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"""
第十二章示例3：
对应文档：
这个示例展示如何使用底层组件进行自定义评估流程。
适合需要自定义评估流程的场景。
"""
from hello_agents import SimpleAgent , HelloAgentsLLM
from hello_agents . evaluation import BFCLDataset , BFCLEvaluator
# 1. 创建智能体
llm = HelloAgentsLLM ( )
agent = SimpleAgent ( name = " TestAgent " , llm = llm )
# 2. 加载数据集
dataset = BFCLDataset (
bfcl_data_dir = " ./temp_gorilla/berkeley-function-call-leaderboard/bfcl_eval/data " ,
category = " simple_python "
)
data = dataset . load ( )
print ( f " ✅ 加载了 { len ( data ) } 个测试样本 " )
# 3. 创建评估器
evaluator = BFCLEvaluator (
dataset = dataset ,
category = " simple_python "
)
# 4. 运行评估
results = evaluator . evaluate (
agent = agent ,
max_samples = 5 # 只评估5个样本
)
# 5. 查看详细结果
print ( f " \n 评估结果: " )
print ( f " 总样本数: { results [ ' total_samples ' ] } " )
print ( f " 正确样本数: { results [ ' correct_samples ' ] } " )
print ( f " 准确率: { results [ ' overall_accuracy ' ] : .2% } " )
# 6. 查看每个样本的详细结果
print ( f " \n 详细结果: " )
for detail in results [ ' detailed_results ' ] :
print ( f " 样本 { detail [ ' sample_id ' ] } : " )
print ( f " 问题: { detail [ ' question ' ] [ : 50 ] } ... " )
print ( f " 预测: { detail [ ' predicted ' ] } " )
print ( f " 正确答案: { detail [ ' expected ' ] } " )
print ( f " 结果: { ' ✅ 正确 ' if detail [ ' success ' ] else ' ❌ 错误 ' } " )
print ( )
# 7. 导出结果
evaluator . export_results (
results ,
output_file = " ./evaluation_results/bfcl_custom_result.json "
)
print ( " ✅ 结果已导出到 ./evaluation_results/bfcl_custom_result.json " )