Benchmarking the capabilities of R&D is a crucial research problem in this area. We are continuously exploring methods to benchmark these capabilities. The current benchmarks are listed on this page.
The default value for ``bench_test_round`` is 10, which takes about 2 hours to run. To modify it from ``10`` to ``2``, adjust the environment variables in the .env file as shown below.
The sample data in ``bench_data_path`` is a dictionary where each key represents a factor name. The value associated with each key is factor data containing the following information:
Ensure the data is placed in the ``FACTOR_COSTEER_SETTINGS.data_folder_debug``. The data files should be in ``.h5`` or ``.md`` format and must not be stored in any subfolders. LLM-Agents will review the file content and implement the tasks.
.. TODO: Add a script to automatically generate the data in the `rdagent/app/quant_factor_benchmark/data` folder.
The ``analysis.py`` script reads data from the pkl file and converts it to an image. Modify the Python code in ``rdagent/app/quant_factor_benchmark/analysis.py`` to specify the path to the pkl file and the output path for the png file.
We have developed a comprehensive benchmark called RD2Bench to assess data and model R&D capabilities. This benchmark includes a series of tasks that outline the features or structures of models. These tasks are used to evaluate the ability of LLM-Agents to implement them.
..code-block::bibtex
@misc{chen2024datacentric,
title={Towards Data-Centric Automatic R&D},
author={Haotian Chen and Xinjie Shen and Zeqi Ye and Wenjun Feng and Haoxue Wang and Xiao Yang and Xu Yang and Weiqing Liu and Jiang Bian},
To replicate the benchmark detailed in the paper, please consult the factors listed in the following file: `RD2bench.json <../_static/RD2bench.json>`_.
Please note use ``only_correct_format=False`` when evaluating the results.