metagpt----ext----aflow----benchmark----README.md

Custom Evaluation Function via Benchmark Class

How to Use

To create a benchmark for a new dataset, follow these steps:

1. Create a new Python file, e.g., my_dataset_benchmark.py

2. Import the base class:

   from metagpt.ext.aflow.benchmark.benchmark import BaseBenchmark

3. Create a new class that inherits from BaseBenchmark:

   class MyDatasetBenchmark(BaseBenchmark):
       def __init__(self, name: str, file_path: str, log_path: str):
           super().__init__(name, file_path, log_path)

4. Implement the required abstract methods:

   • evaluate_problem: Evaluate a single problem
   • calculate_score: Calculate the score for a prediction
   • get_result_columns: Define column names for the results CSV file

5. Override other methods as needed, such as load_data or save_results_to_csv

Example

Refer to the DROPBenchmark class in the drop.py file for an example of how to implement a benchmark for a specific dataset.

By following these guidelines, you can easily create custom benchmark evaluations for new datasets.