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- """
- 第十二章示例3:BFCL自定义评估
- 对应文档:12.2.5 在HelloAgents中实现BFCL评估 - 方式3
- 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")
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