import json import os import pandas as pd import plotly.graph_objects as go from typing import Dict, List 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 create_comparison_tables() -> Dict[str, pd.DataFrame]: """创建不同维度的对比表格""" # 动态获取所有模型名称 models = get_model_names() # 加载所有报告 reports = {model: load_report(model) for model in models} # 1. 整体表现 overall_data = [] for model, report in reports.items(): overall = report['overall'] overall_data.append({ '模型': model, '总题数': overall['total'], '正确数': overall['correct'], '准确率': f"{overall['accuracy']*100:.2f}%" }) overall_df = pd.DataFrame(overall_data) # 2. 按题型分类 type_data = [] for model, report in reports.items(): for q_type, metrics in report['by_type'].items(): type_data.append({ '模型': model, '题型': q_type, '总题数': metrics['total'], '正确数': metrics['correct'], '准确率': f"{metrics['accuracy']*100:.2f}%" }) type_df = pd.DataFrame(type_data) # 3. 按难度分类 difficulty_data = [] difficulty_order = ['easy', 'medium', 'hard'] # 预定义难度顺序 for model, report in reports.items(): for diff in difficulty_order: if diff in report['by_difficulty']: metrics = report['by_difficulty'][diff] difficulty_data.append({ '模型': model, '难度': diff, '总题数': metrics['total'], '正确数': metrics['correct'], '准确率': f"{metrics['accuracy']*100:.2f}%" }) difficulty_df = pd.DataFrame(difficulty_data) # 4. 按朝代分类 dynasty_data = [] for model, report in reports.items(): for dynasty, metrics in report['by_dynasty'].items(): dynasty_data.append({ '模型': model, '朝代': dynasty if dynasty else "未知", '总题数': metrics['total'], '正确数': metrics['correct'], '准确率': f"{metrics['accuracy']*100:.2f}%" }) dynasty_df = pd.DataFrame(dynasty_data) return { 'overall': overall_df, 'by_type': type_df, 'by_difficulty': difficulty_df, 'by_dynasty': dynasty_df } def plot_accuracy_comparison(dfs: Dict[str, pd.DataFrame]): """绘制准确率对比图""" # 1. 整体准确率对比 fig = go.Figure(data=[ go.Bar( name='整体准确率', x=dfs['overall']['模型'], y=[float(x.strip('%')) for x in dfs['overall']['准确率']], text=dfs['overall']['准确率'], textposition='auto', ) ]) fig.update_layout( title='各模型整体准确率对比', yaxis_title='准确率 (%)', yaxis_range=[0, 100], template='plotly_white' ) fig.write_html('accuracy_comparison.html') # 2. 按题型的准确率对比 type_pivot = pd.pivot_table( dfs['by_type'], values='准确率', index='题型', columns='模型', aggfunc=lambda x: x ) fig = go.Figure(data=[ go.Bar( name=model, x=type_pivot.index, y=[float(x.strip('%')) for x in type_pivot[model]], text=type_pivot[model], textposition='auto', ) for model in type_pivot.columns ]) fig.update_layout( title='各模型在不同题型上的准确率对比', yaxis_title='准确率 (%)', yaxis_range=[0, 100], barmode='group', template='plotly_white', height=600 # 增加图表高度 ) fig.write_html('accuracy_by_type.html') # 3. 按难度的准确率对比 difficulty_pivot = pd.pivot_table( dfs['by_difficulty'], values='准确率', index='难度', columns='模型', aggfunc=lambda x: x ) # 确保难度顺序正确 difficulty_order = ['easy', 'medium', 'hard'] difficulty_pivot = difficulty_pivot.reindex(difficulty_order) fig = go.Figure(data=[ go.Bar( name=model, x=difficulty_pivot.index, y=[float(x.strip('%')) for x in difficulty_pivot[model]], text=difficulty_pivot[model], textposition='auto', ) for model in difficulty_pivot.columns ]) fig.update_layout( title='各模型在不同难度上的准确率对比', yaxis_title='准确率 (%)', yaxis_range=[0, 100], barmode='group', template='plotly_white' ) fig.write_html('accuracy_by_difficulty.html') # 4. 按朝代的准确率对比 dynasty_pivot = pd.pivot_table( dfs['by_dynasty'], values='准确率', index='朝代', columns='模型', aggfunc=lambda x: x ) fig = go.Figure(data=[ go.Bar( name=model, x=dynasty_pivot.index, y=[float(x.strip('%')) for x in dynasty_pivot[model]], text=dynasty_pivot[model], textposition='auto', ) for model in dynasty_pivot.columns ]) fig.update_layout( title='各模型在不同朝代诗词上的准确率对比', yaxis_title='准确率 (%)', yaxis_range=[0, 100], barmode='group', template='plotly_white', height=800 # 增加图表高度以适应更多朝代 ) fig.write_html('accuracy_by_dynasty.html') # 5. 雷达图对比 categories = ['整体'] + list(type_pivot.index) fig = go.Figure() for model in dfs['overall']['模型']: values = [float(dfs['overall'][dfs['overall']['模型']==model]['准确率'].iloc[0].strip('%'))] for q_type in type_pivot.index: values.append(float(type_pivot[model][q_type].strip('%'))) fig.add_trace(go.Scatterpolar( r=values, theta=categories, name=model, fill='toself' )) fig.update_layout( polar=dict( radialaxis=dict( visible=True, range=[0, 100] )), showlegend=True, title='各模型在不同维度的表现对比(雷达图)', template='plotly_white' ) fig.write_html('radar_comparison.html') def main(): # 创建对比表格 dfs = create_comparison_tables() # 保存为Excel文件 with pd.ExcelWriter('model_comparison.xlsx') as writer: dfs['overall'].to_excel(writer, sheet_name='整体表现', index=False) dfs['by_type'].to_excel(writer, sheet_name='按题型分类', index=False) dfs['by_difficulty'].to_excel(writer, sheet_name='按难度分类', index=False) dfs['by_dynasty'].to_excel(writer, sheet_name='按朝代分类', index=False) # 打印表格 print("\n整体表现:") print(dfs['overall'].to_string(index=False)) print("\n按题型分类:") print(dfs['by_type'].to_string(index=False)) print("\n按难度分类:") print(dfs['by_difficulty'].to_string(index=False)) print("\n按朝代分类:") print(dfs['by_dynasty'].to_string(index=False)) # 绘制对比图 plot_accuracy_comparison(dfs) if __name__ == '__main__': main()