dynasty else "未知"
        for data in reports.values()
        for dynasty in data['by_dynasty'].keys()
    ))
    
    for dynasty in all_dynasties:
        dynasty_results = []
        for model, data in reports.items():
            if dynasty in data['by_dynasty'] or (dynasty == "未知" and None in data['by_dynasty']):
                metrics = data['by_dynasty'][None if dynasty == "未知" else dynasty]
                dynasty_results.append((model, metrics))
        
        # 按准确率排序
        sorted_results = sorted(dynasty_results,
                              key=lambda x: x[1]['accuracy'],
                              reverse=True)
        
        for model, metrics in sorted_results:
            report.append(f"| {model} | {dynasty} | {metrics['total']} | {metrics['correct']} | {format_percentage(metrics['accuracy'])} |")
        report.append("| --- | --- | --- | --- | --- |")  # 添加分隔线
    report.append("")
    
    # 5. 结论分析
    report.append("## 5. 结论分析")
    report.append("\n### 5.1 整体表现")
    
    # 计算最佳表现模型
    best_model = max(reports.items(), key=lambda x: x[1]['overall']['accuracy'])
    report.append(f"- 最佳表现模型: {best_model[0]}, 整体准确率 {format_percentage(best_model[1]['overall']['accuracy'])}")
    
    # 计算各个维度的最佳表现
    report.append("\n### 5.2 分维度最佳表现")
    
    # 题型维度
    report.append("\n#### 题型维度:")
    for q_type in all_types:
        best = max(reports.items(), 
                  key=lambda x: x[1]['by_type'][q_type]['accuracy'])
        report.append(f"- {q_type}: {best[0]} ({format_percentage(best[1]['by_type'][q_type]['accuracy'])})")
    
    # 难度维度
    report.append("\n#### 难度维度:")
    for diff in difficulty_order:
        best = max(reports.items(),
                  key=lambda x: x[1]['by_difficulty'][diff]['accuracy'])
        report.append(f"- {diff}: {best[0]} ({format_percentage(best[1]['by_difficulty'][diff]['accuracy'])})")
    
    # 写入文件
    with open('evaluation_report.md', 'w', encoding='utf-8') as f:
        f.write('\n'.join(report))

if __name__ == '__main__':
    generate_markdown_report()