Full Code of microsoft/RD-Agent for AI

gitextract_5wquvenf/

├── .bumpversion.cfg
├── .commitlintrc.js
├── .devcontainer/
│   ├── Dockerfile
│   ├── README.md
│   ├── devcontainer.json
│   └── env
├── .github/
│   ├── FUNDING.yml
│   ├── ISSUE_TEMPLATE/
│   │   ├── bug-report.md
│   │   ├── documentation.md
│   │   ├── feature-request.md
│   │   └── question.md
│   ├── PULL_REQUEST_TEMPLATE.md
│   ├── dependabot.yml
│   └── workflows/
│       ├── ci.yml
│       ├── pr.yml
│       ├── readthedocs-preview.yml
│       └── release.yml
├── .gitignore
├── .readthedocs.yaml
├── .streamlit/
│   └── config.toml
├── CHANGELOG.md
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── Makefile
├── README.md
├── SECURITY.md
├── SUPPORT.md
├── TODO.md
├── constraints/
│   ├── 3.10.txt
│   └── 3.11.txt
├── docs/
│   ├── Makefile
│   ├── _static/
│   │   └── RD2bench.json
│   ├── api_reference.rst
│   ├── changelog.md
│   ├── conf.py
│   ├── development.rst
│   ├── index.rst
│   ├── installation_and_configuration.rst
│   ├── introduction.rst
│   ├── make.bat
│   ├── policy.rst
│   ├── project_framework_introduction.rst
│   ├── requirements.txt
│   ├── research/
│   │   ├── benchmark.rst
│   │   ├── catalog.rst
│   │   └── dev.rst
│   ├── scens/
│   │   ├── catalog.rst
│   │   ├── data_agent_fin.rst
│   │   ├── data_copilot_fin.rst
│   │   ├── data_science.rst
│   │   ├── finetune.rst
│   │   ├── model_agent_fin.rst
│   │   ├── model_copilot_general.rst
│   │   └── quant_agent_fin.rst
│   └── ui.rst
├── pyproject.toml
├── rdagent/
│   ├── app/
│   │   ├── CI/
│   │   │   ├── README.md
│   │   │   ├── ci.ipynb
│   │   │   ├── prompts.yaml
│   │   │   └── run.py
│   │   ├── benchmark/
│   │   │   ├── factor/
│   │   │   │   ├── analysis.py
│   │   │   │   └── eval.py
│   │   │   └── model/
│   │   │       ├── README.md
│   │   │       └── eval.py
│   │   ├── cli.py
│   │   ├── data_science/
│   │   │   ├── conf.py
│   │   │   ├── debug.py
│   │   │   └── loop.py
│   │   ├── finetune/
│   │   │   ├── data_science/
│   │   │   │   ├── conf.py
│   │   │   │   ├── loop.py
│   │   │   │   ├── scen.py
│   │   │   │   └── tpl/
│   │   │   │       ├── components/
│   │   │   │       │   └── coder/
│   │   │   │       │       └── data_science/
│   │   │   │       │           └── pipeline/
│   │   │   │       │               └── prompts.yaml
│   │   │   │       └── scenarios/
│   │   │   │           └── data_science/
│   │   │   │               └── proposal/
│   │   │   │                   └── exp_gen/
│   │   │   │                       └── prompts_v2.yaml
│   │   │   ├── llm/
│   │   │   │   ├── README.md
│   │   │   │   ├── conf.py
│   │   │   │   ├── job/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── run_ft_job.sh
│   │   │   │   │   ├── scenarios.json
│   │   │   │   │   └── tasks.json.example
│   │   │   │   ├── loop.py
│   │   │   │   └── ui/
│   │   │   │       ├── __init__.py
│   │   │   │       ├── app.py
│   │   │   │       ├── benchmarks/
│   │   │   │       │   ├── __init__.py
│   │   │   │       │   ├── base.py
│   │   │   │       │   ├── bioprobench.py
│   │   │   │       │   ├── chemcotbench.py
│   │   │   │       │   ├── financeiq.py
│   │   │   │       │   ├── panorama.py
│   │   │   │       │   └── tablebench.py
│   │   │   │       ├── components.py
│   │   │   │       ├── config.py
│   │   │   │       ├── data_loader.py
│   │   │   │       └── ft_summary.py
│   │   │   └── share/
│   │   │       ├── eval.py
│   │   │       └── prompts.yaml
│   │   ├── general_model/
│   │   │   └── general_model.py
│   │   ├── kaggle/
│   │   │   ├── conf.py
│   │   │   └── loop.py
│   │   ├── qlib_rd_loop/
│   │   │   ├── conf.py
│   │   │   ├── factor.py
│   │   │   ├── factor_from_report.py
│   │   │   ├── model.py
│   │   │   ├── prompts.yaml
│   │   │   └── quant.py
│   │   ├── rl/
│   │   │   ├── conf.py
│   │   │   ├── loop.py
│   │   │   └── ui/
│   │   │       ├── __init__.py
│   │   │       ├── app.py
│   │   │       ├── components.py
│   │   │       ├── config.py
│   │   │       ├── data_loader.py
│   │   │       └── rl_summary.py
│   │   └── utils/
│   │       ├── ape.py
│   │       ├── health_check.py
│   │       ├── info.py
│   │       ├── prompts.yaml
│   │       ├── ws.py
│   │       └── ws_ft.py
│   ├── components/
│   │   ├── agent/
│   │   │   ├── __init__.py
│   │   │   ├── base.py
│   │   │   ├── context7/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── conf.py
│   │   │   │   └── prompts.yaml
│   │   │   ├── mcp/
│   │   │   │   └── __init__.py
│   │   │   └── rag/
│   │   │       ├── __init__.py
│   │   │       └── conf.py
│   │   ├── benchmark/
│   │   │   ├── __init__.py
│   │   │   ├── conf.py
│   │   │   ├── configs/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── models.yaml
│   │   │   │   └── opencompass_template.yaml
│   │   │   ├── eval_method.py
│   │   │   ├── example.json
│   │   │   └── utils.py
│   │   ├── coder/
│   │   │   ├── CoSTEER/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── config.py
│   │   │   │   ├── evaluators.py
│   │   │   │   ├── evolvable_subjects.py
│   │   │   │   ├── evolving_strategy.py
│   │   │   │   ├── knowledge_management.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   └── task.py
│   │   │   ├── data_science/
│   │   │   │   ├── conf.py
│   │   │   │   ├── ensemble/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── conf.py
│   │   │   │   │   ├── eval.py
│   │   │   │   │   ├── eval_tests/
│   │   │   │   │   │   └── ensemble_test.txt
│   │   │   │   │   ├── exp.py
│   │   │   │   │   ├── prompts.yaml
│   │   │   │   │   └── test.py
│   │   │   │   ├── feature/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── eval.py
│   │   │   │   │   ├── eval_tests/
│   │   │   │   │   │   └── feature_test.txt
│   │   │   │   │   ├── exp.py
│   │   │   │   │   ├── prompts.yaml
│   │   │   │   │   └── test.py
│   │   │   │   ├── model/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── eval.py
│   │   │   │   │   ├── eval_tests/
│   │   │   │   │   │   └── model_test.txt
│   │   │   │   │   ├── exp.py
│   │   │   │   │   ├── prompts.yaml
│   │   │   │   │   └── test.py
│   │   │   │   ├── pipeline/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── eval.py
│   │   │   │   │   ├── eval_tests/
│   │   │   │   │   │   └── submission_format_test.txt
│   │   │   │   │   ├── exp.py
│   │   │   │   │   └── prompts.yaml
│   │   │   │   ├── raw_data_loader/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── conf.py
│   │   │   │   │   ├── eval.py
│   │   │   │   │   ├── eval_tests/
│   │   │   │   │   │   └── data_loader_test.txt
│   │   │   │   │   ├── exp.py
│   │   │   │   │   ├── prompts.yaml
│   │   │   │   │   └── test.py
│   │   │   │   ├── share/
│   │   │   │   │   ├── doc.py
│   │   │   │   │   ├── ds_costeer.py
│   │   │   │   │   ├── eval.py
│   │   │   │   │   ├── notebook.py
│   │   │   │   │   ├── prompts.yaml
│   │   │   │   │   └── util.py
│   │   │   │   ├── utils.py
│   │   │   │   └── workflow/
│   │   │   │       ├── __init__.py
│   │   │   │       ├── eval.py
│   │   │   │       ├── eval_tests/
│   │   │   │       │   └── submission_format_test.txt
│   │   │   │       ├── exp.py
│   │   │   │       ├── prompts.yaml
│   │   │   │       └── test.py
│   │   │   ├── factor_coder/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── config.py
│   │   │   │   ├── eva_utils.py
│   │   │   │   ├── evaluators.py
│   │   │   │   ├── evolving_strategy.py
│   │   │   │   ├── factor.py
│   │   │   │   ├── factor_execution_template.txt
│   │   │   │   └── prompts.yaml
│   │   │   ├── finetune/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── conf.py
│   │   │   │   ├── eval.py
│   │   │   │   ├── exp.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   └── unified_validator.py
│   │   │   ├── model_coder/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── benchmark/
│   │   │   │   │   ├── eval.py
│   │   │   │   │   ├── gt_code/
│   │   │   │   │   │   ├── A-DGN.py
│   │   │   │   │   │   ├── dirgnn.py
│   │   │   │   │   │   ├── gpsconv.py
│   │   │   │   │   │   ├── linkx.py
│   │   │   │   │   │   ├── pmlp.py
│   │   │   │   │   │   └── visnet.py
│   │   │   │   │   └── model_dict.json
│   │   │   │   ├── conf.py
│   │   │   │   ├── eva_utils.py
│   │   │   │   ├── evaluators.py
│   │   │   │   ├── evolving_strategy.py
│   │   │   │   ├── gt_code.py
│   │   │   │   ├── model.py
│   │   │   │   ├── model_execute_template_v1.txt
│   │   │   │   ├── model_execute_template_v2.txt
│   │   │   │   ├── one_shot/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   └── prompt.yaml
│   │   │   │   ├── prompts.yaml
│   │   │   │   └── task_loader.py
│   │   │   └── rl/
│   │   │       ├── __init__.py
│   │   │       ├── costeer.py
│   │   │       └── prompts.yaml
│   │   ├── document_reader/
│   │   │   └── document_reader.py
│   │   ├── interactor/
│   │   │   └── __init__.py
│   │   ├── knowledge_management/
│   │   │   ├── graph.py
│   │   │   └── vector_base.py
│   │   ├── loader/
│   │   │   ├── experiment_loader.py
│   │   │   └── task_loader.py
│   │   ├── proposal/
│   │   │   ├── __init__.py
│   │   │   └── prompts.yaml
│   │   ├── runner/
│   │   │   └── __init__.py
│   │   └── workflow/
│   │       ├── conf.py
│   │       └── rd_loop.py
│   ├── core/
│   │   ├── conf.py
│   │   ├── developer.py
│   │   ├── evaluation.py
│   │   ├── evolving_agent.py
│   │   ├── evolving_framework.py
│   │   ├── exception.py
│   │   ├── experiment.py
│   │   ├── interactor.py
│   │   ├── knowledge_base.py
│   │   ├── prompts.py
│   │   ├── proposal.py
│   │   ├── scenario.py
│   │   └── utils.py
│   ├── log/
│   │   ├── __init__.py
│   │   ├── base.py
│   │   ├── conf.py
│   │   ├── logger.py
│   │   ├── mle_summary.py
│   │   ├── server/
│   │   │   ├── README.md
│   │   │   ├── app.py
│   │   │   └── debug_app.py
│   │   ├── storage.py
│   │   ├── timer.py
│   │   ├── ui/
│   │   │   ├── __init__.py
│   │   │   ├── aide.py
│   │   │   ├── app.py
│   │   │   ├── conf.py
│   │   │   ├── ds_summary.py
│   │   │   ├── ds_trace.py
│   │   │   ├── ds_user_interact.py
│   │   │   ├── dsapp.py
│   │   │   ├── llm_st.py
│   │   │   ├── qlib_report_figure.py
│   │   │   ├── st_fixed_container.py
│   │   │   ├── storage.py
│   │   │   ├── utils.py
│   │   │   └── web.py
│   │   └── utils/
│   │       ├── __init__.py
│   │       └── folder.py
│   ├── oai/
│   │   ├── backend/
│   │   │   ├── __init__.py
│   │   │   ├── base.py
│   │   │   ├── deprec.py
│   │   │   ├── litellm.py
│   │   │   └── pydantic_ai.py
│   │   ├── llm_conf.py
│   │   ├── llm_utils.py
│   │   └── utils/
│   │       └── embedding.py
│   ├── scenarios/
│   │   ├── data_science/
│   │   │   ├── __init__.py
│   │   │   ├── debug/
│   │   │   │   └── data.py
│   │   │   ├── dev/
│   │   │   │   ├── feedback.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   └── runner/
│   │   │   │       ├── __init__.py
│   │   │   │       ├── eval.py
│   │   │   │       └── prompts.yaml
│   │   │   ├── eval_tests/
│   │   │   │   └── mle_submission_format_test.txt
│   │   │   ├── example/
│   │   │   │   ├── README.md
│   │   │   │   ├── arf-12-hours-prediction-task/
│   │   │   │   │   ├── description.md
│   │   │   │   │   └── sample.py
│   │   │   │   ├── eval/
│   │   │   │   │   ├── arf-12-hours-prediction-task/
│   │   │   │   │   │   ├── grade.py
│   │   │   │   │   │   └── valid.py
│   │   │   │   │   └── playground-series-s4e9/
│   │   │   │   │       ├── grade.py
│   │   │   │   │       └── valid.py
│   │   │   │   ├── playground-series-s4e9/
│   │   │   │   │   └── description.md
│   │   │   │   └── source_data/
│   │   │   │       ├── arf-12-hours-prediction-task/
│   │   │   │       │   └── prepare.py
│   │   │   │       └── playground-series-s4e9/
│   │   │   │           └── prepare.py
│   │   │   ├── experiment/
│   │   │   │   ├── __init__.py
│   │   │   │   └── experiment.py
│   │   │   ├── interactor/
│   │   │   │   └── __init__.py
│   │   │   ├── loop.py
│   │   │   ├── proposal/
│   │   │   │   ├── __init__.py
│   │   │   │   └── exp_gen/
│   │   │   │       ├── README.md
│   │   │   │       ├── __init__.py
│   │   │   │       ├── base.py
│   │   │   │       ├── diversity_strategy.py
│   │   │   │       ├── draft/
│   │   │   │       │   ├── draft.py
│   │   │   │       │   └── prompts_draft.yaml
│   │   │   │       ├── idea_pool.py
│   │   │   │       ├── merge.py
│   │   │   │       ├── merge.yaml
│   │   │   │       ├── naive.py
│   │   │   │       ├── naive.yaml
│   │   │   │       ├── package_info.py
│   │   │   │       ├── planner/
│   │   │   │       │   └── __init__.py
│   │   │   │       ├── prompts.yaml
│   │   │   │       ├── prompts_v2.yaml
│   │   │   │       ├── proposal.py
│   │   │   │       ├── router/
│   │   │   │       │   └── __init__.py
│   │   │   │       ├── select/
│   │   │   │       │   ├── expand.py
│   │   │   │       │   ├── prompts.yaml
│   │   │   │       │   └── submit.py
│   │   │   │       ├── trace_scheduler.py
│   │   │   │       └── utils.py
│   │   │   ├── scen/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   └── utils.py
│   │   │   ├── share.yaml
│   │   │   ├── sing_docker/
│   │   │   │   ├── Dockerfile
│   │   │   │   ├── entrypoint.sh
│   │   │   │   └── kaggle_environment.yaml
│   │   │   └── test_eval.py
│   │   ├── finetune/
│   │   │   ├── benchmark/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── benchmark.py
│   │   │   │   ├── configs/
│   │   │   │   │   ├── models.yaml
│   │   │   │   │   └── opencompass_template.yaml
│   │   │   │   ├── data/
│   │   │   │   │   ├── adaptor.py
│   │   │   │   │   ├── default.py
│   │   │   │   │   └── financeiq_gen.py
│   │   │   │   └── merge/
│   │   │   │       ├── __init__.py
│   │   │   │       ├── merge.py
│   │   │   │       └── merge_model_template.yaml
│   │   │   ├── datasets/
│   │   │   │   ├── README.md
│   │   │   │   ├── __init__.py
│   │   │   │   ├── bioprobench/
│   │   │   │   │   └── README.md
│   │   │   │   ├── chemcot/
│   │   │   │   │   ├── README.md
│   │   │   │   │   └── __init__.py
│   │   │   │   ├── deepscaler/
│   │   │   │   │   └── README.md
│   │   │   │   ├── financeiq/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   └── split.py
│   │   │   │   ├── panorama/
│   │   │   │   │   └── README.md
│   │   │   │   └── tableinstruct/
│   │   │   │       └── README.md
│   │   │   ├── dev/
│   │   │   │   ├── feedback.py
│   │   │   │   └── prompts.yaml
│   │   │   ├── download/
│   │   │   │   ├── __init__.py
│   │   │   │   └── hf.py
│   │   │   ├── env/
│   │   │   │   ├── conda/
│   │   │   │   │   ├── deepspeed/
│   │   │   │   │   │   ├── ds_z2_config.json
│   │   │   │   │   │   └── ds_z3_config.json
│   │   │   │   │   ├── llm_finetune_requirements.txt
│   │   │   │   │   └── opencompass_requirements.txt
│   │   │   │   └── docker/
│   │   │   │       ├── llm_finetune/
│   │   │   │       │   └── Dockerfile
│   │   │   │       └── opencompass/
│   │   │   │           └── Dockerfile
│   │   │   ├── experiment/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── experiment.py
│   │   │   │   └── workspace.py
│   │   │   ├── loop.py
│   │   │   ├── proposal/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   ├── proposal.py
│   │   │   │   └── trace.py
│   │   │   ├── scen/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── docker_scripts/
│   │   │   │   │   └── extract_parameters.py
│   │   │   │   ├── llama_factory_manager.py
│   │   │   │   ├── memory_estimator.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   ├── scenario.py
│   │   │   │   └── utils.py
│   │   │   ├── share.yaml
│   │   │   ├── train/
│   │   │   │   ├── eval.py
│   │   │   │   └── runner.py
│   │   │   └── utils.py
│   │   ├── general_model/
│   │   │   ├── prompts.yaml
│   │   │   └── scenario.py
│   │   ├── kaggle/
│   │   │   ├── README.md
│   │   │   ├── developer/
│   │   │   │   ├── coder.py
│   │   │   │   ├── feedback.py
│   │   │   │   └── runner.py
│   │   │   ├── docker/
│   │   │   │   ├── DS_docker/
│   │   │   │   │   └── Dockerfile
│   │   │   │   ├── kaggle_docker/
│   │   │   │   │   └── Dockerfile
│   │   │   │   └── mle_bench_docker/
│   │   │   │       └── Dockerfile
│   │   │   ├── experiment/
│   │   │   │   ├── README.md
│   │   │   │   ├── kaggle_experiment.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   ├── scenario.py
│   │   │   │   ├── spaceship-titanic_template/
│   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   ├── feature/
│   │   │   │   │   │   └── feature.py
│   │   │   │   │   ├── model/
│   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   └── train.py
│   │   │   │   ├── templates/
│   │   │   │   │   ├── covid19-global-forecasting-week-1/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── digit-recognizer/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_nn.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── feedback-prize-english-language-learning/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── forest-cover-type-prediction/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   ├── train.py
│   │   │   │   │   │   └── train_past.py
│   │   │   │   │   ├── meta_tpl_deprecated/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_nn.py
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   └── model_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── new-york-city-taxi-fare-prediction/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_linear.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_linear.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── optiver-realized-volatility-prediction/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── playground-series-s3e11/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── playground-series-s3e14/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── playground-series-s3e16/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── playground-series-s3e26/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── playground-series-s4e5/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── playground-series-s4e8/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── playground-series-s4e9/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── sf-crime/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── spaceship-titanic/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── statoil-iceberg-classifier-challenge/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── tabular-playground-series-dec-2021/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── tabular-playground-series-may-2022/
│   │   │   │   │   │   ├── fea_share_preprocess.py
│   │   │   │   │   │   ├── feature/
│   │   │   │   │   │   │   └── feature.py
│   │   │   │   │   │   ├── model/
│   │   │   │   │   │   │   ├── model_randomforest.py
│   │   │   │   │   │   │   ├── model_xgboost.py
│   │   │   │   │   │   │   ├── select_lightgbm.py
│   │   │   │   │   │   │   ├── select_nn.py
│   │   │   │   │   │   │   ├── select_randomforest.py
│   │   │   │   │   │   │   └── select_xgboost.py
│   │   │   │   │   │   └── train.py
│   │   │   │   │   └── ventilator-pressure-prediction/
│   │   │   │   │       ├── fea_share_preprocess.py
│   │   │   │   │       ├── feature/
│   │   │   │   │       │   └── feature.py
│   │   │   │   │       ├── model/
│   │   │   │   │       │   ├── model_randomforest.py
│   │   │   │   │       │   ├── model_xgboost.py
│   │   │   │   │       │   ├── select_lightgbm.py
│   │   │   │   │       │   ├── select_nn.py
│   │   │   │   │       │   ├── select_randomforest.py
│   │   │   │   │       │   └── select_xgboost.py
│   │   │   │   │       └── train.py
│   │   │   │   ├── utils.py
│   │   │   │   └── workspace.py
│   │   │   ├── kaggle_crawler.py
│   │   │   ├── knowledge_management/
│   │   │   │   ├── README.md
│   │   │   │   ├── extract_knowledge.py
│   │   │   │   ├── graph.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   └── vector_base.py
│   │   │   ├── prompts.yaml
│   │   │   ├── proposal/
│   │   │   │   └── proposal.py
│   │   │   └── tpl_ex/
│   │   │       ├── aerial-cactus-identification/
│   │   │       │   ├── README.md
│   │   │       │   ├── ensemble.py
│   │   │       │   ├── feature.py
│   │   │       │   ├── load_data.py
│   │   │       │   ├── main.py
│   │   │       │   ├── model01.py
│   │   │       │   └── spec/
│   │   │       │       ├── data_loader.md
│   │   │       │       ├── ensemble.md
│   │   │       │       ├── feature.md
│   │   │       │       ├── model.md
│   │   │       │       └── workflow.md
│   │   │       └── meta/
│   │   │           └── spec.md
│   │   ├── qlib/
│   │   │   ├── developer/
│   │   │   │   ├── factor_coder.py
│   │   │   │   ├── factor_runner.py
│   │   │   │   ├── feedback.py
│   │   │   │   ├── model_coder.py
│   │   │   │   ├── model_runner.py
│   │   │   │   └── utils.py
│   │   │   ├── docker/
│   │   │   │   └── Dockerfile
│   │   │   ├── experiment/
│   │   │   │   ├── factor_data_template/
│   │   │   │   │   ├── README.md
│   │   │   │   │   └── generate.py
│   │   │   │   ├── factor_experiment.py
│   │   │   │   ├── factor_from_report_experiment.py
│   │   │   │   ├── factor_template/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── conf_baseline.yaml
│   │   │   │   │   ├── conf_combined_factors.yaml
│   │   │   │   │   ├── conf_combined_factors_sota_model.yaml
│   │   │   │   │   └── read_exp_res.py
│   │   │   │   ├── model_experiment.py
│   │   │   │   ├── model_template/
│   │   │   │   │   ├── README.md
│   │   │   │   │   ├── conf_baseline_factors_model.yaml
│   │   │   │   │   ├── conf_sota_factors_model.yaml
│   │   │   │   │   └── read_exp_res.py
│   │   │   │   ├── prompts.yaml
│   │   │   │   ├── quant_experiment.py
│   │   │   │   ├── utils.py
│   │   │   │   └── workspace.py
│   │   │   ├── factor_experiment_loader/
│   │   │   │   ├── json_loader.py
│   │   │   │   ├── pdf_loader.py
│   │   │   │   └── prompts.yaml
│   │   │   ├── prompts.yaml
│   │   │   └── proposal/
│   │   │       ├── bandit.py
│   │   │       ├── factor_proposal.py
│   │   │       ├── model_proposal.py
│   │   │       └── quant_proposal.py
│   │   ├── rl/
│   │   │   ├── autorl_bench/
│   │   │   │   ├── .gitignore
│   │   │   │   ├── README.md
│   │   │   │   ├── __init__.py
│   │   │   │   ├── agents/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── claude/
│   │   │   │   │   │   ├── AGENTS.md
│   │   │   │   │   │   ├── config.yaml
│   │   │   │   │   │   └── start.sh
│   │   │   │   │   ├── codex/
│   │   │   │   │   │   ├── AGENTS.md
│   │   │   │   │   │   ├── config.yaml
│   │   │   │   │   │   └── start.sh
│   │   │   │   │   ├── example_agent/
│   │   │   │   │   │   ├── config.yaml
│   │   │   │   │   │   ├── start.sh
│   │   │   │   │   │   └── train.py
│   │   │   │   │   ├── gemini/
│   │   │   │   │   │   ├── AGENTS.md
│   │   │   │   │   │   ├── config.yaml
│   │   │   │   │   │   └── start.sh
│   │   │   │   │   ├── opencode/
│   │   │   │   │   │   ├── config.yaml
│   │   │   │   │   │   └── start.sh
│   │   │   │   │   ├── openhands/
│   │   │   │   │   │   ├── config.yaml
│   │   │   │   │   │   └── start.sh
│   │   │   │   │   ├── rdagent/
│   │   │   │   │   │   ├── config.yaml
│   │   │   │   │   │   └── start.sh
│   │   │   │   │   └── registry.py
│   │   │   │   ├── benchmarks/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── alfworld/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   ├── base_config.yaml
│   │   │   │   │   │   ├── data.py
│   │   │   │   │   │   ├── description.md
│   │   │   │   │   │   ├── eval.py
│   │   │   │   │   │   ├── react_prompts.json
│   │   │   │   │   │   └── requirements.txt
│   │   │   │   │   ├── alpacaeval/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   ├── annotators_gpt52_fn/
│   │   │   │   │   │   │   ├── alpaca_eval_fn.txt
│   │   │   │   │   │   │   ├── annotations_seed0_configs.json
│   │   │   │   │   │   │   └── configs.yaml
│   │   │   │   │   │   ├── data.py
│   │   │   │   │   │   ├── description.md
│   │   │   │   │   │   ├── eval.py
│   │   │   │   │   │   └── requirements.txt
│   │   │   │   │   ├── deepsearchqa/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   ├── data.py
│   │   │   │   │   │   ├── description.md
│   │   │   │   │   │   └── eval.py
│   │   │   │   │   ├── gsm8k/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   ├── data.py
│   │   │   │   │   │   └── description.md
│   │   │   │   │   ├── humaneval/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   ├── data.py
│   │   │   │   │   │   ├── description.md
│   │   │   │   │   │   └── requirements.txt
│   │   │   │   │   ├── smith/
│   │   │   │   │   │   ├── __init__.py
│   │   │   │   │   │   └── per_sample_eval.py
│   │   │   │   │   └── webshop/
│   │   │   │   │       ├── __init__.py
│   │   │   │   │       ├── data.py
│   │   │   │   │       ├── description.md
│   │   │   │   │       ├── eval.py
│   │   │   │   │       └── requirements.txt
│   │   │   │   ├── conf.py
│   │   │   │   ├── core/
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   ├── evaluator.py
│   │   │   │   │   ├── instructions.md
│   │   │   │   │   ├── metrics.py
│   │   │   │   │   ├── opencompass.py
│   │   │   │   │   ├── server.py
│   │   │   │   │   ├── skill.md
│   │   │   │   │   ├── ui.py
│   │   │   │   │   └── utils.py
│   │   │   │   ├── requirements.txt
│   │   │   │   ├── run.py
│   │   │   │   └── test/
│   │   │   │       ├── __init__.py
│   │   │   │       ├── test_benchmark.py
│   │   │   │       └── test_fixes.py
│   │   │   ├── dev/
│   │   │   │   ├── feedback.py
│   │   │   │   └── prompts.yaml
│   │   │   ├── env/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── conf.py
│   │   │   │   └── docker/
│   │   │   │       ├── base/
│   │   │   │       │   └── Dockerfile
│   │   │   │       ├── evalplus/
│   │   │   │       │   └── Dockerfile
│   │   │   │       ├── gsm8k/
│   │   │   │       │   └── Dockerfile
│   │   │   │       └── miniwob/
│   │   │   │           └── Dockerfile
│   │   │   ├── experiment/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── experiment.py
│   │   │   │   └── workspace.py
│   │   │   ├── loop.py
│   │   │   ├── proposal/
│   │   │   │   ├── prompts.yaml
│   │   │   │   ├── proposal.py
│   │   │   │   └── trace.py
│   │   │   ├── scen/
│   │   │   │   └── scenario.py
│   │   │   └── train/
│   │   │       └── runner.py
│   │   └── shared/
│   │       ├── get_runtime_info.py
│   │       └── runtime_info.py
│   └── utils/
│       ├── __init__.py
│       ├── agent/
│       │   ├── __init__.py
│       │   ├── apply_patch.py
│       │   ├── ret.py
│       │   ├── tpl.py
│       │   ├── tpl.yaml
│       │   └── workflow.py
│       ├── blob/
│       │   ├── azsync.sh
│       │   └── gen_token.sh
│       ├── env.py
│       ├── fmt.py
│       ├── prompts.yaml
│       ├── qlib.py
│       ├── repo/
│       │   ├── README.md
│       │   ├── diff.py
│       │   └── repo_utils.py
│       └── workflow/
│           ├── __init__.py
│           ├── loop.py
│           ├── misc.py
│           └── tracking.py
├── requirements/
│   ├── docs.txt
│   ├── lint.txt
│   ├── package.txt
│   ├── test.txt
│   └── torch.txt
├── requirements.txt
├── test/
│   ├── finetune/
│   │   ├── test_benchmark.py
│   │   ├── test_benchmark_api.py
│   │   └── test_benchmark_tablebench.py
│   ├── notebook/
│   │   ├── test_notebook_converter.py
│   │   ├── test_util.py
│   │   └── testfiles/
│   │       ├── main.ipynb
│   │       ├── main.py
│   │       ├── main2.ipynb
│   │       ├── main2.py
│   │       ├── main_missing_main_fn.py
│   │       └── main_missing_sections.py
│   ├── oai/
│   │   ├── test_advanced.py
│   │   ├── test_base.py
│   │   ├── test_completion.py
│   │   ├── test_embedding_and_similarity.py
│   │   ├── test_llm_connectivity.py
│   │   ├── test_prefect_cache.py
│   │   └── test_pydantic.py
│   ├── qlib/
│   │   └── test_model_factor_proposal.py
│   ├── rl/
│   │   └── __init__.py
│   └── utils/
│       ├── README.md
│       ├── coder/
│       │   ├── test_CoSTEER.py
│       │   └── test_finetune_coder.py
│       ├── test_agent_infra.py
│       ├── test_conf.py
│       ├── test_env.py
│       ├── test_import.py
│       ├── test_kaggle.py
│       ├── test_misc.py
│       └── test_ws.py
└── web/
    ├── .gitignore
    ├── README.md
    ├── auto-imports.d.ts
    ├── components.d.ts
    ├── index.html
    ├── package.json
    ├── src/
    │   ├── App.vue
    │   ├── common/
    │   │   ├── code-theme.css
    │   │   ├── py-theme.css
    │   │   └── reset.css
    │   ├── components/
    │   │   ├── chartBox.vue
    │   │   ├── code.vue
    │   │   ├── development.vue
    │   │   ├── dialog.vue
    │   │   ├── feedback.vue
    │   │   ├── footer.vue
    │   │   ├── kateX.vue
    │   │   ├── lineChart.vue
    │   │   ├── lineChartOne.vue
    │   │   ├── loading-dot.vue
    │   │   ├── loading.vue
    │   │   ├── loop-component.vue
    │   │   ├── markdown.vue
    │   │   ├── markdownToHtml.vue
    │   │   ├── navBar.vue
    │   │   ├── research.vue
    │   │   ├── saveImage.vue
    │   │   ├── select-component.vue
    │   │   ├── sm-select-component.vue
    │   │   ├── step-component.vue
    │   │   ├── svgIcon.vue
    │   │   ├── swiper.vue
    │   │   └── upload-progress.vue
    │   ├── constants/
    │   │   ├── mle-competitions.js
    │   │   └── qlib.js
    │   ├── main.ts
    │   ├── router/
    │   │   └── index.ts
    │   ├── shims-vue.d.ts
    │   ├── style.css
    │   ├── utils/
    │   │   ├── api.js
    │   │   ├── crypto.js
    │   │   ├── getAssets.ts
    │   │   ├── request.js
    │   │   └── snap.svg-min.js
    │   ├── views/
    │   │   ├── Home.vue
    │   │   ├── Login.vue
    │   │   ├── Playground.vue
    │   │   ├── Playground1.vue
    │   │   ├── PlaygroundPage.vue
    │   │   └── ResultPage.vue
    │   └── vite-env.d.ts
    ├── tsconfig.json
    ├── tsconfig.node.json
    └── vite.config.ts

SYMBOL INDEX (3037 symbols across 506 files)

FILE: rdagent/app/CI/run.py
  class CIError (line 43) | class CIError:
    method to_dict (line 53) | def to_dict(self) -> dict[str, object]:
    method __str__ (line 56) | def __str__(self) -> str:
  class CIFeedback (line 61) | class CIFeedback(Feedback):
    method statistics (line 64) | def statistics(self) -> dict[Literal["ruff", "mypy"], dict[str, int]]:
  class FixRecord (line 73) | class FixRecord:
    method to_dict (line 79) | def to_dict(self) -> dict[str, Any]:
  class CodeFile (line 90) | class CodeFile:
    method __init__ (line 91) | def __init__(self, path: Path | str) -> None:
    method add_line_number (line 96) | def add_line_number(cls: CodeFile, code: list[str] | str, start: int =...
    method remove_line_number (line 107) | def remove_line_number(cls: CodeFile, code: list[str] | str) -> list[s...
    method load (line 117) | def load(self) -> None:
    method get (line 126) | def get(
    method apply_changes (line 159) | def apply_changes(self, changes: list[tuple[int, int, str]]) -> None:
    method get_code_blocks (line 182) | def get_code_blocks(self, max_lines: int = 30) -> list[tuple[int, int]]:
    method __str__ (line 214) | def __str__(self) -> str:
  class Repo (line 218) | class Repo(EvolvableSubjects):
    method __init__ (line 219) | def __init__(self, project_path: Path | str, excludes: list[Path] | No...
  class RuffRule (line 255) | class RuffRule:
  class RuffEvaluator (line 282) | class RuffEvaluator(Evaluator):
    method __init__ (line 287) | def __init__(self, command: str | None = None) -> None:
    method explain_rule (line 294) | def explain_rule(error_code: str) -> RuffRule:
    method evaluate (line 307) | def evaluate(self, evo: Repo, **kwargs: dict) -> CIFeedback:
  class MypyEvaluator (line 358) | class MypyEvaluator(Evaluator):
    method __init__ (line 359) | def __init__(self, command: str | None = None) -> None:
    method evaluate (line 365) | def evaluate(self, evo: Repo, **kwargs: dict) -> CIFeedback:
  class MultiEvaluator (line 413) | class MultiEvaluator(Evaluator):
    method __init__ (line 414) | def __init__(self, *evaluators: Evaluator) -> None:
    method evaluate (line 417) | def evaluate(self, evo: Repo, **kwargs: dict) -> CIFeedback:
  class CIEvoStr (line 431) | class CIEvoStr(EvolvingStrategy):
    method evolve (line 432) | def evolve(  # noqa: C901, PLR0912, PLR0915
  class CIEvoAgent (line 696) | class CIEvoAgent(EvoAgent):
    method __init__ (line 697) | def __init__(self, evolving_strategy: CIEvoStr) -> None:
    method multistep_evolve (line 701) | def multistep_evolve(self, evo: Repo, eva: Evaluator) -> Repo:

FILE: rdagent/app/benchmark/factor/analysis.py
  class BenchmarkAnalyzer (line 15) | class BenchmarkAnalyzer:
    method __init__ (line 16) | def __init__(self, settings, only_correct_format=False):
    method load_index_map (line 21) | def load_index_map(self):
    method load_data (line 29) | def load_data(self, file_path):
    method process_results (line 39) | def process_results(self, results):
    method reformat_index (line 48) | def reformat_index(self, display_df):
    method result_all_key_order (line 81) | def result_all_key_order(self, x):
    method analyze_data (line 96) | def analyze_data(self, sum_df):
  class Plotter (line 178) | class Plotter:
    method change_fs (line 180) | def change_fs(font_size):
    method plot_data (line 190) | def plot_data(data, file_name, title):
  function main (line 204) | def main(

FILE: rdagent/app/cli.py
  function ui (line 45) | def ui(port=19899, log_dir="", debug: bool = False, data_science: bool =...
  function server_ui (line 65) | def server_ui(port=19899):
  function ds_user_interact (line 74) | def ds_user_interact(port=19900):
  function fin_factor_cli (line 83) | def fin_factor_cli(
  function fin_model_cli (line 94) | def fin_model_cli(
  function fin_quant_cli (line 105) | def fin_quant_cli(
  function fin_factor_report_cli (line 116) | def fin_factor_report_cli(
  function general_model_cli (line 126) | def general_model_cli(report_file_path: str):
  function data_science_cli (line 131) | def data_science_cli(
  function llm_finetune_cli (line 150) | def llm_finetune_cli(
  function grade_summary_cli (line 177) | def grade_summary_cli(log_folder: str):
  function health_check_cli (line 186) | def health_check_cli(
  function collect_info_cli (line 195) | def collect_info_cli():

FILE: rdagent/app/data_science/conf.py
  class DataScienceBasePropSetting (line 9) | class DataScienceBasePropSetting(KaggleBasePropSetting):

FILE: rdagent/app/data_science/loop.py
  function main (line 13) | def main(

FILE: rdagent/app/finetune/data_science/conf.py
  class DSFinetuneScen (line 9) | class DSFinetuneScen(ExtendedBaseSettings):
  function update_settings (line 30) | def update_settings(competition: str):

FILE: rdagent/app/finetune/data_science/loop.py
  function main (line 13) | def main(

FILE: rdagent/app/finetune/data_science/scen.py
  class DSFinetuneScen (line 11) | class DSFinetuneScen(DataScienceScen):
    method _get_data_folder_description (line 14) | def _get_data_folder_description(self) -> str:

FILE: rdagent/app/finetune/llm/conf.py
  class LLMFinetunePropSetting (line 8) | class LLMFinetunePropSetting(ExtendedBaseSettings):

FILE: rdagent/app/finetune/llm/loop.py
  function main (line 17) | def main(

FILE: rdagent/app/finetune/llm/ui/app.py
  function get_job_options (line 28) | def get_job_options(base_path: Path) -> list[str]:
  function main (line 66) | def main():

FILE: rdagent/app/finetune/llm/ui/benchmarks/__init__.py
  function get_core_metric_score (line 22) | def get_core_metric_score(benchmark_name: str, accuracy_summary: dict) -...

FILE: rdagent/app/finetune/llm/ui/benchmarks/base.py
  class BenchmarkProcessor (line 6) | class BenchmarkProcessor(ABC):
    method match (line 28) | def match(cls, benchmark_name: str) -> bool:
    method get_core_metric (line 34) | def get_core_metric(cls, accuracy_summary: dict) -> tuple[str, float, ...
    method is_higher_better (line 49) | def is_higher_better(cls, metric_name: str) -> bool:

FILE: rdagent/app/finetune/llm/ui/benchmarks/bioprobench.py
  class BioProBenchProcessor (line 6) | class BioProBenchProcessor(BenchmarkProcessor):
    method match (line 17) | def match(cls, benchmark_name: str) -> bool:
    method get_core_metric (line 21) | def get_core_metric(cls, accuracy_summary: dict) -> tuple[str, float, ...

FILE: rdagent/app/finetune/llm/ui/benchmarks/chemcotbench.py
  class ChemCotBenchProcessor (line 6) | class ChemCotBenchProcessor(BenchmarkProcessor):
    method match (line 43) | def match(cls, benchmark_name: str) -> bool:
    method get_core_metric (line 47) | def get_core_metric(cls, accuracy_summary: dict) -> tuple[str, float, ...

FILE: rdagent/app/finetune/llm/ui/benchmarks/financeiq.py
  class FinanceIQProcessor (line 6) | class FinanceIQProcessor(BenchmarkProcessor):
    method match (line 10) | def match(cls, benchmark_name: str) -> bool:
    method get_core_metric (line 14) | def get_core_metric(cls, accuracy_summary: dict) -> tuple[str, float, ...

FILE: rdagent/app/finetune/llm/ui/benchmarks/panorama.py
  class PanoramaProcessor (line 6) | class PanoramaProcessor(BenchmarkProcessor):
    method match (line 16) | def match(cls, benchmark_name: str) -> bool:
    method get_core_metric (line 20) | def get_core_metric(cls, accuracy_summary: dict) -> tuple[str, float, ...

FILE: rdagent/app/finetune/llm/ui/benchmarks/tablebench.py
  class TableBenchProcessor (line 6) | class TableBenchProcessor(BenchmarkProcessor):
    method match (line 24) | def match(cls, benchmark_name: str) -> bool:
    method get_core_metric (line 28) | def get_core_metric(cls, accuracy_summary: dict) -> tuple[str, float, ...

FILE: rdagent/app/finetune/llm/ui/components.py
  function convert_latex_for_streamlit (line 17) | def convert_latex_for_streamlit(text: str) -> str:
  function format_duration (line 32) | def format_duration(seconds: float | None) -> str:
  function render_session (line 42) | def render_session(session: Session, show_types: list[str]) -> None:
  function render_loop (line 58) | def render_loop(loop: Loop, show_types: list[str]) -> None:
  function _render_loop_content (line 124) | def _render_loop_content(loop: Loop, show_types: list[str]) -> None:
  function render_evo_loop (line 158) | def render_evo_loop(evo: EvoLoop, show_types: list[str]) -> None:
  function render_event (line 170) | def render_event(event: Event) -> None:
  function render_scenario (line 208) | def render_scenario(content: Any) -> None:
  function render_dataset_selection (line 279) | def render_dataset_selection(content: Any) -> None:
  function render_hypothesis (line 296) | def render_hypothesis(content: Any) -> None:
  function render_settings (line 306) | def render_settings(content: Any) -> None:
  function render_llm_call (line 313) | def render_llm_call(content: Any) -> None:
  function render_template (line 354) | def render_template(content: Any) -> None:
  function render_experiment (line 378) | def render_experiment(content: Any) -> None:
  function render_code (line 392) | def render_code(content: Any) -> None:
  function _extract_evaluator_name (line 410) | def _extract_evaluator_name(title: str) -> str:
  function _render_single_feedback (line 416) | def _render_single_feedback(fb: Any, evaluator_name: str = "") -> None:
  function render_docker_exec (line 461) | def render_docker_exec(content: Any, event_title: str = "") -> None:
  function render_feedback (line 530) | def render_feedback(content: Any) -> None:
  function render_token (line 572) | def render_token(content: Any) -> None:
  function render_time_info (line 585) | def render_time_info(content: Any) -> None:
  function render_generic (line 593) | def render_generic(content: Any) -> None:
  function render_training_result (line 602) | def render_training_result(result: dict) -> None:
  function render_benchmark_result (line 665) | def render_benchmark_result(content: dict) -> None:
  function render_summary (line 709) | def render_summary(summary: dict) -> None:

FILE: rdagent/app/finetune/llm/ui/data_loader.py
  class Event (line 19) | class Event:
    method time_str (line 34) | def time_str(self) -> str:
  class EvoLoop (line 39) | class EvoLoop:
  class Loop (line 48) | class Loop:
  class Session (line 59) | class Session:
  function extract_loop_id (line 66) | def extract_loop_id(tag: str) -> int | None:
  function extract_evo_id (line 71) | def extract_evo_id(tag: str) -> int | None:
  function extract_stage (line 76) | def extract_stage(tag: str) -> str:
  function get_valid_sessions (line 88) | def get_valid_sessions(log_folder: Path) -> list[str]:
  function parse_event (line 98) | def parse_event(tag: str, content: Any, timestamp: datetime) -> Event | ...
  function load_ft_session (line 365) | def load_ft_session(log_path: Path) -> Session:
  function get_summary (line 423) | def get_summary(session: Session) -> dict:

FILE: rdagent/app/finetune/llm/ui/ft_summary.py
  function is_valid_task (line 16) | def is_valid_task(task_path: Path) -> bool:
  function get_loop_dirs (line 21) | def get_loop_dirs(task_path: Path) -> list[Path]:
  function extract_benchmark_score (line 27) | def extract_benchmark_score(loop_path: Path, split: str = "") -> tuple[s...
  function extract_benchmark_scores (line 60) | def extract_benchmark_scores(loop_path: Path) -> dict[str, tuple[str, fl...
  function extract_baseline_score (line 73) | def extract_baseline_score(task_path: Path) -> tuple[str, float] | None:
  function extract_baseline_scores (line 101) | def extract_baseline_scores(task_path: Path) -> dict[str, tuple[str, flo...
  function get_loop_status (line 144) | def get_loop_status(
  function get_max_loops (line 214) | def get_max_loops(job_path: Path) -> int:
  function get_job_summary_df (line 224) | def get_job_summary_df(job_path: Path) -> tuple[pd.DataFrame, pd.DataFra...
  function style_status_cell (line 318) | def style_status_cell(val: str, decision: bool | None = None) -> str:
  function style_df_with_decisions (line 364) | def style_df_with_decisions(df: pd.DataFrame, decisions_df: pd.DataFrame...
  function render_job_summary (line 386) | def render_job_summary(job_path: Path, is_root: bool = False) -> None:
  function extract_full_benchmark (line 429) | def extract_full_benchmark(loop_path: Path, split: str = "") -> dict | N...
  function extract_baseline_full_benchmark (line 457) | def extract_baseline_full_benchmark(task_path: Path, split: str = "valid...
  function get_task_full_benchmark_df (line 490) | def get_task_full_benchmark_df(task_path: Path, split: str) -> pd.DataFr...
  function render_task_detail_selector (line 554) | def render_task_detail_selector(job_path: Path) -> None:

FILE: rdagent/app/finetune/share/eval.py
  class PrevModelLoadEvaluator (line 13) | class PrevModelLoadEvaluator(CoSTEEREvaluator):
    method __init__ (line 16) | def __init__(self, scen: Scenario):
    method evaluate (line 19) | def evaluate(

FILE: rdagent/app/general_model/general_model.py
  function extract_models_and_implement (line 14) | def extract_models_and_implement(report_file_path: str) -> None:

FILE: rdagent/app/kaggle/conf.py
  class KaggleBasePropSetting (line 6) | class KaggleBasePropSetting(ExtendedBaseSettings):

FILE: rdagent/app/kaggle/loop.py
  class KaggleRDLoop (line 29) | class KaggleRDLoop(RDLoop):
    method __init__ (line 30) | def __init__(self, PROP_SETTING: BasePropSetting):
    method coding (line 58) | def coding(self, prev_out: dict[str, Any]):
    method running (line 71) | def running(self, prev_out: dict[str, Any]):
  function main (line 114) | def main(path=None, step_n=None, competition=None):

FILE: rdagent/app/qlib_rd_loop/conf.py
  class ModelBasePropSetting (line 8) | class ModelBasePropSetting(BasePropSetting):
  class FactorBasePropSetting (line 52) | class FactorBasePropSetting(BasePropSetting):
  class FactorFromReportPropSetting (line 96) | class FactorFromReportPropSetting(FactorBasePropSetting):
  class QuantBasePropSetting (line 112) | class QuantBasePropSetting(BasePropSetting):

FILE: rdagent/app/qlib_rd_loop/factor.py
  class FactorRDLoop (line 17) | class FactorRDLoop(RDLoop):
    method running (line 21) | def running(self, prev_out: dict[str, Any]):
  function main (line 30) | def main(

FILE: rdagent/app/qlib_rd_loop/factor_from_report.py
  function generate_hypothesis (line 26) | def generate_hypothesis(factor_result: dict, report_content: str) -> str:
  function extract_hypothesis_and_exp_from_reports (line 61) | def extract_hypothesis_and_exp_from_reports(report_file_path: str) -> Ql...
  class FactorReportLoop (line 97) | class FactorReportLoop(FactorRDLoop, metaclass=LoopMeta):
    method __init__ (line 98) | def __init__(self, report_folder: str = None):
    method direct_exp_gen (line 112) | async def direct_exp_gen(self, prev_out: dict[str, Any]):
    method coding (line 137) | def coding(self, prev_out: dict[str, Any]):
  function main (line 143) | def main(report_folder=None, path=None, all_duration=None, checkout=True):

FILE: rdagent/app/qlib_rd_loop/model.py
  class ModelRDLoop (line 14) | class ModelRDLoop(RDLoop):
  function main (line 18) | def main(

FILE: rdagent/app/qlib_rd_loop/quant.py
  class QuantRDLoop (line 30) | class QuantRDLoop(RDLoop):
    method __init__ (line 36) | def __init__(self, PROP_SETTING: BasePropSetting):
    method direct_exp_gen (line 74) | async def direct_exp_gen(self, prev_out: dict[str, Any]):
    method coding (line 92) | def coding(self, prev_out: dict[str, Any]):
    method running (line 100) | def running(self, prev_out: dict[str, Any]):
    method feedback (line 111) | def feedback(self, prev_out: dict[str, Any]):
  function main (line 131) | def main(

FILE: rdagent/app/rl/conf.py
  class RLPostTrainingPropSetting (line 8) | class RLPostTrainingPropSetting(ExtendedBaseSettings):

FILE: rdagent/app/rl/loop.py
  function main (line 16) | def main(

FILE: rdagent/app/rl/ui/app.py
  function _safe_resolve (line 23) | def _safe_resolve(user_input: str | None, safe_root: Path) -> Path:
  function get_job_options (line 46) | def get_job_options(base_path: Path) -> list[str]:
  function main (line 75) | def main():

FILE: rdagent/app/rl/ui/components.py
  function format_duration (line 14) | def format_duration(seconds: float | None) -> str:
  function render_session (line 24) | def render_session(session: Session, show_types: list[str]) -> None:
  function render_loop (line 38) | def render_loop(loop: Loop, show_types: list[str]) -> None:
  function render_event (line 95) | def render_event(event: Event) -> None:
  function render_scenario (line 127) | def render_scenario(content: Any) -> None:
  function render_hypothesis (line 135) | def render_hypothesis(content: Any) -> None:
  function render_settings (line 144) | def render_settings(content: Any) -> None:
  function render_llm_call (line 151) | def render_llm_call(content: Any) -> None:
  function render_template (line 181) | def render_template(content: Any) -> None:
  function render_experiment (line 200) | def render_experiment(content: Any) -> None:
  function render_code (line 211) | def render_code(content: Any) -> None:
  function render_docker_exec (line 228) | def render_docker_exec(content: Any) -> None:
  function render_feedback (line 245) | def render_feedback(content: Any) -> None:
  function render_token (line 273) | def render_token(content: Any) -> None:
  function render_time_info (line 286) | def render_time_info(content: Any) -> None:
  function render_generic (line 294) | def render_generic(content: Any) -> None:
  function render_summary (line 303) | def render_summary(summary: dict) -> None:

FILE: rdagent/app/rl/ui/data_loader.py
  class Event (line 21) | class Event:
    method time_str (line 35) | def time_str(self) -> str:
  class Loop (line 40) | class Loop:
  class Session (line 51) | class Session:
  function extract_loop_id (line 58) | def extract_loop_id(tag: str) -> int | None:
  function extract_stage (line 63) | def extract_stage(tag: str) -> str:
  function get_valid_sessions (line 75) | def get_valid_sessions(log_folder: Path) -> list[str]:
  function parse_event (line 85) | def parse_event(tag: str, content: Any, timestamp: datetime) -> Event | ...
  function load_session (line 237) | def load_session(log_path: Path) -> Session:
  function get_summary (line 285) | def get_summary(session: Session) -> dict:

FILE: rdagent/app/rl/ui/rl_summary.py
  function is_valid_task (line 13) | def is_valid_task(task_path: Path) -> bool:
  function get_loop_dirs (line 18) | def get_loop_dirs(task_path: Path) -> list[Path]:
  function get_loop_status (line 24) | def get_loop_status(task_path: Path, loop_id: int) -> tuple[str, bool | ...
  function get_max_loops (line 64) | def get_max_loops(job_path: Path) -> int:
  function get_job_summary_df (line 74) | def get_job_summary_df(job_path: Path) -> tuple[pd.DataFrame, pd.DataFra...
  function style_status_cell (line 119) | def style_status_cell(val: str, decision: bool | None = None) -> str:
  function style_df_with_decisions (line 136) | def style_df_with_decisions(df: pd.DataFrame, decisions_df: pd.DataFrame):
  function render_job_summary (line 152) | def render_job_summary(job_path: Path, is_root: bool = False) -> None:

FILE: rdagent/app/utils/ape.py
  function get_llm_qa (line 11) | def get_llm_qa(file_path):

FILE: rdagent/app/utils/health_check.py
  function check_docker_status (line 14) | def check_docker_status() -> None:
  function is_port_in_use (line 32) | def is_port_in_use(port):
  function check_and_list_free_ports (line 37) | def check_and_list_free_ports(start_port=19899, max_ports=10) -> None:
  function test_chat (line 51) | def test_chat(chat_model, chat_api_key, chat_api_base):
  function test_embedding (line 78) | def test_embedding(embedding_model, embedding_api_key, embedding_api_base):
  function env_check (line 94) | def env_check():
  function health_check (line 135) | def health_check(

FILE: rdagent/app/utils/info.py
  function sys_info (line 14) | def sys_info():
  function python_info (line 27) | def python_info():
  function docker_info (line 34) | def docker_info():
  function rdagent_info (line 52) | def rdagent_info():
  function collect_info (line 80) | def collect_info():

FILE: rdagent/app/utils/ws.py
  function run (line 13) | def run(competition: str, cmd: str, local_path: str = "./", mount_path: ...

FILE: rdagent/app/utils/ws_ft.py
  function run (line 13) | def run(

FILE: rdagent/components/agent/base.py
  class BaseAgent (line 12) | class BaseAgent:
    method __init__ (line 15) | def __init__(self, system_prompt: str, toolsets: list[str]): ...
    method query (line 18) | def query(self, query: str) -> str: ...
  class PAIAgent (line 21) | class PAIAgent(BaseAgent):
    method __init__ (line 29) | def __init__(
    method _run_query (line 56) | def _run_query(self, query: str) -> str:
    method query (line 64) | def query(self, query: str) -> str:

FILE: rdagent/components/agent/context7/__init__.py
  class Agent (line 11) | class Agent(PAIAgent):
    method __init__ (line 16) | def __init__(self):
    method _build_enhanced_query (line 25) | def _build_enhanced_query(self, error_message: str, full_code: Optiona...
    method query (line 46) | def query(self, query: str) -> str:

FILE: rdagent/components/agent/context7/conf.py
  class Settings (line 17) | class Settings(BaseSettings):

FILE: rdagent/components/agent/rag/__init__.py
  class Agent (line 8) | class Agent(PAIAgent):
    method __init__ (line 13) | def __init__(self, system_prompt: str | None = None):

FILE: rdagent/components/agent/rag/conf.py
  class Settings (line 10) | class Settings(BaseSettings):

FILE: rdagent/components/benchmark/conf.py
  class BenchmarkSettings (line 10) | class BenchmarkSettings(ExtendedBaseSettings):
    class Config (line 11) | class Config:

FILE: rdagent/components/benchmark/eval_method.py
  class TestCase (line 31) | class TestCase:
    method __init__ (line 32) | def __init__(
  class TestCases (line 41) | class TestCases:
    method __init__ (line 42) | def __init__(self, test_case_l: list[TestCase] = []):
    method __getitem__ (line 46) | def __getitem__(self, item):
    method __len__ (line 49) | def __len__(self):
    method get_exp (line 52) | def get_exp(self):
    method target_task (line 56) | def target_task(self):
    method ground_truth (line 60) | def ground_truth(self):
  class BaseEval (line 64) | class BaseEval:
    method __init__ (line 69) | def __init__(
    method load_cases_to_eval (line 90) | def load_cases_to_eval(
    method eval_case (line 105) | def eval_case(
  class FactorImplementEval (line 140) | class FactorImplementEval(BaseEval):
    method __init__ (line 141) | def __init__(
    method develop (line 160) | def develop(self):
    method eval (line 181) | def eval(self, gen_factor_l_all_rounds):
    method summarize_res (line 200) | def summarize_res(res: EVAL_RES) -> pd.DataFrame:

FILE: rdagent/components/benchmark/utils.py
  function _guess_dataset_var (line 13) | def _guess_dataset_var(mod_path: str) -> str:
  function build_dataset_imports_explicit (line 30) | def build_dataset_imports_explicit(dataset_imports: str | Iterable[str])...

FILE: rdagent/components/coder/CoSTEER/__init__.py
  class CoSTEER (line 20) | class CoSTEER(Developer[Experiment]):
    method __init__ (line 21) | def __init__(
    method get_develop_max_seconds (line 69) | def get_develop_max_seconds(self) -> int | None:
    method _get_last_fb (line 76) | def _get_last_fb(self) -> CoSTEERMultiFeedback:
    method should_use_new_evo (line 82) | def should_use_new_evo(self, base_fb: CoSTEERMultiFeedback | None, new...
    method develop (line 93) | def develop(self, exp: Experiment) -> Experiment:
    method _exp_postprocess_by_feedback (line 157) | def _exp_postprocess_by_feedback(self, evo: Experiment, feedback: CoST...

FILE: rdagent/components/coder/CoSTEER/config.py
  class CoSTEERSettings (line 6) | class CoSTEERSettings(ExtendedBaseSettings):
    class Config (line 9) | class Config:

FILE: rdagent/components/coder/CoSTEER/evaluators.py
  class CoSTEERSingleFeedback (line 32) | class CoSTEERSingleFeedback(Feedback):
    method val_and_update_init_dict (line 54) | def val_and_update_init_dict(data: dict) -> dict:
    method merge (line 86) | def merge(cls, feedback_li: list["CoSTEERSingleFeedback"]) -> "CoSTEER...
    method __str__ (line 110) | def __str__(self) -> str:
    method __bool__ (line 121) | def __bool__(self):
  class CoSTEERSingleFeedbackDeprecated (line 125) | class CoSTEERSingleFeedbackDeprecated(CoSTEERSingleFeedback):
    method __init__ (line 128) | def __init__(
    method execution (line 155) | def execution(self):
    method execution (line 159) | def execution(self, value):
    method return_checking (line 163) | def return_checking(self):
    method return_checking (line 169) | def return_checking(self, value):
    method code (line 176) | def code(self):
    method code (line 180) | def code(self, value):
    method __str__ (line 183) | def __str__(self) -> str:
  class CoSTEERMultiFeedback (line 199) | class CoSTEERMultiFeedback(Feedback):
    method __init__ (line 202) | def __init__(self, feedback_list: List[CoSTEERSingleFeedback]) -> None:
    method __getitem__ (line 205) | def __getitem__(self, index: int) -> CoSTEERSingleFeedback:
    method __len__ (line 208) | def __len__(self) -> int:
    method append (line 211) | def append(self, feedback: CoSTEERSingleFeedback) -> None:
    method __iter__ (line 214) | def __iter__(self):
    method is_acceptable (line 217) | def is_acceptable(self) -> bool:
    method finished (line 220) | def finished(self) -> bool:
    method __bool__ (line 227) | def __bool__(self) -> bool:
  class CoSTEEREvaluator (line 231) | class CoSTEEREvaluator(Evaluator):
    method __init__ (line 232) | def __init__(
    method evaluate (line 243) | def evaluate(
  class CoSTEERMultiEvaluator (line 253) | class CoSTEERMultiEvaluator(RAGEvaluator):
    method __init__ (line 256) | def __init__(self, single_evaluator: CoSTEEREvaluator | list[CoSTEEREv...
    method evaluate_iter (line 261) | def evaluate_iter(

FILE: rdagent/components/coder/CoSTEER/evolvable_subjects.py
  class EvolvingItem (line 6) | class EvolvingItem(Experiment, EvolvableSubjects):
    method __init__ (line 11) | def __init__(
    method from_experiment (line 28) | def from_experiment(cls, exp: Experiment) -> "EvolvingItem":

FILE: rdagent/components/coder/CoSTEER/evolving_strategy.py
  class MultiProcessEvolvingStrategy (line 22) | class MultiProcessEvolvingStrategy(EvolvingStrategy):
    method __init__ (line 25) | def __init__(self, scen: Scenario, settings: CoSTEERSettings, improve_...
    method implement_one_task (line 30) | def implement_one_task(
    method implement_func_list (line 61) | def implement_func_list(self) -> list[Callable]:
    method assign_code_list_to_evo (line 71) | def assign_code_list_to_evo(self, code_list: list[dict], evo: Evolving...
    method assign_code_list_to_evo (line 86) | def assign_code_list_to_evo(self, code_list: list[dict | None], evo) -...
    method evolve_iter (line 108) | def evolve_iter(

FILE: rdagent/components/coder/CoSTEER/knowledge_management.py
  class CoSTEERKnowledge (line 36) | class CoSTEERKnowledge(Knowledge):
    method __init__ (line 37) | def __init__(
    method get_implementation_and_feedback_str (line 47) | def get_implementation_and_feedback_str(self) -> str:
  class CoSTEERRAGStrategy (line 55) | class CoSTEERRAGStrategy(RAGStrategy):
    method __init__ (line 56) | def __init__(self, *args, dump_knowledge_base_path: Path = None, **kwa...
    method load_or_init_knowledge_base (line 60) | def load_or_init_knowledge_base(
    method dump_knowledge_base (line 82) | def dump_knowledge_base(self):
    method load_dumped_knowledge_base (line 91) | def load_dumped_knowledge_base(self, *args, **kwargs):
  class CoSTEERQueriedKnowledge (line 102) | class CoSTEERQueriedKnowledge(QueriedKnowledge):
    method __init__ (line 135) | def __init__(self, success_task_to_knowledge_dict: dict = {}, failed_t...
  class CoSTEERKnowledgeBaseV1 (line 140) | class CoSTEERKnowledgeBaseV1(EvolvingKnowledgeBase):
    method __init__ (line 141) | def __init__(self, path: str | Path = None) -> None:
    method query (line 148) | def query(self) -> CoSTEERQueriedKnowledge | None:
  class CoSTEERQueriedKnowledgeV1 (line 155) | class CoSTEERQueriedKnowledgeV1(CoSTEERQueriedKnowledge):
    method __init__ (line 156) | def __init__(
  class CoSTEERRAGStrategyV1 (line 168) | class CoSTEERRAGStrategyV1(CoSTEERRAGStrategy):
    method __init__ (line 171) | def __init__(self, settings: CoSTEERSettings, *args, **kwargs) -> None:
    method generate_knowledge (line 176) | def generate_knowledge(
    method query (line 219) | def query(
  class CoSTEERQueriedKnowledgeV2 (line 281) | class CoSTEERQueriedKnowledgeV2(CoSTEERQueriedKnowledgeV1):
    method __init__ (line 339) | def __init__(
  class CoSTEERRAGStrategyV2 (line 354) | class CoSTEERRAGStrategyV2(CoSTEERRAGStrategy):
    method __init__ (line 355) | def __init__(self, settings: CoSTEERSettings, *args, **kwargs) -> None:
    method generate_knowledge (line 360) | def generate_knowledge(
    method query (line 431) | def query(self, evo: EvolvableSubjects, evolving_trace: list[EvoStep])...
    method analyze_component (line 457) | def analyze_component(
    method analyze_error (line 488) | def analyze_error(
    method former_trace_query (line 530) | def former_trace_query(
    method component_query (line 593) | def component_query(
    method error_query (line 723) | def error_query(
  class CoSTEERKnowledgeBaseV2 (line 852) | class CoSTEERKnowledgeBaseV2(EvolvingKnowledgeBase):
    method __init__ (line 853) | def __init__(self, init_component_list=None, path: str | Path = None) ...
    method get_all_nodes_by_label (line 881) | def get_all_nodes_by_label(self, label: str) -> list[UndirectedNode]:
    method update_success_task (line 884) | def update_success_task(
    method query (line 926) | def query(self):
    method graph_get_node_by_content (line 929) | def graph_get_node_by_content(self, content: str) -> UndirectedNode:
    method graph_query_by_content (line 932) | def graph_query_by_content(
    method graph_query_by_node (line 974) | def graph_query_by_node(
    method graph_query_by_intersection (line 1009) | def graph_query_by_intersection(

FILE: rdagent/components/coder/CoSTEER/task.py
  class CoSTEERTask (line 4) | class CoSTEERTask(Task):
    method __init__ (line 5) | def __init__(self, base_code: str = None, *args, **kwargs) -> None:

FILE: rdagent/components/coder/data_science/conf.py
  class DSCoderCoSTEERSettings (line 16) | class DSCoderCoSTEERSettings(CoSTEERSettings):
    class Config (line 19) | class Config:
  function get_ds_env (line 41) | def get_ds_env(
  function get_clear_ws_cmd (line 78) | def get_clear_ws_cmd(stage: Literal["before_training", "before_inference...

FILE: rdagent/components/coder/data_science/ensemble/__init__.py
  class EnsembleMultiProcessEvolvingStrategy (line 43) | class EnsembleMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 44) | def implement_one_task(
    method assign_code_list_to_evo (line 127) | def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
  class EnsembleCoSTEER (line 144) | class EnsembleCoSTEER(DSCoSTEER):
    method __init__ (line 145) | def __init__(

FILE: rdagent/components/coder/data_science/ensemble/eval.py
  class EnsembleCoSTEEREvaluator (line 24) | class EnsembleCoSTEEREvaluator(CoSTEEREvaluator):
    method evaluate (line 25) | def evaluate(

FILE: rdagent/components/coder/data_science/ensemble/exp.py
  class EnsembleTask (line 12) | class EnsembleTask(CoSTEERTask):

FILE: rdagent/components/coder/data_science/ensemble/test.py
  function load_ensemble_spec (line 26) | def load_ensemble_spec():
  function develop_one_competition (line 32) | def develop_one_competition(competition: str):

FILE: rdagent/components/coder/data_science/feature/__init__.py
  class FeatureMultiProcessEvolvingStrategy (line 28) | class FeatureMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 29) | def implement_one_task(
    method assign_code_list_to_evo (line 101) | def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
  class FeatureCoSTEER (line 118) | class FeatureCoSTEER(DSCoSTEER):
    method __init__ (line 119) | def __init__(

FILE: rdagent/components/coder/data_science/feature/eval.py
  class FeatureCoSTEEREvaluator (line 19) | class FeatureCoSTEEREvaluator(CoSTEEREvaluator):
    method evaluate (line 20) | def evaluate(

FILE: rdagent/components/coder/data_science/feature/exp.py
  class FeatureTask (line 12) | class FeatureTask(CoSTEERTask):

FILE: rdagent/components/coder/data_science/feature/test.py
  function develop_one_competition (line 15) | def develop_one_competition(competition: str):  # -> experiment

FILE: rdagent/components/coder/data_science/model/__init__.py
  class ModelMultiProcessEvolvingStrategy (line 30) | class ModelMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 31) | def implement_one_task(
    method assign_code_list_to_evo (line 133) | def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
  class ModelCoSTEER (line 150) | class ModelCoSTEER(DSCoSTEER):
    method __init__ (line 151) | def __init__(

FILE: rdagent/components/coder/data_science/model/eval.py
  class ModelGeneralCaseSpecEvaluator (line 29) | class ModelGeneralCaseSpecEvaluator(CoSTEEREvaluator):
    method evaluate (line 38) | def evaluate(

FILE: rdagent/components/coder/data_science/model/exp.py
  class ModelTask (line 7) | class ModelTask(CoSTEERTask):
    method __init__ (line 8) | def __init__(
    method get_task_information (line 17) | def get_task_information(self):

FILE: rdagent/components/coder/data_science/model/test.py
  function develop_one_competition (line 19) | def develop_one_competition(competition: str):

FILE: rdagent/components/coder/data_science/pipeline/__init__.py
  class PipelineMultiProcessEvolvingStrategy (line 54) | class PipelineMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 55) | def implement_one_task(
    method assign_code_list_to_evo (line 116) | def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
  class PipelineCoSTEER (line 133) | class PipelineCoSTEER(DSCoSTEER):
    method __init__ (line 134) | def __init__(

FILE: rdagent/components/coder/data_science/pipeline/eval.py
  class DSCoderFeedback (line 33) | class DSCoderFeedback(CoSTEERSingleFeedback):
    method val_and_update_init_dict (line 43) | def val_and_update_init_dict(data: dict) -> dict:
    method __str__ (line 71) | def __str__(self) -> str:
    method merge (line 87) | def merge(cls, feedback_li: list[CoSTEERSingleFeedback]) -> "DSCoderFe...
  class PipelineCoSTEEREvaluator (line 123) | class PipelineCoSTEEREvaluator(CoSTEEREvaluator):
    method evaluate (line 125) | def evaluate(

FILE: rdagent/components/coder/data_science/pipeline/exp.py
  class PipelineTask (line 5) | class PipelineTask(CoSTEERTask):
    method __init__ (line 6) | def __init__(self, name: str = "Pipeline", package_info: str | None = ...

FILE: rdagent/components/coder/data_science/raw_data_loader/__init__.py
  class DataLoaderMultiProcessEvolvingStrategy (line 58) | class DataLoaderMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 59) | def implement_one_task(
    method assign_code_list_to_evo (line 181) | def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
  class DataLoaderCoSTEER (line 198) | class DataLoaderCoSTEER(DSCoSTEER):
    method __init__ (line 199) | def __init__(
    method develop (line 222) | def develop(self, exp):

FILE: rdagent/components/coder/data_science/raw_data_loader/eval.py
  class DataLoaderCoSTEEREvaluator (line 26) | class DataLoaderCoSTEEREvaluator(CoSTEEREvaluator):
    method evaluate (line 27) | def evaluate(

FILE: rdagent/components/coder/data_science/raw_data_loader/exp.py
  class DataLoaderTask (line 5) | class DataLoaderTask(CoSTEERTask):

FILE: rdagent/components/coder/data_science/raw_data_loader/test.py
  function develop_one_competition (line 15) | def develop_one_competition(competition: str):  # -> experiment

FILE: rdagent/components/coder/data_science/share/doc.py
  class DocDev (line 12) | class DocDev(Developer[Experiment]):
    method develop (line 17) | def develop(self, exp: Experiment) -> None:

FILE: rdagent/components/coder/data_science/share/ds_costeer.py
  class DSCoSTEER (line 4) | class DSCoSTEER(CoSTEER):
    method get_develop_max_seconds (line 5) | def get_develop_max_seconds(self) -> int | None:

FILE: rdagent/components/coder/data_science/share/eval.py
  class ModelDumpEvaluator (line 29) | class ModelDumpEvaluator(CoSTEEREvaluator):
    method __init__ (line 32) | def __init__(self, scen: Scenario, data_type: Literal["sample", "full"]):
    method evaluate (line 36) | def evaluate(

FILE: rdagent/components/coder/data_science/share/notebook.py
  class NotebookConverter (line 22) | class NotebookConverter:
    method validate_code_format (line 27) | def validate_code_format(self, code: str) -> str | None:
    method convert (line 41) | def convert(

FILE: rdagent/components/coder/data_science/share/util.py
  class CodeSection (line 9) | class CodeSection(TypedDict):
  function extract_function_body (line 20) | def extract_function_body(source_code: str, function_name: str) -> Optio...
  function split_sections (line 39) | def split_sections(
  function split_code_sections (line 79) | def split_code_sections(source_code: str) -> tuple[Optional[str], list[s...
  function split_output_sections (line 86) | def split_output_sections(stdout: str, known_sections: list[str]) -> tup...
  function extract_comment_under_first_print (line 94) | def extract_comment_under_first_print(source_code) -> tuple[Optional[str...
  function extract_first_section_name_from_code (line 133) | def extract_first_section_name_from_code(source_code):
  function extract_first_section_name_from_output (line 151) | def extract_first_section_name_from_output(stdout: str) -> Optional[str]:
  function is_function_called (line 161) | def is_function_called(source_code: str, func_name: str) -> bool:
  function remove_function (line 178) | def remove_function(source_code: str, function_name: str) -> str:
  function remove_main_block (line 194) | def remove_main_block(source_code: str) -> str:
  function extract_top_level_functions_with_decorators_and_comments (line 224) | def extract_top_level_functions_with_decorators_and_comments(
  function split_code_and_output_into_sections (line 300) | def split_code_and_output_into_sections(code: str, stdout: str) -> list[...

FILE: rdagent/components/coder/data_science/utils.py
  function remove_eda_part (line 4) | def remove_eda_part(stdout: str) -> str:

FILE: rdagent/components/coder/data_science/workflow/__init__.py
  class WorkflowMultiProcessEvolvingStrategy (line 26) | class WorkflowMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 27) | def implement_one_task(
    method assign_code_list_to_evo (line 94) | def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
  class WorkflowCoSTEER (line 111) | class WorkflowCoSTEER(DSCoSTEER):
    method __init__ (line 112) | def __init__(

FILE: rdagent/components/coder/data_science/workflow/eval.py
  class WorkflowGeneralCaseSpecEvaluator (line 27) | class WorkflowGeneralCaseSpecEvaluator(CoSTEEREvaluator):
    method evaluate (line 36) | def evaluate(

FILE: rdagent/components/coder/data_science/workflow/exp.py
  class WorkflowTask (line 12) | class WorkflowTask(CoSTEERTask):
    method __init__ (line 13) | def __init__(self, name: str = "Workflow", *args, **kwargs) -> None:

FILE: rdagent/components/coder/data_science/workflow/test.py
  function develop_one_competition (line 18) | def develop_one_competition(competition: str):

FILE: rdagent/components/coder/factor_coder/__init__.py
  class FactorCoSTEER (line 12) | class FactorCoSTEER(CoSTEER):
    method __init__ (line 13) | def __init__(
    method develop (line 25) | def develop(self, exp: Experiment) -> Experiment:

FILE: rdagent/components/coder/factor_coder/config.py
  class FactorCoSTEERSettings (line 10) | class FactorCoSTEERSettings(CoSTEERSettings):
  function get_factor_env (line 32) | def get_factor_env(

FILE: rdagent/components/coder/factor_coder/eva_utils.py
  class FactorEvaluator (line 16) | class FactorEvaluator:
    method __init__ (line 19) | def __init__(self, scen=None) -> None:
    method evaluate (line 23) | def evaluate(
    method _get_df (line 46) | def _get_df(self, gt_implementation: Workspace, implementation: Worksp...
    method __str__ (line 63) | def __str__(self) -> str:
  class FactorCodeEvaluator (line 67) | class FactorCodeEvaluator(FactorEvaluator):
    method evaluate (line 68) | def evaluate(
  class FactorInfEvaluator (line 120) | class FactorInfEvaluator(FactorEvaluator):
    method evaluate (line 121) | def evaluate(
  class FactorSingleColumnEvaluator (line 142) | class FactorSingleColumnEvaluator(FactorEvaluator):
    method evaluate (line 143) | def evaluate(
  class FactorOutputFormatEvaluator (line 163) | class FactorOutputFormatEvaluator(FactorEvaluator):
    method evaluate (line 164) | def evaluate(
  class FactorDatetimeDailyEvaluator (line 217) | class FactorDatetimeDailyEvaluator(FactorEvaluator):
    method evaluate (line 218) | def evaluate(
  class FactorRowCountEvaluator (line 247) | class FactorRowCountEvaluator(FactorEvaluator):
    method evaluate (line 248) | def evaluate(
  class FactorIndexEvaluator (line 271) | class FactorIndexEvaluator(FactorEvaluator):
    method evaluate (line 272) | def evaluate(
  class FactorMissingValuesEvaluator (line 296) | class FactorMissingValuesEvaluator(FactorEvaluator):
    method evaluate (line 297) | def evaluate(
  class FactorEqualValueRatioEvaluator (line 317) | class FactorEqualValueRatioEvaluator(FactorEvaluator):
    method evaluate (line 318) | def evaluate(
  class FactorCorrelationEvaluator (line 348) | class FactorCorrelationEvaluator(FactorEvaluator):
    method __init__ (line 349) | def __init__(self, hard_check: bool, *args, **kwargs) -> None:
    method evaluate (line 353) | def evaluate(
  class FactorValueEvaluator (line 389) | class FactorValueEvaluator(FactorEvaluator):
    method evaluate (line 390) | def evaluate(
  class FactorFinalDecisionEvaluator (line 478) | class FactorFinalDecisionEvaluator(FactorEvaluator):
    method evaluate (line 479) | def evaluate(

FILE: rdagent/components/coder/factor_coder/evaluators.py
  class FactorEvaluatorForCoder (line 20) | class FactorEvaluatorForCoder(CoSTEEREvaluator):
    method __init__ (line 25) | def __init__(self, *args, **kwargs) -> None:
    method evaluate (line 31) | def evaluate(
  function shorten_prompt (line 124) | def shorten_prompt(tpl: str, render_kwargs: dict, shorten_key: str, max_...

FILE: rdagent/components/coder/factor_coder/evolving_strategy.py
  class FactorMultiProcessEvolvingStrategy (line 23) | class FactorMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method __init__ (line 24) | def __init__(self, *args, **kwargs) -> None:
    method error_summary (line 29) | def error_summary(
    method implement_one_task (line 60) | def implement_one_task(
    method assign_code_list_to_evo (line 166) | def assign_code_list_to_evo(self, code_list, evo):

FILE: rdagent/components/coder/factor_coder/factor.py
  class FactorTask (line 20) | class FactorTask(CoSTEERTask):
    method __init__ (line 23) | def __init__(
    method factor_description (line 44) | def factor_description(self):
    method get_task_information (line 48) | def get_task_information(self):
    method get_task_brief_information (line 54) | def get_task_brief_information(self):
    method get_task_information_and_implementation_result (line 60) | def get_task_information_and_implementation_result(self):
    method from_dict (line 70) | def from_dict(dict):
    method __repr__ (line 73) | def __repr__(self) -> str:
  class FactorFBWorkspace (line 77) | class FactorFBWorkspace(FBWorkspace):
    method __init__ (line 90) | def __init__(
    method hash_func (line 99) | def hash_func(self, data_type: str = "Debug") -> str:
    method execute (line 107) | def execute(self, data_type: str = "Debug") -> Tuple[str, pd.DataFrame]:
    method __str__ (line 212) | def __str__(self) -> str:
    method __repr__ (line 217) | def __repr__(self) -> str:
    method from_folder (line 221) | def from_folder(task: FactorTask, path: Union[str, Path], **kwargs):

FILE: rdagent/components/coder/finetune/__init__.py
  class LLMFinetuneEvolvingStrategy (line 45) | class LLMFinetuneEvolvingStrategy(MultiProcessEvolvingStrategy):
    method __init__ (line 48) | def __init__(self, scen: Scenario, settings, *args, **kwargs):
    method implement_func_list (line 52) | def implement_func_list(self) -> list[Callable]:
    method implement_data (line 55) | def implement_data(
    method _get_dataset_info (line 153) | def _get_dataset_info(self, involving_datasets: list[str], datasets_pa...
    method implement_lf_config (line 245) | def implement_lf_config(
    method _generate_llamafactory_config_with_llm (line 288) | def _generate_llamafactory_config_with_llm(
  class LLMFinetuneCoSTEER (line 368) | class LLMFinetuneCoSTEER(CoSTEER):
    method __init__ (line 371) | def __init__(

FILE: rdagent/components/coder/finetune/conf.py
  function is_docker_env (line 27) | def is_docker_env(env: Env) -> bool:
  function get_workspace_prefix (line 32) | def get_workspace_prefix(env: Env) -> str:
  function get_data_processing_cache_key (line 52) | def get_data_processing_cache_key(local_path: str | Path) -> list[list[s...
  class FTPathConfig (line 70) | class FTPathConfig:
    method is_docker (line 85) | def is_docker(self) -> bool:
    method models (line 92) | def models(self) -> str:
    method datasets (line 99) | def datasets(self) -> str:
    method workspace (line 106) | def workspace(self) -> str:
    method deepspeed (line 111) | def deepspeed(self) -> str:
  class FTCoderCoSTEERSettings (line 126) | class FTCoderCoSTEERSettings(CoSTEERSettings):
    class Config (line 129) | class Config:
  function _get_standard_ft_volumes (line 142) | def _get_standard_ft_volumes() -> dict:
  function get_ft_env (line 168) | def get_ft_env(
  function get_data_processing_env (line 231) | def get_data_processing_env(
  function clear_workspace (line 273) | def clear_workspace(workspace: FBWorkspace, env: Env) -> None:
  function get_benchmark_env (line 308) | def get_benchmark_env(
  function inject_data_stats (line 368) | def inject_data_stats(implementation: FBWorkspace, data: list, stdout: s...

FILE: rdagent/components/coder/finetune/eval.py
  class FTDataEvaluator (line 42) | class FTDataEvaluator(CoSTEEREvaluator):
    method evaluate (line 51) | def evaluate(
    method _generate_llm_feedback (line 134) | def _generate_llm_feedback(
    method _validate_data_json (line 207) | def _validate_data_json(self, data_json_path: Path) -> dict:
    method _update_dataset_info (line 246) | def _update_dataset_info(self, implementation: FBWorkspace, sample_cou...
    method _sample_data (line 271) | def _sample_data(self, data: list, n: int = 5) -> list:
    method _analyze_data_quality (line 277) | def _analyze_data_quality(self, data: list) -> dict:
  class FTCoderEvaluator (line 307) | class FTCoderEvaluator(CoSTEEREvaluator):
    method __init__ (line 310) | def __init__(self, *args, **kwargs):
    method evaluate (line 313) | def evaluate(

FILE: rdagent/components/coder/finetune/exp.py
  class FTTask (line 12) | class FTTask(CoSTEERTask):
    method __init__ (line 15) | def __init__(
    method get_task_information (line 31) | def get_task_information(self) -> str:

FILE: rdagent/components/coder/finetune/unified_validator.py
  class ValidationResult (line 43) | class ValidationResult:
  class LLMConfigValidator (line 54) | class LLMConfigValidator:
    method __init__ (line 63) | def __init__(self):
    method validate_and_test (line 66) | def validate_and_test(self, config_yaml: str, workspace: FBWorkspace, ...
    method _filter_parameters (line 89) | def _filter_parameters(self, config_yaml: str) -> tuple[str, List[str]]:
    method _inject_required_parameters (line 114) | def _inject_required_parameters(self, config_yaml: str) -> str:
    method _get_supported_parameters (line 128) | def _get_supported_parameters(self) -> Set[str]:
    method _parse_execution_log (line 158) | def _parse_execution_log(self, stdout: str, exit_code: int, failed_sta...
    method _run_micro_batch_test (line 256) | def _run_micro_batch_test(self, config_yaml: str, workspace: FBWorkspa...

FILE: rdagent/components/coder/model_coder/__init__.py
  class ModelCoSTEER (line 11) | class ModelCoSTEER(CoSTEER):
    method __init__ (line 12) | def __init__(

FILE: rdagent/components/coder/model_coder/benchmark/eval.py
  function get_data_conf (line 7) | def get_data_conf(init_val):
  class ModelImpValEval (line 17) | class ModelImpValEval:
    method evaluate (line 35) | def evaluate(self, gt: ModelFBWorkspace, gen: ModelFBWorkspace):

FILE: rdagent/components/coder/model_coder/benchmark/gt_code/A-DGN.py
  class AntiSymmetricConv (line 13) | class AntiSymmetricConv(torch.nn.Module):
    method __init__ (line 54) | def __init__(
    method reset_parameters (line 87) | def reset_parameters(self):
    method forward (line 93) | def forward(self, x: Tensor, edge_index: Adj, *args, **kwargs) -> Tensor:
    method __repr__ (line 111) | def __repr__(self) -> str:

FILE: rdagent/components/coder/model_coder/benchmark/gt_code/dirgnn.py
  class DirGNNConv (line 8) | class DirGNNConv(torch.nn.Module):
    method __init__ (line 26) | def __init__(
    method reset_parameters (line 54) | def reset_parameters(self):
    method forward (line 61) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
    method __repr__ (line 73) | def __repr__(self) -> str:

FILE: rdagent/components/coder/model_coder/benchmark/gt_code/gpsconv.py
  class GPSConv (line 16) | class GPSConv(torch.nn.Module):
    method __init__ (line 59) | def __init__(
    method reset_parameters (line 116) | def reset_parameters(self):
    method forward (line 129) | def forward(
    method __repr__ (line 178) | def __repr__(self) -> str:

FILE: rdagent/components/coder/model_coder/benchmark/gt_code/linkx.py
  class SparseLinear (line 13) | class SparseLinear(MessagePassing):
    method __init__ (line 14) | def __init__(self, in_channels: int, out_channels: int, bias: bool = T...
    method reset_parameters (line 27) | def reset_parameters(self):
    method forward (line 31) | def forward(
    method message (line 44) | def message(self, weight_j: Tensor, edge_weight: OptTensor) -> Tensor:
    method message_and_aggregate (line 50) | def message_and_aggregate(self, adj_t: Adj, weight: Tensor) -> Tensor:
  class LINKX (line 54) | class LINKX(torch.nn.Module):
    method __init__ (line 88) | def __init__(
    method reset_parameters (line 127) | def reset_parameters(self):
    method forward (line 139) | def forward(
    method __repr__ (line 162) | def __repr__(self) -> str:

FILE: rdagent/components/coder/model_coder/benchmark/gt_code/pmlp.py
  class PMLP (line 10) | class PMLP(torch.nn.Module):
    method __init__ (line 30) | def __init__(
    method reset_parameters (line 68) | def reset_parameters(self):
    method forward (line 75) | def forward(
    method __repr__ (line 98) | def __repr__(self) -> str:

FILE: rdagent/components/coder/model_coder/benchmark/gt_code/visnet.py
  class CosineCutoff (line 12) | class CosineCutoff(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, cutoff: float) -> None:
    method forward (line 32) | def forward(self, distances: Tensor) -> Tensor:
  class ExpNormalSmearing (line 48) | class ExpNormalSmearing(torch.nn.Module):
    method __init__ (line 64) | def __init__(
    method _initial_params (line 86) | def _initial_params(self) -> Tuple[Tensor, Tensor]:
    method reset_parameters (line 93) | def reset_parameters(self):
    method forward (line 99) | def forward(self, dist: Tensor) -> Tensor:
  class Sphere (line 110) | class Sphere(torch.nn.Module):
    method __init__ (line 124) | def __init__(self, lmax: int = 2) -> None:
    method forward (line 128) | def forward(self, edge_vec: Tensor) -> Tensor:
    method _spherical_harmonics (line 142) | def _spherical_harmonics(
  class VecLayerNorm (line 188) | class VecLayerNorm(torch.nn.Module):
    method __init__ (line 203) | def __init__(
    method reset_parameters (line 223) | def reset_parameters(self):
    method max_min_norm (line 227) | def max_min_norm(self, vec: Tensor) -> Tensor:
    method forward (line 258) | def forward(self, vec: Tensor) -> Tensor:
  class Distance (line 279) | class Distance(torch.nn.Module):
    method __init__ (line 296) | def __init__(
    method forward (line 307) | def forward(
  class NeighborEmbedding (line 344) | class NeighborEmbedding(MessagePassing):
    method __init__ (line 358) | def __init__(
    method reset_parameters (line 373) | def reset_parameters(self):
    method forward (line 381) | def forward(
    method message (line 416) | def message(self, x_j: Tensor, W: Tensor) -> Tensor:
  class EdgeEmbedding (line 420) | class EdgeEmbedding(torch.nn.Module):
    method __init__ (line 431) | def __init__(self, num_rbf: int, hidden_channels: int) -> None:
    method reset_parameters (line 436) | def reset_parameters(self):
    method forward (line 441) | def forward(
  class ViS_MP (line 462) | class ViS_MP(MessagePassing):
    method __init__ (line 482) | def __init__(
    method vector_rejection (line 536) | def vector_rejection(vec: Tensor, d_ij: Tensor) -> Tensor:
    method reset_parameters (line 546) | def reset_parameters(self):
    method forward (line 573) | def forward(
    method message (line 624) | def message(
    method edge_update (line 638) | def edge_update(self, vec_i: Tensor, vec_j: Tensor, d_ij: Tensor, f_ij...
    method aggregate (line 645) | def aggregate(
  class ViS_MP_Vertex (line 658) | class ViS_MP_Vertex(ViS_MP):
    method __init__ (line 678) | def __init__(
    method reset_parameters (line 696) | def reset_parameters(self):
    method edge_update (line 706) | def edge_update(self, vec_i: Tensor, vec_j: Tensor, d_ij: Tensor, f_ij...
  class ViSNetBlock (line 720) | class ViSNetBlock(torch.nn.Module):
    method __init__ (line 752) | def __init__(
    method reset_parameters (line 811) | def reset_parameters(self):
    method forward (line 822) | def forward(
  class GatedEquivariantBlock (line 866) | class GatedEquivariantBlock(torch.nn.Module):
    method __init__ (line 884) | def __init__(
    method reset_parameters (line 910) | def reset_parameters(self):
    method forward (line 919) | def forward(self, x: Tensor, v: Tensor) -> Tuple[Tensor, Tensor]:
  class EquivariantScalar (line 940) | class EquivariantScalar(torch.nn.Module):
    method __init__ (line 949) | def __init__(self, hidden_channels: int) -> None:
    method reset_parameters (line 969) | def reset_parameters(self):
    method pre_reduce (line 974) | def pre_reduce(self, x: Tensor, v: Tensor) -> Tensor:
  class Atomref (line 990) | class Atomref(torch.nn.Module):
    method __init__ (line 1000) | def __init__(
    method reset_parameters (line 1020) | def reset_parameters(self):
    method forward (line 1024) | def forward(self, x: Tensor, z: Tensor) -> Tensor:
  class ViSNet (line 1034) | class ViSNet(torch.nn.Module):
    method __init__ (line 1077) | def __init__(
    method reset_parameters (line 1124) | def reset_parameters(self):
    method forward (line 1131) | def forward(

FILE: rdagent/components/coder/model_coder/conf.py
  class ModelCoSTEERSettings (line 9) | class ModelCoSTEERSettings(CoSTEERSettings):
  function get_model_env (line 16) | def get_model_env(

FILE: rdagent/components/coder/model_coder/eva_utils.py
  function shape_evaluator (line 15) | def shape_evaluator(prediction: np.ndarray, target_shape: Tuple = None) ...
  function value_evaluator (line 32) | def value_evaluator(
  class ModelCodeEvaluator (line 52) | class ModelCodeEvaluator(CoSTEEREvaluator):
    method evaluate (line 53) | def evaluate(
  class ModelFinalEvaluator (line 105) | class ModelFinalEvaluator(CoSTEEREvaluator):
    method evaluate (line 106) | def evaluate(

FILE: rdagent/components/coder/model_coder/evaluators.py
  class ModelCoSTEEREvaluator (line 20) | class ModelCoSTEEREvaluator(CoSTEEREvaluator):
    method evaluate (line 21) | def evaluate(

FILE: rdagent/components/coder/model_coder/evolving_strategy.py
  class ModelMultiProcessEvolvingStrategy (line 20) | class ModelMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 21) | def implement_one_task(
    method assign_code_list_to_evo (line 82) | def assign_code_list_to_evo(self, code_list, evo):

FILE: rdagent/components/coder/model_coder/gt_code.py
  class AntiSymmetricConv (line 18) | class AntiSymmetricConv(torch.nn.Module):
    method __init__ (line 59) | def __init__(
    method reset_parameters (line 92) | def reset_parameters(self):
    method forward (line 98) | def forward(self, x: Tensor, edge_index: Adj, *args, **kwargs) -> Tensor:
    method __repr__ (line 116) | def __repr__(self) -> str:

FILE: rdagent/components/coder/model_coder/model.py
  class ModelTask (line 15) | class ModelTask(CoSTEERTask):
    method __init__ (line 16) | def __init__(
    method get_task_information (line 39) | def get_task_information(self):
    method get_task_brief_information (line 51) | def get_task_brief_information(self):
    method from_dict (line 62) | def from_dict(dict):
    method __repr__ (line 65) | def __repr__(self) -> str:
  class ModelFBWorkspace (line 69) | class ModelFBWorkspace(FBWorkspace):
    method hash_func (line 90) | def hash_func(
    method execute (line 105) | def execute(

FILE: rdagent/components/coder/model_coder/one_shot/__init__.py
  class ModelCodeWriter (line 12) | class ModelCodeWriter(Developer[ModelExperiment]):
    method develop (line 13) | def develop(self, exp: ModelExperiment) -> ModelExperiment:

FILE: rdagent/components/coder/model_coder/task_loader.py
  function extract_model_from_doc (line 20) | def extract_model_from_doc(doc_content: str) -> dict:
  function merge_file_to_model_dict_to_model_dict (line 71) | def merge_file_to_model_dict_to_model_dict(
  function extract_model_from_docs (line 92) | def extract_model_from_docs(docs_dict):
  class ModelExperimentLoaderFromDict (line 99) | class ModelExperimentLoaderFromDict(ModelTaskLoader):
    method load (line 100) | def load(self, model_dict: dict) -> QlibModelExperiment:
  class ModelExperimentLoaderFromPDFfiles (line 118) | class ModelExperimentLoaderFromPDFfiles(ModelTaskLoader):
    method load (line 120) | def load(self, file_or_folder_path: str) -> QlibModelExperiment:

FILE: rdagent/components/coder/rl/costeer.py
  class RLCoderCoSTEERSettings (line 23) | class RLCoderCoSTEERSettings(CoSTEERSettings):
  class RLEvolvingStrategy (line 29) | class RLEvolvingStrategy(EvolvingStrategy):
    method __init__ (line 32) | def __init__(self, scen: Scenario, settings: CoSTEERSettings):
    method evolve_iter (line 36) | def evolve_iter(
    method _generate_code (line 54) | def _generate_code(self, task: Task, evolving_trace: list[EvoStep] = [...
    method _mock_code (line 85) | def _mock_code(self) -> dict[str, str]:
  class RLCoderEvaluator (line 98) | class RLCoderEvaluator:
    method __init__ (line 101) | def __init__(self, scen: Scenario) -> None:
    method evaluate (line 104) | def evaluate(
  class RLCoSTEER (line 121) | class RLCoSTEER(CoSTEER):
    method __init__ (line 124) | def __init__(self, scen: Scenario, *args, **kwargs) -> None:

FILE: rdagent/components/document_reader/document_reader.py
  function load_documents_by_langchain (line 20) | def load_documents_by_langchain(path: str) -> list:
  function process_documents_by_langchain (line 36) | def process_documents_by_langchain(docs: list[Document]) -> dict[str, str]:
  function load_and_process_pdfs_by_langchain (line 63) | def load_and_process_pdfs_by_langchain(path: str) -> dict[str, str]:
  function load_and_process_one_pdf_by_azure_document_intelligence (line 67) | def load_and_process_one_pdf_by_azure_document_intelligence(
  function load_and_process_pdfs_by_azure_document_intelligence (line 87) | def load_and_process_pdfs_by_azure_document_intelligence(path: Path) -> ...
  function extract_first_page_screenshot_from_pdf (line 112) | def extract_first_page_screenshot_from_pdf(pdf_path: str) -> Image:

FILE: rdagent/components/interactor/__init__.py
  class SkipInteractor (line 6) | class SkipInteractor(Interactor[ASpecificExp]):
    method interact (line 8) | def interact(self, exp: ASpecificExp, trace: Trace) -> ASpecificExp:

FILE: rdagent/components/knowledge_management/graph.py
  class UndirectedNode (line 22) | class UndirectedNode(Node):
    method __init__ (line 23) | def __init__(self, content: str = "", label: str = "", embedding: Any ...
    method add_neighbor (line 29) | def add_neighbor(self, node: UndirectedNode) -> None:
    method remove_neighbor (line 33) | def remove_neighbor(self, node: UndirectedNode) -> None:
    method get_neighbors (line 38) | def get_neighbors(self) -> set[UndirectedNode]:
    method __str__ (line 41) | def __str__(self) -> str:
    method __repr__ (line 47) | def __repr__(self) -> str:
  class Graph (line 54) | class Graph(KnowledgeBase):
    method __init__ (line 59) | def __init__(self, path: str | Path | None = None) -> None:
    method size (line 63) | def size(self) -> int:
    method get_node (line 66) | def get_node(self, node_id: str) -> Node | None:
    method add_node (line 69) | def add_node(self, **kwargs: Any) -> NoReturn:
    method get_all_nodes (line 72) | def get_all_nodes(self) -> list[Node]:
    method get_all_nodes_by_label_list (line 75) | def get_all_nodes_by_label_list(self, label_list: list[str]) -> list[N...
    method find_node (line 78) | def find_node(self, content: str, label: str) -> Node | None:
    method batch_embedding (line 85) | def batch_embedding(nodes: list[Node]) -> list[Node]:
    method __str__ (line 104) | def __str__(self) -> str:
  class UndirectedGraph (line 108) | class UndirectedGraph(Graph):
    method __init__ (line 113) | def __init__(self, path: str | Path | None = None) -> None:
    method __str__ (line 117) | def __str__(self) -> str:
    method add_node (line 120) | def add_node(
    method add_nodes (line 169) | def add_nodes(self, node: UndirectedNode, neighbors: list[UndirectedNo...
    method get_node (line 176) | def get_node(self, node_id: str) -> UndirectedNode:
    method get_node_by_content (line 179) | def get_node_by_content(self, content: str) -> UndirectedNode | None:
    method get_nodes_within_steps (line 197) | def get_nodes_within_steps(
    method get_nodes_intersection (line 235) | def get_nodes_intersection(
    method semantic_search (line 275) | def semantic_search(
    method clear (line 314) | def clear(self) -> None:
    method query_by_node (line 318) | def query_by_node(
    method query_by_content (line 356) | def query_by_content(
    method intersection (line 430) | def intersection(nodes1: list[UndirectedNode], nodes2: list[Undirected...
    method different (line 434) | def different(nodes1: list[UndirectedNode], nodes2: list[UndirectedNod...
    method cal_distance (line 438) | def cal_distance(node1: UndirectedNode, node2: UndirectedNode) -> float:
    method filter_label (line 442) | def filter_label(nodes: list[UndirectedNode], labels: list[str]) -> li...
  function graph_to_edges (line 446) | def graph_to_edges(graph: dict[str, list[str]]) -> list[tuple[str, str]]:
  function assign_random_coordinate_to_node (line 458) | def assign_random_coordinate_to_node(
  function assign_isometric_coordinate_to_node (line 472) | def assign_isometric_coordinate_to_node(
  function curly_node_coordinate (line 488) | def curly_node_coordinate(

FILE: rdagent/components/knowledge_management/vector_base.py
  class KnowledgeMetaData (line 13) | class KnowledgeMetaData:
    method __init__ (line 14) | def __init__(self, content: str = "", label: str = None, embedding=Non...
    method split_into_trunk (line 22) | def split_into_trunk(self, size: int = 1000, overlap: int = 0):
    method create_embedding (line 40) | def create_embedding(self):
    method from_dict (line 50) | def from_dict(self, data: dict):
    method __repr__ (line 55) | def __repr__(self):
  function contents_to_documents (line 62) | def contents_to_documents(contents: List[str], label: str = None) -> Lis...
  class VectorBase (line 72) | class VectorBase(KnowledgeBase):
    method add (line 77) | def add(self, document: Union[Document, List[Document]]):
    method search (line 90) | def search(self, content: str, topk_k: int | None = None, similarity_t...
  class PDVectorBase (line 106) | class PDVectorBase(VectorBase):
    method __init__ (line 111) | def __init__(self, path: Union[str, Path] = None):
    method shape (line 115) | def shape(self):
    method add (line 118) | def add(self, document: Union[Document, List[Document]]):
    method search (line 158) | def search(

FILE: rdagent/components/loader/experiment_loader.py
  class FactorExperimentLoader (line 5) | class FactorExperimentLoader(Loader[FactorExperiment]):
  class ModelExperimentLoader (line 9) | class ModelExperimentLoader(Loader[FactorExperiment]):

FILE: rdagent/components/loader/task_loader.py
  class FactorTaskLoader (line 10) | class FactorTaskLoader(Loader[FactorTask]):
  class ModelTaskLoader (line 14) | class ModelTaskLoader(Loader[ModelTask]):
  class ModelTaskLoaderJson (line 18) | class ModelTaskLoaderJson(ModelTaskLoader):
    method __init__ (line 44) | def __init__(self, json_uri: str) -> None:
    method load (line 48) | def load(self, *argT, **kwargs) -> Sequence[ModelTask]:
  class ModelWsLoader (line 85) | class ModelWsLoader(WsLoader[ModelTask, ModelFBWorkspace]):
    method __init__ (line 86) | def __init__(self, path: Path) -> None:
    method load (line 89) | def load(self, task: ModelTask) -> ModelFBWorkspace:

FILE: rdagent/components/proposal/__init__.py
  class LLMHypothesisGen (line 18) | class LLMHypothesisGen(HypothesisGen):
    method __init__ (line 19) | def __init__(self, scen: Scenario):
    method prepare_context (line 24) | def prepare_context(self, trace: Trace) -> Tuple[dict, bool]: ...
    method convert_response (line 27) | def convert_response(self, response: str) -> Hypothesis: ...
    method gen (line 29) | def gen(
  class FactorHypothesisGen (line 68) | class FactorHypothesisGen(LLMHypothesisGen):
    method __init__ (line 69) | def __init__(self, scen: Scenario):
  class ModelHypothesisGen (line 74) | class ModelHypothesisGen(LLMHypothesisGen):
    method __init__ (line 75) | def __init__(self, scen: Scenario):
  class FactorAndModelHypothesisGen (line 80) | class FactorAndModelHypothesisGen(LLMHypothesisGen):
    method __init__ (line 81) | def __init__(self, scen: Scenario):
  class LLMHypothesis2Experiment (line 86) | class LLMHypothesis2Experiment(Hypothesis2Experiment[Experiment]):
    method prepare_context (line 88) | def prepare_context(self, hypothesis: Hypothesis, trace: Trace) -> Tup...
    method convert_response (line 91) | def convert_response(self, response: str, hypothesis: Hypothesis, trac...
    method convert (line 94) | def convert(self, hypothesis: Hypothesis, trace: Trace) -> Experiment:
  class FactorHypothesis2Experiment (line 124) | class FactorHypothesis2Experiment(LLMHypothesis2Experiment):
    method __init__ (line 125) | def __init__(self):
  class ModelHypothesis2Experiment (line 130) | class ModelHypothesis2Experiment(LLMHypothesis2Experiment):
    method __init__ (line 131) | def __init__(self):
  class FactorAndModelHypothesis2Experiment (line 136) | class FactorAndModelHypothesis2Experiment(LLMHypothesis2Experiment):
    method __init__ (line 137) | def __init__(self):

FILE: rdagent/components/runner/__init__.py
  class CachedRunner (line 6) | class CachedRunner(Developer[ASpecificExp]):
    method get_cache_key (line 7) | def get_cache_key(self, exp: Experiment) -> str:
    method assign_cached_result (line 16) | def assign_cached_result(self, exp: Experiment, cached_res: Experiment...

FILE: rdagent/components/workflow/conf.py
  class BasePropSetting (line 4) | class BasePropSetting(ExtendedBaseSettings):

FILE: rdagent/components/workflow/rd_loop.py
  class RDLoop (line 31) | class RDLoop(LoopBase, metaclass=LoopMeta):
    method __init__ (line 33) | def __init__(self, PROP_SETTING: BasePropSetting):
    method _set_interactor (line 71) | def _set_interactor(self, user_request_q: Queue, user_response_q: Queue):
    method _init_base_features (line 75) | def _init_base_features(self, base_features_path: str | None):
    method _interact_init_params (line 112) | def _interact_init_params(self) -> None:
    method _interact_hypo (line 154) | def _interact_hypo(self, hypo: Hypothesis) -> Hypothesis:
    method _interact_feedback (line 169) | def _interact_feedback(self, feedback: HypothesisFeedback) -> Hypothes...
    method _propose (line 184) | def _propose(self):
    method _exp_gen (line 193) | def _exp_gen(self, hypothesis: Hypothesis):
    method direct_exp_gen (line 199) | async def direct_exp_gen(self, prev_out: dict[str, Any]):
    method coding (line 212) | def coding(self, prev_out: dict[str, Any]):
    method running (line 217) | def running(self, prev_out: dict[str, Any]):
    method feedback (line 222) | def feedback(self, prev_out: dict[str, Any]):
    method record (line 238) | def record(self, prev_out: dict[str, Any]):

FILE: rdagent/core/conf.py
  class ExtendedBaseSettings (line 13) | class ExtendedBaseSettings(BaseSettings):
    method settings_customise_sources (line 16) | def settings_customise_sources(
  class RDAgentSettings (line 46) | class RDAgentSettings(ExtendedBaseSettings):
    method get_max_parallel (line 92) | def get_max_parallel(self) -> int:
    method is_force_subproc (line 102) | def is_force_subproc(self) -> bool:

FILE: rdagent/core/developer.py
  class Developer (line 12) | class Developer(ABC, Generic[ASpecificExp]):
    method __init__ (line 13) | def __init__(self, scen: Scenario) -> None:
    method develop (line 17) | def develop(self, exp: ASpecificExp) -> ASpecificExp:  # TODO: remove ...

FILE: rdagent/core/evaluation.py
  class Feedback (line 8) | class Feedback:
    method is_acceptable (line 15) | def is_acceptable(self) -> bool:
    method finished (line 22) | def finished(self) -> bool:
    method __bool__ (line 29) | def __bool__(self) -> bool:
  class EvaluableObj (line 33) | class EvaluableObj:
  class Evaluator (line 42) | class Evaluator(ABC):
    method evaluate (line 53) | def evaluate(

FILE: rdagent/core/evolving_agent.py
  class EvoAgent (line 26) | class EvoAgent(ABC, Generic[ASpecificEvaluator, ASpecificEvolvableSubjec...
    method __init__ (line 28) | def __init__(self, max_loop: int, evolving_strategy: EvolvingStrategy)...
    method multistep_evolve (line 33) | def multistep_evolve(
  class RAGEvaluator (line 43) | class RAGEvaluator(IterEvaluator):
    method evaluate_iter (line 46) | def evaluate_iter(
  class RAGEvoAgent (line 78) | class RAGEvoAgent(EvoAgent[RAGEvaluator, ASpecificEvolvableSubjects], Ge...
    method __init__ (line 80) | def __init__(
    method _get_overall_feedback (line 120) | def _get_overall_feedback(
    method multistep_evolve (line 140) | def multistep_evolve(

FILE: rdagent/core/evolving_framework.py
  class Knowledge (line 17) | class Knowledge:
  class QueriedKnowledge (line 21) | class QueriedKnowledge:
  class EvolvingKnowledgeBase (line 25) | class EvolvingKnowledgeBase(KnowledgeBase):
    method query (line 27) | def query(
  class EvolvableSubjects (line 33) | class EvolvableSubjects(EvaluableObj):
    method clone (line 36) | def clone(self) -> EvolvableSubjects:
  class EvoStep (line 44) | class EvoStep(Generic[ASpecificEvolvableSubjects]):
  class EvolvingStrategy (line 61) | class EvolvingStrategy(ABC, Generic[ASpecificEvolvableSubjects]):
    method __init__ (line 62) | def __init__(self, scen: Scenario) -> None:
    method evolve_iter (line 66) | def evolve_iter(
  class IterEvaluator (line 94) | class IterEvaluator(Evaluator):
    method evaluate (line 101) | def evaluate(self, eo: EvaluableObj) -> Feedback:
    method evaluate_iter (line 115) | def evaluate_iter(self) -> Generator[Feedback, EvaluableObj | None, Fe...
  class RAGStrategy (line 141) | class RAGStrategy(ABC, Generic[ASpecificEvolvableSubjects]):
    method __init__ (line 144) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method load_or_init_knowledge_base (line 148) | def load_or_init_knowledge_base(
    method query (line 156) | def query(
    method generate_knowledge (line 165) | def generate_knowledge(
    method dump_knowledge_base (line 179) | def dump_knowledge_base(self, *args: Any, **kwargs: Any) -> None:
    method load_dumped_knowledge_base (line 183) | def load_dumped_knowledge_base(self, *args: Any, **kwargs: Any) -> None:

FILE: rdagent/core/exception.py
  class WorkflowError (line 1) | class WorkflowError(Exception):
  class FormatError (line 7) | class FormatError(WorkflowError):
  class CodeBlockParseError (line 13) | class CodeBlockParseError(FormatError):
    method __init__ (line 16) | def __init__(self, message: str, content: str, language: str) -> None:
  class CoderError (line 23) | class CoderError(WorkflowError):
  class CodeFormatError (line 36) | class CodeFormatError(CoderError):
  class CustomRuntimeError (line 42) | class CustomRuntimeError(CoderError):
  class NoOutputError (line 48) | class NoOutputError(CoderError):
  class RunnerError (line 54) | class RunnerError(Exception):
  class KaggleError (line 67) | class KaggleError(Exception):
  class PolicyError (line 73) | class PolicyError(Exception):
  class EvaluatorDidNotTerminateError (line 79) | class EvaluatorDidNotTerminateError(RuntimeError):

FILE: rdagent/core/experiment.py
  class AbsTask (line 34) | class AbsTask(ABC):
    method __init__ (line 35) | def __init__(self, name: str, version: int = 1) -> None:
    method get_task_information (line 45) | def get_task_information(self) -> str:
  class UserInstructions (line 51) | class UserInstructions(list[str]):
    method __str__ (line 52) | def __str__(self) -> str:
  class Task (line 58) | class Task(AbsTask):
    method __init__ (line 59) | def __init__(
    method get_task_information (line 70) | def get_task_information(self) -> str:
    method __repr__ (line 73) | def __repr__(self) -> str:
  class RunningInfo (line 82) | class RunningInfo:
  class Workspace (line 87) | class Workspace(ABC, Generic[ASpecificTask, ASpecificFeedback]):
    method __init__ (line 93) | def __init__(self, target_task: ASpecificTask | None = None) -> None:
    method execute (line 99) | def execute(self, *args: Any, **kwargs: Any) -> object | None:
    method copy (line 104) | def copy(self) -> Workspace:
    method all_codes (line 110) | def all_codes(self) -> str:
    method create_ws_ckp (line 117) | def create_ws_ckp(self) -> None:
    method recover_ws_ckp (line 123) | def recover_ws_ckp(self) -> None:
  class WsLoader (line 132) | class WsLoader(ABC, Generic[ASpecificTask, ASpecificWS]):
    method load (line 134) | def load(self, task: ASpecificTask) -> ASpecificWS:
  class FBWorkspace (line 139) | class FBWorkspace(Workspace):
    method __init__ (line 162) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method _format_code_dict (line 172) | def _format_code_dict(code_dict: dict[str, str]) -> str:
    method all_codes (line 182) | def all_codes(self) -> str:
    method get_codes (line 189) | def get_codes(self, pattern: str) -> str:
    method prepare (line 198) | def prepare(self) -> None:
    method link_all_files_in_folder_to_workspace (line 209) | def link_all_files_in_folder_to_workspace(data_path: Path, workspace_p...
    method inject_files (line 223) | def inject_files(self, **files: str) -> None:
    method remove_files (line 245) | def remove_files(self, file_names: str | list[str]) -> None:
    method get_files (line 257) | def get_files(self) -> list[Path]:
    method inject_code_from_folder (line 266) | def inject_code_from_folder(self, folder_path: Path) -> None:
    method inject_code_from_file_dict (line 275) | def inject_code_from_file_dict(self, workspace: FBWorkspace) -> None:
    method inject_from_workspace (line 283) | def inject_from_workspace(self, workspace: FBWorkspace) -> None:
    method copy (line 287) | def copy(self) -> FBWorkspace:
    method clear (line 293) | def clear(self) -> None:
    method before_execute (line 300) | def before_execute(self) -> None:
    method execute (line 307) | def execute(self, env: Env, entry: str) -> str:
    method run (line 314) | def run(self, env: Env, entry: str) -> EnvResult:
    method create_ws_ckp (line 324) | def create_ws_ckp(self) -> None:
    method recover_ws_ckp (line 350) | def recover_ws_ckp(self) -> None:
    method __str__ (line 380) | def __str__(self) -> str:
  class ExperimentPlan (line 390) | class ExperimentPlan(dict[str, Any]):
  class Experiment (line 396) | class Experiment(
    method __init__ (line 404) | def __init__(
    method set_user_instructions (line 446) | def set_user_instructions(self, user_instructions: UserInstructions | ...
    method result (line 461) | def result(self) -> object:
    method result (line 465) | def result(self, value: object) -> None:
    method create_ws_ckp (line 469) | def create_ws_ckp(self) -> None:
    method recover_ws_ckp (line 476) | def recover_ws_ckp(self) -> None:
  class Loader (line 495) | class Loader(ABC, Generic[TaskOrExperiment]):
    method load (line 497) | def load(self, *args: Any, **kwargs: Any) -> TaskOrExperiment:

FILE: rdagent/core/interactor.py
  class Interactor (line 13) | class Interactor(ABC, Generic[ASpecificExp]):
    method __init__ (line 14) | def __init__(self, scen: Scenario) -> None:
    method interact (line 18) | def interact(self, exp: ASpecificExp, trace: Trace | None = None) -> A...

FILE: rdagent/core/knowledge_base.py
  class KnowledgeBase (line 8) | class KnowledgeBase:
    method __init__ (line 9) | def __init__(self, path: str | Path | None = None) -> None:
    method load (line 13) | def load(self) -> None:
    method dump (line 22) | def dump(self) -> None:

FILE: rdagent/core/prompts.py
  class Prompts (line 8) | class Prompts(SingletonBaseClass, dict[str, str]):
    method __init__ (line 9) | def __init__(self, file_path: Path) -> None:

FILE: rdagent/core/proposal.py
  class Hypothesis (line 24) | class Hypothesis:
    method __init__ (line 32) | def __init__(
    method __str__ (line 48) | def __str__(self) -> str:
  class ExperimentFeedback (line 58) | class ExperimentFeedback(Feedback):
    method __init__ (line 59) | def __init__(
    method __bool__ (line 78) | def __bool__(self) -> bool:
    method __str__ (line 81) | def __str__(self) -> str:
    method from_exception (line 89) | def from_exception(cls, e: Exception) -> ExperimentFeedback:
  class HypothesisFeedback (line 96) | class HypothesisFeedback(ExperimentFeedback):
    method __init__ (line 97) | def __init__(
    method __str__ (line 122) | def __str__(self) -> str:
  class Trace (line 141) | class Trace(Generic[ASpecificScen, ASpecificKB]):
    method __init__ (line 146) | def __init__(self, scen: ASpecificScen, knowledge_base: ASpecificKB | ...
    method get_sota_hypothesis_and_experiment (line 178) | def get_sota_hypothesis_and_experiment(self) -> tuple[Hypothesis | Non...
    method is_selection_new_tree (line 187) | def is_selection_new_tree(self, selection: tuple[int, ...] | None = No...
    method get_current_selection (line 197) | def get_current_selection(self) -> tuple[int, ...]:
    method set_current_selection (line 200) | def set_current_selection(self, selection: tuple[int, ...]) -> None:
    method get_parent_exps (line 203) | def get_parent_exps(
    method exp2idx (line 219) | def exp2idx(self, exp: Experiment | list[Experiment]) -> int | list[in...
    method idx2exp (line 231) | def idx2exp(self, idx: int | list[int]) -> Experiment | list[Experiment]:
    method is_parent (line 237) | def is_parent(self, parent_idx: int, child_idx: int) -> bool:
    method get_parents (line 241) | def get_parents(self, child_idx: int) -> list[int]:
    method sync_dag_parent_and_hist (line 256) | def sync_dag_parent_and_hist(
    method get_children (line 286) | def get_children(self, parent_idx: int | None = None) -> list[NodeType]:
    method get_sota_experiment (line 298) | def get_sota_experiment(self, node_id: int | None = None) -> Experimen...
  class CheckpointSelector (line 321) | class CheckpointSelector:
    method get_selection (line 327) | def get_selection(self, trace: Trace) -> tuple[int, ...] | None:
  class SOTAexpSelector (line 343) | class SOTAexpSelector:
    method get_sota_exp_to_submit (line 349) | def get_sota_exp_to_submit(self, trace: Trace) -> Experiment | None:
  class ExpPlanner (line 355) | class ExpPlanner(ABC, Generic[ASpecificPlan]):
    method __init__ (line 361) | def __init__(self, scen: Scenario) -> None:
    method plan (line 365) | def plan(self, trace: Trace) -> ASpecificPlan:
  class ExpGen (line 372) | class ExpGen(ABC):
    method __init__ (line 374) | def __init__(self, scen: Scenario) -> None:
    method gen (line 378) | def gen(self, trace: Trace) -> Experiment:
    method async_gen (line 394) | async def async_gen(self, trace: Trace, loop: LoopBase) -> Experiment:
    method reset (line 405) | def reset(self) -> None:
  class HypothesisGen (line 414) | class HypothesisGen(ABC):
    method __init__ (line 416) | def __init__(self, scen: Scenario) -> None:
    method gen (line 420) | def gen(
  class Hypothesis2Experiment (line 437) | class Hypothesis2Experiment(ABC, Generic[ASpecificExp]):
    method convert (line 443) | def convert(self, hypothesis: Hypothesis, trace: Trace) -> ASpecificExp:
  class Experiment2Feedback (line 451) | class Experiment2Feedback(ABC):
    method __init__ (line 455) | def __init__(self, scen: Scenario) -> None:
    method generate_feedback (line 459) | def generate_feedback(

FILE: rdagent/core/scenario.py
  class Scenario (line 6) | class Scenario(ABC):
    method background (line 14) | def background(self) -> str:
    method get_source_data_desc (line 18) | def get_source_data_desc(self, task: Task | None = None) -> str:  # no...
    method source_data (line 27) | def source_data(self) -> str:
    method rich_style_description (line 39) | def rich_style_description(self) -> str:
    method get_scenario_all_desc (line 43) | def get_scenario_all_desc(
    method get_runtime_environment (line 56) | def get_runtime_environment(self) -> str:
    method experiment_setting (line 62) | def experiment_setting(self) -> str | None:

FILE: rdagent/core/utils.py
  class RDAgentException (line 20) | class RDAgentException(Exception):  # noqa: N818
  class SingletonBaseClass (line 24) | class SingletonBaseClass:
    method __new__ (line 32) | def __new__(cls, *args: Any, **kwargs: Any) -> Any:
    method __reduce__ (line 47) | def __reduce__(self) -> NoReturn:
  function parse_json (line 58) | def parse_json(response: str) -> Any:
  function similarity (line 67) | def similarity(text1: str, text2: str) -> int:
  function import_class (line 75) | def import_class(class_path: str) -> Any:
  class CacheSeedGen (line 91) | class CacheSeedGen:
    method __init__ (line 101) | def __init__(self) -> None:
    method set_seed (line 104) | def set_seed(self, seed: int) -> None:
    method get_next_seed (line 107) | def get_next_seed(self) -> int:
  function _subprocess_wrapper (line 115) | def _subprocess_wrapper(f: Callable, seed: int, args: list) -> Any:
  function multiprocessing_wrapper (line 124) | def multiprocessing_wrapper(func_calls: list[tuple[Callable, tuple]], n:...
  function cache_with_pickle (line 156) | def cache_with_pickle(hash_func: Callable, post_process_func: Callable |...

FILE: rdagent/log/base.py
  class Message (line 12) | class Message:
  class Storage (line 25) | class Storage:
    method log (line 43) | def log(
    method iter_msg (line 67) | def iter_msg(self) -> Generator[Message, None, None]:
    method truncate (line 74) | def truncate(self, time: datetime) -> None:
    method __str__ (line 80) | def __str__(self) -> str:
  class View (line 84) | class View:
    method display (line 93) | def display(self, s: Storage, watch: bool = False) -> None:

FILE: rdagent/log/conf.py
  class LogSettings (line 10) | class LogSettings(ExtendedBaseSettings):
    method set_ui_server_port (line 22) | def set_ui_server_port(self, port: int | None) -> None:
    method model_post_init (line 30) | def model_post_init(self, _context: Any, /) -> None:

FILE: rdagent/log/logger.py
  class RDAgentLog (line 20) | class RDAgentLog(SingletonBaseClass):
    method _configure_console_sinks (line 48) | def _configure_console_sinks(cls) -> None:
    method _tag (line 59) | def _tag(self) -> str:  # Get current tag
    method _tag (line 63) | def _tag(self, value: str) -> None:
    method __init__ (line 66) | def __init__(self) -> None:
    method refresh_storages_from_settings (line 76) | def refresh_storages_from_settings(self) -> None:
    method rebind_console_to_current_streams (line 82) | def rebind_console_to_current_streams(self) -> None:
    method tag (line 92) | def tag(self, tag: str) -> Generator[None, None, None]:
    method set_storages_path (line 106) | def set_storages_path(self, path: str | Path) -> None:
    method truncate_storages (line 113) | def truncate_storages(self, time: datetime) -> None:
    method get_pids (line 117) | def get_pids(self) -> str:
    method log_object (line 132) | def log_object(self, obj: object, *, tag: str = "") -> None:
    method _log (line 138) | def _log(self, level: str, msg: str, *, tag: str = "", raw: bool = Fal...
    method info (line 146) | def info(self, msg: str, *, tag: str = "", raw: bool = False) -> None:
    method warning (line 149) | def warning(self, msg: str, *, tag: str = "", raw: bool = False) -> None:
    method error (line 152) | def error(self, msg: str, *, tag: str = "", raw: bool = False) -> None:

FILE: rdagent/log/mle_summary.py
  function save_grade_info (line 25) | def save_grade_info(log_trace_path: Path):
  function save_all_grade_info (line 45) | def save_all_grade_info(log_folder: str | Path) -> None:
  function _get_loop_and_fn_after_hours (line 54) | def _get_loop_and_fn_after_hours(log_folder: Path, hours: int):
  function summarize_folder (line 74) | def summarize_folder(log_folder: Path, hours: int | None = None) -> None:
  function grade_summary (line 244) | def grade_summary(log_folder: str) -> None:

FILE: rdagent/log/server/app.py
  class _YellowWarningFormatter (line 30) | class _YellowWarningFormatter(logging.Formatter):
    method format (line 31) | def format(self, record: logging.LogRecord) -> str:
  function _configure_app_logger (line 37) | def _configure_app_logger() -> None:
  class RDAgentTask (line 52) | class RDAgentTask:
    method __init__ (line 53) | def __init__(
    method start (line 88) | def start(self) -> None:
    method is_alive (line 92) | def is_alive(self) -> bool:
    method get_end_code (line 95) | def get_end_code(self) -> int:
    method stop (line 100) | def stop(self) -> None:
    method _run (line 116) | def _run(self) -> None:
  function _drain_user_requests_into_messages (line 175) | def _drain_user_requests_into_messages(task: RDAgentTask) -> None:
  function favicon (line 202) | def favicon():
  function _normalize_static_request_path (line 206) | def _normalize_static_request_path(fn: str) -> str:
  function _get_or_create_task (line 213) | def _get_or_create_task(trace_id: str) -> RDAgentTask:
  function _resolve_stdout_path (line 230) | def _resolve_stdout_path(trace_id: str) -> Path | None:
  function read_trace (line 250) | def read_trace(log_path: Path, id: str = "") -> None:
  function update_trace (line 284) | def update_trace():
  function download_stdout_file (line 333) | def download_stdout_file():
  function upload_file (line 351) | def upload_file():
  function receive_msgs (line 454) | def receive_msgs():
  function submit_user_interaction_response (line 474) | def submit_user_interaction_response():
  function control_process (line 497) | def control_process():
  function test (line 537) | def test():
  function index (line 545) | def index():
  function server_static_files (line 552) | def server_static_files(fn):
  function main (line 556) | def main(port: int = 19899):

FILE: rdagent/log/server/debug_app.py
  function favicon (line 27) | def favicon():
  function update_trace (line 36) | def update_trace():
  function upload_file (line 74) | def upload_file():
  function receive_msgs (line 117) | def receive_msgs():
  function control_process (line 136) | def control_process():
  function test (line 150) | def test():
  function index (line 156) | def index():
  function server_static_files (line 163) | def server_static_files(fn):
  function main (line 167) | def main(port: int = 19899):

FILE: rdagent/log/storage.py
  function _remove_empty_dir (line 14) | def _remove_empty_dir(path: Path) -> None:
  class FileStorage (line 28) | class FileStorage(Storage):
    method __init__ (line 35) | def __init__(self, path: str | Path) -> None:
    method log (line 38) | def log(
    method iter_msg (line 79) | def iter_msg(self, tag: str | None = None, pattern: str | None = None)...
    method truncate (line 107) | def truncate(self, time: datetime) -> None:
    method __str__ (line 115) | def __str__(self) -> str:

FILE: rdagent/log/timer.py
  class RDAgentTimer (line 8) | class RDAgentTimer:
    method __init__ (line 9) | def __init__(self) -> None:
    method reset (line 15) | def reset(self, all_duration: str | timedelta) -> None:
    method restart_by_remain_time (line 41) | def restart_by_remain_time(self) -> None:
    method add_duration (line 50) | def add_duration(self, duration: timedelta) -> None:
    method is_timeout (line 56) | def is_timeout(self) -> bool:
    method update_remain_time (line 63) | def update_remain_time(self) -> None:
    method remain_time (line 68) | def remain_time(self) -> timedelta | None:
  class RDAgentTimerWrapper (line 75) | class RDAgentTimerWrapper(SingletonBaseClass):
    method __init__ (line 76) | def __init__(self) -> None:
    method replace_timer (line 81) | def replace_timer(self, timer: RDAgentTimer) -> None:

FILE: rdagent/log/ui/app.py
  function filter_log_folders (line 72) | def filter_log_folders(main_log_path):
  function should_display (line 131) | def should_display(msg: Message):
  function get_msgs_until (line 142) | def get_msgs_until(end_func: Callable[[Message], bool] = lambda _: True):
  function refresh (line 258) | def refresh(same_trace: bool = False):
  function evolving_feedback_window (line 292) | def evolving_feedback_window(wsf: FactorSingleFeedback | ModelSingleFeed...
  function display_hypotheses (line 327) | def display_hypotheses(hypotheses: dict[int, Hypothesis], decisions: dic...
  function metrics_window (line 370) | def metrics_window(df: pd.DataFrame, R: int, C: int, *, height: int = 30...
  function summary_window (line 424) | def summary_window():
  function tabs_hint (line 501) | def tabs_hint():
  function tasks_window (line 508) | def tasks_window(tasks: list[FactorTask | ModelTask]):
  function research_window (line 550) | def research_window():
  function feedback_window (line 586) | def feedback_window():
  function evolving_window (line 670) | def evolving_window():
  function analyze_task_completion (line 866) | def analyze_task_completion():

FILE: rdagent/log/ui/conf.py
  class UIBasePropSetting (line 6) | class UIBasePropSetting(ExtendedBaseSettings):

FILE: rdagent/log/ui/ds_summary.py
  function curves_win (line 27) | def curves_win(summary: dict):
  function all_summarize_win (line 71) | def all_summarize_win():

FILE: rdagent/log/ui/ds_trace.py
  function convert_defaultdict_to_dict (line 68) | def convert_defaultdict_to_dict(d):
  function load_data (line 74) | def load_data(log_path: Path):
  function load_stdout (line 176) | def load_stdout(stdout_path: Path):
  function task_win (line 185) | def task_win(task):
  function workspace_win (line 199) | def workspace_win(workspace, cmp_workspace=None, cmp_name="last code."):
  function show_text (line 247) | def show_text(text, lang=None):
  function highlight_prompts_uri (line 257) | def highlight_prompts_uri(uri):
  function llm_log_win (line 265) | def llm_log_win(llm_d: list):
  function hypothesis_win (line 387) | def hypothesis_win(hypo):
  function exp_gen_win (line 394) | def exp_gen_win(exp_gen_data, llm_data=None):
  function evolving_win (line 408) | def evolving_win(data, key, llm_data=None, base_workspace=None):
  function coding_win (line 452) | def coding_win(data, base_exp, llm_data: dict | None = None):
  function running_win (line 488) | def running_win(data, base_exp, llm_data=None, last_sota_exp=None):
  function feedback_win (line 524) | def feedback_win(fb_data, llm_data=None):
  function sota_win (line 540) | def sota_win(sota_exp, trace):
  function main_win (line 557) | def main_win(loop_id, llm_data=None):
  function replace_ep_path (line 609) | def replace_ep_path(p: Path):
  function get_llm_call_stats (line 621) | def get_llm_call_stats(llm_data: dict) -> tuple[int, int]:
  function get_timeout_stats (line 643) | def get_timeout_stats(llm_data: dict):
  function timedelta_to_str (line 667) | def timedelta_to_str(td: timedelta | None) -> str:
  function summarize_win (line 677) | def summarize_win():
  function stdout_win (line 1077) | def stdout_win(loop_id: int):
  function get_folders_sorted (line 1101) | def get_folders_sorted(log_path, sort_by_time=False):
  function get_state_data_range (line 1180) | def get_state_data_range(state_data):

FILE: rdagent/log/ui/ds_user_interact.py
  function render_main_content (line 21) | def render_main_content():
  function update_sessions (line 133) | def update_sessions():
  function render_sidebar (line 151) | def render_sidebar():

FILE: rdagent/log/ui/dsapp.py
  function convert_log_folder_str (line 10) | def convert_log_folder_str(lf: str) -> str:
  function extract_amlt_name (line 16) | def extract_amlt_name(x: str) -> str:

FILE: rdagent/log/ui/llm_st.py
  function get_folders_sorted (line 22) | def get_folders_sorted(log_path):
  function load_data (line 52) | def load_data():
  function show_text (line 83) | def show_text(text, lang=None):
  function highlight_prompts_uri (line 93) | def highlight_prompts_uri(uri):

FILE: rdagent/log/ui/qlib_report_figure.py
  class BaseGraph (line 9) | class BaseGraph:
    method __init__ (line 12) | def __init__(
    method _init_data (line 38) | def _init_data(self):
    method _init_parameters (line 48) | def _init_parameters(self, **kwargs):
    method get_instance_with_graph_parameters (line 62) | def get_instance_with_graph_parameters(graph_type: str = None, **kwargs):
    method _get_layout (line 77) | def _get_layout(self) -> go.Layout:
    method _get_data (line 84) | def _get_data(self) -> list:
    method figure (line 99) | def figure(self) -> go.Figure:
  class SubplotsGraph (line 110) | class SubplotsGraph:
    method __init__ (line 116) | def __init__(
    method _init_sub_graph_data (line 203) | def _init_sub_graph_data(self):
    method _init_subplots_kwargs (line 229) | def _init_subplots_kwargs(self):
    method _init_figure (line 246) | def _init_figure(self):
    method figure (line 286) | def figure(self):
  function _calculate_maximum (line 290) | def _calculate_maximum(df: pd.DataFrame, is_ex: bool = False):
  function _calculate_mdd (line 306) | def _calculate_mdd(series):
  function _calculate_report_data (line 316) | def _calculate_report_data(raw_df: pd.DataFrame) -> pd.DataFrame:
  function report_figure (line 348) | def report_figure(df: pd.DataFrame) -> list | tuple:

FILE: rdagent/log/ui/st_fixed_container.py
  function st_fixed_container (line 73) | def st_fixed_container(

FILE: rdagent/log/ui/storage.py
  class WebStorage (line 13) | class WebStorage(Storage):
    method __init__ (line 19) | def __init__(self, port: int, path: str) -> None:
    method __str__ (line 30) | def __str__(self):
    method log (line 33) | def log(self, obj: object, tag: str, timestamp: datetime | None = None...
    method truncate (line 54) | def truncate(self, time: datetime) -> None:
    method iter_msg (line 57) | def iter_msg(self, **kwargs: Any) -> Generator[Message, None, None]:
    method _obj_to_json (line 66) | def _obj_to_json(

FILE: rdagent/log/ui/utils.py
  function get_script_time (line 117) | def get_script_time(stdout_p: Path):
  function _log_path_hash_func (line 134) | def _log_path_hash_func(log_path: Path) -> str:
  function map_stat (line 146) | def map_stat(sota_mle_score: dict | None) -> str:
  function get_best_report (line 164) | def get_best_report(log_path: Path) -> dict | None:
  function _get_sota_exp_stat_hash_func (line 182) | def _get_sota_exp_stat_hash_func(log_path: Path, selector: Literal["auto...
  function get_sota_exp_stat (line 186) | def get_sota_exp_stat(
  function _get_score_stat_hash_func (line 262) | def _get_score_stat_hash_func(log_path: Path, sota_loop_id: int) -> str:
  function get_score_stat (line 266) | def get_score_stat(log_path: Path, sota_loop_id: int) -> tuple[float | N...
  function load_times_deprecated (line 365) | def load_times_deprecated(log_path: Path):
  function load_times_info (line 382) | def load_times_info(log_path: Path) -> dict[int, dict[str, dict[Literal[...
  function _log_folders_summary_hash_func (line 423) | def _log_folders_summary_hash_func(log_folder: str | Path, hours: int | ...
  function get_summary_df (line 432) | def get_summary_df(log_folder: str | Path, hours: int | None = None) -> ...
  function percent_df (line 668) | def percent_df(summary_df: pd.DataFrame, show_origin=True) -> pd.DataFrame:
  function get_statistics_df (line 712) | def get_statistics_df(summary_df: pd.DataFrame) -> pd.DataFrame:
  function curve_figure (line 783) | def curve_figure(scores: pd.DataFrame) -> go.Figure:
  function lite_curve_figure (line 818) | def lite_curve_figure(summary):
  function trace_figure (line 884) | def trace_figure(trace: Trace, merge_loops: list = []):
  function timeline_figure (line 975) | def timeline_figure(times_dict: dict[int, dict[str, dict[Literal["start_...
  function compare (line 1089) | def compare(

FILE: rdagent/log/ui/web.py
  class WebView (line 29) | class WebView(View):
    method __init__ (line 30) | def __init__(self, ui: "StWindow"):
    method display (line 35) | def display(self, s: Storage, watch: bool = False):
  class StWindow (line 42) | class StWindow:
    method __init__ (line 43) | def __init__(self, container: "DeltaGenerator"):
    method consume_msg (line 46) | def consume_msg(self, msg: Message):
  class LLMWindow (line 51) | class LLMWindow(StWindow):
    method __init__ (line 52) | def __init__(self, container: "DeltaGenerator", session_name: str = "c...
    method consume_msg (line 56) | def consume_msg(self, msg: Message):
  class ProgressTabsWindow (line 60) | class ProgressTabsWindow(StWindow):
    method __init__ (line 65) | def __init__(
    method consume_msg (line 78) | def consume_msg(self, msg: Message):
  class ObjectsTabsWindow (line 102) | class ObjectsTabsWindow(StWindow):
    method __init__ (line 103) | def __init__(
    method consume_msg (line 115) | def consume_msg(self, msg: Message):
  class RoundTabsWindow (line 145) | class RoundTabsWindow(StWindow):
    method __init__ (line 146) | def __init__(
    method consume_msg (line 161) | def consume_msg(self, msg: Message):
  class HypothesisWindow (line 169) | class HypothesisWindow(StWindow):
    method consume_msg (line 170) | def consume_msg(self, msg: Message | Hypothesis):
  class HypothesisFeedbackWindow (line 179) | class HypothesisFeedbackWindow(StWindow):
    method consume_msg (line 180) | def consume_msg(self, msg: Message | HypothesisFeedback):
  class FactorTaskWindow (line 192) | class FactorTaskWindow(StWindow):
    method consume_msg (line 193) | def consume_msg(self, msg: Message | FactorTask):
  class ModelTaskWindow (line 206) | class ModelTaskWindow(StWindow):
    method consume_msg (line 207) | def consume_msg(self, msg: Message | ModelTask):
  class FactorFeedbackWindow (line 220) | class FactorFeedbackWindow(StWindow):
    method consume_msg (line 221) | def consume_msg(self, msg: Message | FactorSingleFeedback):
  class ModelFeedbackWindow (line 237) | class ModelFeedbackWindow(StWindow):
    method consume_msg (line 238) | def consume_msg(self, msg: Message | ModelSingleFeedback):
  class WorkspaceWindow (line 256) | class WorkspaceWindow(StWindow):
    method __init__ (line 257) | def __init__(self, container: "DeltaGenerator", show_task_info: bool =...
    method consume_msg (line 261) | def consume_msg(self, msg: Message | FactorFBWorkspace | ModelFBWorksp...
  class QlibFactorExpWindow (line 285) | class QlibFactorExpWindow(StWindow):
    method __init__ (line 286) | def __init__(self, container: DeltaGenerator, show_task_info: bool = F...
    method consume_msg (line 290) | def consume_msg(self, msg: Message | QlibFactorExperiment):
  class QlibModelExpWindow (line 318) | class QlibModelExpWindow(StWindow):
    method __init__ (line 319) | def __init__(self, container: DeltaGenerator, show_task_info: bool = F...
    method consume_msg (line 323) | def consume_msg(self, msg: Message | QlibModelExperiment):
  class SimpleTraceWindow (line 345) | class SimpleTraceWindow(StWindow):
    method __init__ (line 346) | def __init__(
    method consume_msg (line 358) | def consume_msg(self, msg: Message):
  function mock_msg (line 433) | def mock_msg(obj) -> Message:
  class TraceObjWindow (line 437) | class TraceObjWindow(StWindow):
    method __init__ (line 438) | def __init__(self, container: "DeltaGenerator" = st.container()):
    method consume_msg (line 441) | def consume_msg(self, msg: Message | Trace):
  class ResearchWindow (line 457) | class ResearchWindow(StWindow):
    method consume_msg (line 458) | def consume_msg(self, msg: Message):
  class EvolvingWindow (line 477) | class EvolvingWindow(StWindow):
    method __init__ (line 478) | def __init__(self, container: "DeltaGenerator"):
    method consume_msg (line 482) | def consume_msg(self, msg: Message):
  class DevelopmentWindow (line 519) | class DevelopmentWindow(StWindow):
    method __init__ (line 520) | def __init__(self, container: "DeltaGenerator"):
    method consume_msg (line 528) | def consume_msg(self, msg: Message):
  class FeedbackWindow (line 533) | class FeedbackWindow(StWindow):
    method __init__ (line 534) | def __init__(self, container: "DeltaGenerator"):
    method consume_msg (line 537) | def consume_msg(self, msg: Message):
  class SingleRDLoopWindow (line 550) | class SingleRDLoopWindow(StWindow):
    method __init__ (line 551) | def __init__(self, container: "DeltaGenerator"):
    method consume_msg (line 558) | def consume_msg(self, msg: Message):
  class TraceWindow (line 568) | class TraceWindow(StWindow):
    method __init__ (line 569) | def __init__(
    method consume_msg (line 598) | def consume_msg(self, msg: Message):

FILE: rdagent/log/utils/__init__.py
  class LogColors (line 9) | class LogColors:
    method get_all_colors (line 30) | def get_all_colors(cls: type["LogColors"]) -> list:
    method render (line 35) | def render(self, text: str, color: str = "", style: str = "") -> str:
    method remove_ansi_codes (line 55) | def remove_ansi_codes(s: str) -> str:
  class CallerInfo (line 63) | class CallerInfo(TypedDict):
  function get_caller_info (line 69) | def get_caller_info(level: int = 2) -> CallerInfo:
  function is_valid_session (line 83) | def is_valid_session(log_path: Path) -> bool:
  function extract_loopid_func_name (line 87) | def extract_loopid_func_name(tag: str) -> tuple[str, str] | tuple[None, ...
  function extract_evoid (line 93) | def extract_evoid(tag: str) -> str | None:
  function extract_json (line 99) | def extract_json(log_content: str) -> dict | None:
  function gen_datetime (line 106) | def gen_datetime(dt: datetime | None = None) -> datetime:
  function dict_get_with_warning (line 117) | def dict_get_with_warning(d: dict, key: str, default: Any = None) -> Any:

FILE: rdagent/log/utils/folder.py
  function get_first_session_file_after_duration (line 14) | def get_first_session_file_after_duration(log_folder: str | Path, durati...
  function first_li_si_after_one_time (line 40) | def first_li_si_after_one_time(log_path: Path, hours: int = 12) -> tuple...

FILE: rdagent/oai/backend/base.py
  class JSONParser (line 37) | class JSONParser:
    method __init__ (line 40) | def __init__(self, add_json_in_prompt: bool = False) -> None:
    method parse (line 49) | def parse(self, content: str) -> str:
    method _direct_parse (line 71) | def _direct_parse(self, content: str) -> str:
    method _extract_from_code_block (line 81) | def _extract_from_code_block(self, content: str) -> str:
    method _fix_python_syntax (line 90) | def _fix_python_syntax(self, content: str) -> str:
    method _extract_with_fix_combined (line 95) | def _extract_with_fix_combined(self, content: str) -> str:
    method _fix_python_booleans (line 107) | def _fix_python_booleans(json_str: str) -> str:
    method _extract_first_json (line 132) | def _extract_first_json(response: str) -> str:
  class CodeBlockParser (line 139) | class CodeBlockParser:
    method __init__ (line 150) | def __init__(self, language: str = "python", fallback_to_raw: bool = F...
    method _get_language_aliases (line 161) | def _get_language_aliases(self, language: str) -> List[str]:
    method parse (line 168) | def parse(self, content: str) -> str:
  class SQliteLazyCache (line 195) | class SQliteLazyCache(SingletonBaseClass):
    method __init__ (line 196) | def __init__(self, cache_location: str) -> None:
    method chat_get (line 230) | def chat_get(self, key: str) -> str | None:
    method embedding_get (line 236) | def embedding_get(self, key: str) -> list | dict | str | None:
    method chat_set (line 242) | def chat_set(self, key: str, value: str) -> None:
    method embedding_set (line 251) | def embedding_set(self, content_to_embedding_dict: dict) -> None:
    method message_get (line 260) | def message_get(self, conversation_id: str) -> list[dict[str, Any]]:
    method message_set (line 265) | def message_set(self, conversation_id: str, message_value: list[dict[s...
  class SessionChatHistoryCache (line 274) | class SessionChatHistoryCache(SingletonBaseClass):
    method __init__ (line 275) | def __init__(self) -> None:
    method message_get (line 279) | def message_get(self, conversation_id: str) -> list[dict[str, Any]]:
    method message_set (line 282) | def message_set(self, conversation_id: str, message_value: list[dict[s...
  class ChatSession (line 286) | class ChatSession:
    method __init__ (line 287) | def __init__(self, api_backend: Any, conversation_id: str | None = Non...
    method build_chat_completion_message (line 292) | def build_chat_completion_message(self, user_prompt: str) -> list[dict...
    method build_chat_completion_message_and_calculate_token (line 305) | def build_chat_completion_message_and_calculate_token(self, user_promp...
    method build_chat_completion (line 309) | def build_chat_completion(self, user_prompt: str, *args, **kwargs) -> ...
    method get_conversation_id (line 345) | def get_conversation_id(self) -> str:
    method display_history (line 348) | def display_history(self) -> None:
  class APIBackend (line 353) | class APIBackend(ABC):
    method __init__ (line 360) | def __init__(
    method build_chat_session (line 381) | def build_chat_session(
    method _build_messages (line 392) | def _build_messages(
    method _build_log_messages (line 429) | def _build_log_messages(self, messages: list[dict[str, Any]]) -> str:
    method build_messages_and_create_chat_completion (line 440) | def build_messages_and_create_chat_completion(  # type: ignore[no-unty...
    method create_embedding (line 493) | def create_embedding(self, input_content: str | list[str], *args, **kw...
    method build_messages_and_calculate_token (line 505) | def build_messages_and_calculate_token(
    method _try_create_chat_completion_or_embedding (line 520) | def _try_create_chat_completion_or_embedding(  # type: ignore[no-untyp...
    method _add_json_in_prompt (line 615) | def _add_json_in_prompt(self, messages: list[dict[str, Any]]) -> None:
    method _create_chat_completion_auto_continue (line 625) | def _create_chat_completion_auto_continue(
    method _create_embedding_with_cache (line 733) | def _create_embedding_with_cache(
    method supports_response_schema (line 757) | def supports_response_schema(self) -> bool:
    method _calculate_token_from_messages (line 764) | def _calculate_token_from_messages(self, messages: list[dict[str, Any]...
    method _create_embedding_inner_function (line 771) | def _create_embedding_inner_function(self, input_content_list: list[st...
    method _create_chat_completion_inner_function (line 778) | def _create_chat_completion_inner_function(  # type: ignore[no-untyped...
    method chat_token_limit (line 791) | def chat_token_limit(self) -> int:

FILE: rdagent/oai/backend/deprec.py
  class ConvManager (line 64) | class ConvManager:
    method __init__ (line 70) | def __init__(
    method _rotate_files (line 79) | def _rotate_files(self) -> None:
    method append (line 92) | def append(self, conv: tuple[list, str]) -> None:
  class DeprecBackend (line 99) | class DeprecBackend(APIBackend):
    method __init__ (line 109) | def __init__(  # noqa: C901, PLR0912, PLR0915
    method _get_encoder (line 238) | def _get_encoder(self) -> tiktoken.Encoding:
    method supports_response_schema (line 266) | def supports_response_schema(self) -> bool:
    method _create_embedding_inner_function (line 273) | def _create_embedding_inner_function(self, input_content_list: list[st...
    method _create_chat_completion_inner_function (line 294) | def _create_chat_completion_inner_function(  # type: ignore[no-untyped...
    method _calculate_token_from_messages (line 467) | def _calculate_token_from_messages(self, messages: list[dict[str, Any]...

FILE: rdagent/oai/backend/litellm.py
  function _reduce_no_init (line 25) | def _reduce_no_init(exc: Exception) -> tuple:
  class LiteLLMSettings (line 35) | class LiteLLMSettings(LLMSettings):
    class Config (line 37) | class Config:
  class LiteLLMAPIBackend (line 48) | class LiteLLMAPIBackend(APIBackend):
    method __init__ (line 53) | def __init__(self, *args: Any, **kwargs: Any) -> None:
    method _calculate_token_from_messages (line 60) | def _calculate_token_from_messages(self, messages: list[dict[str, Any]...
    method _create_embedding_inner_function (line 71) | def _create_embedding_inner_function(self, input_content_list: list[st...
    class CompleteKwargs (line 89) | class CompleteKwargs(TypedDict):
    method get_complete_kwargs (line 95) | def get_complete_kwargs(self) -> CompleteKwargs:
    method _create_chat_completion_inner_function (line 127) | def _create_chat_completion_inner_function(  # type: ignore[no-untyped...
    method supports_response_schema (line 226) | def supports_response_schema(self) -> bool:
    method chat_token_limit (line 233) | def chat_token_limit(self) -> int:

FILE: rdagent/oai/backend/pydantic_ai.py
  function get_agent_model (line 27) | def get_agent_model() -> OpenAIChatModel:

FILE: rdagent/oai/llm_conf.py
  class LLMSettings (line 11) | class LLMSettings(ExtendedBaseSettings):

FILE: rdagent/oai/llm_utils.py
  function calculate_embedding_distance_between_str_list (line 13) | def calculate_embedding_distance_between_str_list(
  function get_api_backend (line 35) | def get_api_backend(*args: Any, **kwargs: Any) -> BaseAPIBackend:  # TOD...

FILE: rdagent/oai/utils/embedding.py
  function get_embedding_max_tokens (line 27) | def get_embedding_max_tokens(model: str) -> int:
  function trim_text_for_embedding (line 63) | def trim_text_for_embedding(text: str, model: str, max_tokens: Optional[...
  function truncate_content_list (line 118) | def truncate_content_list(content_list: list[str], model: str) -> list[s...

FILE: rdagent/scenarios/data_science/debug/data.py
  class DataHandler (line 18) | class DataHandler:
    method load (line 21) | def load(self, path) -> pd.DataFrame:
    method dump (line 24) | def dump(self, df: pd.DataFrame, path):
  class GenericDataHandler (line 28) | class GenericDataHandler(DataHandler):
    method load (line 34) | def load(self, path) -> pd.DataFrame:
    method dump (line 65) | def dump(self, df: pd.DataFrame | dict, path):
  class DataReducer (line 93) | class DataReducer:
    method __init__ (line 96) | def __init__(self, min_frac=0.02, min_num=5):
    method reduce (line 101) | def reduce(self, df: pd.DataFrame) -> pd.DataFrame:
  class RandDataReducer (line 105) | class RandDataReducer(DataReducer):
    method reduce (line 111) | def reduce(self, df: pd.DataFrame, frac: float = None) -> pd.DataFrame:
  class FolderReducer (line 119) | class FolderReducer(DataReducer):
    method reduce (line 124) | def reduce(self, array: list, frac: float = None) -> list:
  class FileReducer (line 141) | class FileReducer(DataReducer):
    method reduce (line 146) | def reduce(self, files: list[Path]) -> list:
  class FileKeepReducer (line 158) | class FileKeepReducer(DataReducer):
    method reduce (line 163) | def reduce(self, files: list[Path]) -> list:
  class SingleFileReducer (line 181) | class SingleFileReducer(DataReducer):
    method reduce (line 186) | def reduce(self, files: list[Path]) -> list:
  class UniqueIDDataReducer (line 191) | class UniqueIDDataReducer(DataReducer):
    method reduce (line 192) | def reduce(self, df: pd.DataFrame) -> pd.DataFrame:
  class JsonReducer (line 237) | class JsonReducer(DataReducer):
    method extract_filename (line 239) | def extract_filename(self, item: Any) -> Optional[str]:
    method reduce (line 255) | def reduce(self, data: dict) -> dict:
    method _find_all_lists (line 274) | def _find_all_lists(
    method _sample_list (line 299) | def _sample_list(self, lst: List[Any]) -> List[Any]:
  class DataSampler (line 306) | class DataSampler:
    method __init__ (line 309) | def __init__(self, data_folder, sample_folder, reducer):
    method sample (line 316) | def sample(self) -> None:
  class DefaultSampler (line 320) | class DefaultSampler(DataSampler):
    method sample (line 321) | def sample(self) -> None:
  class FolderSampler (line 467) | class FolderSampler(DataSampler):
    method sample (line 472) | def sample(self) -> None:
  class SingleFilePerFolderSampler (line 520) | class SingleFilePerFolderSampler(DataSampler):
    method sample (line 526) | def sample(self) -> None:
  function copy_file (line 558) | def copy_file(src_fp, target_folder, data_folder):
  function copy_folder (line 569) | def copy_folder(src_fp, target_folder, data_folder):
  function count_files_in_folder (line 579) | def count_files_in_folder(files_to_process):
  function map_competition (line 592) | def map_competition(competition: str) -> tuple[DataReducer, DataSampler]:
  function create_debug_data (line 605) | def create_debug_data(

FILE: rdagent/scenarios/data_science/dev/feedback.py
  class DSExperiment2Feedback (line 23) | class DSExperiment2Feedback(Experiment2Feedback):
    method __init__ (line 24) | def __init__(self, scen: Scenario, version: str = "exp_feedback") -> N...
    method generate_feedback (line 28) | def generate_feedback(self, exp: DSExperiment, trace: DSTrace) -> Expe...

FILE: rdagent/scenarios/data_science/dev/runner/__init__.py
  class DSRunnerCoSTEERSettings (line 29) | class DSRunnerCoSTEERSettings(CoSTEERSettings):
    class Config (line 32) | class Config:
  class DSRunnerMultiProcessEvolvingStrategy (line 42) | class DSRunnerMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 44) | def implement_one_task(
    method assign_code_list_to_evo (line 116) | def assign_code_list_to_evo(self, code_list: list[dict[str, str]], evo):
  class DSCoSTEERRunner (line 135) | class DSCoSTEERRunner(CoSTEER):
    method __init__ (line 136) | def __init__(
    method get_develop_max_seconds (line 169) | def get_develop_max_seconds(self) -> int | None:
    method should_use_new_evo (line 175) | def should_use_new_evo(self, base_fb: CoSTEERMultiFeedback | None, new...
    method develop (line 196) | def develop(self, exp):

FILE: rdagent/scenarios/data_science/dev/runner/eval.py
  class DSRunnerFeedback (line 34) | class DSRunnerFeedback(CoSTEERSingleFeedback):
    method is_acceptable (line 45) | def is_acceptable(self) -> bool:
    method __str__ (line 50) | def __str__(self) -> str:
  class DSRunnerEvaluator (line 72) | class DSRunnerEvaluator(CoSTEEREvaluator):
    method evaluate (line 74) | def evaluate(

FILE: rdagent/scenarios/data_science/example/arf-12-hours-prediction-task/sample.py
  function sample_and_copy_subfolder (line 10) | def sample_and_copy_subfolder(
  function copy_other_file (line 46) | def copy_other_file(source: Path, target: Path):
  function create_debug_data (line 63) | def create_debug_data(

FILE: rdagent/scenarios/data_science/example/eval/arf-12-hours-prediction-task/grade.py
  function prepare_for_auroc_metric (line 7) | def prepare_for_auroc_metric(submission: pd.DataFrame, answers: pd.DataF...
  function grade (line 46) | def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:

FILE: rdagent/scenarios/data_science/example/eval/playground-series-s4e9/grade.py
  class InvalidSubmissionError (line 9) | class InvalidSubmissionError(Exception):
  function prepare_for_metric (line 17) | def prepare_for_metric(submission: pd.DataFrame, answers: pd.DataFrame) ...
  function grade (line 42) | def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:

FILE: rdagent/scenarios/data_science/example/source_data/playground-series-s4e9/prepare.py
  function prepare (line 7) | def prepare(raw: Path, public: Path, private: Path):

FILE: rdagent/scenarios/data_science/experiment/experiment.py
  class DSExperiment (line 11) | class DSExperiment(Experiment[Task, FBWorkspace, FBWorkspace]):
    method __init__ (line 12) | def __init__(self, pending_tasks_list: list, hypothesis_candidates: li...
    method set_user_instructions (line 27) | def set_user_instructions(self, user_instructions: UserInstructions | ...
    method is_ready_to_run (line 35) | def is_ready_to_run(self) -> bool:
    method set_local_selection (line 42) | def set_local_selection(self, local_selection: tuple[int, ...]) -> None:

FILE: rdagent/scenarios/data_science/interactor/__init__.py
  class DSInteractor (line 17) | class DSInteractor(Interactor[DSExperiment]):
    method dump_and_wait_for_user_input (line 19) | def dump_and_wait_for_user_input(
    method interact (line 32) | def interact(self, exp: DSExperiment, trace: DSTrace) -> DSExperiment:
  class FBDSInteractor (line 70) | class FBDSInteractor(DSInteractor):
    method dump_and_wait_for_user_input (line 71) | def dump_and_wait_for_user_input(

FILE: rdagent/scenarios/data_science/loop.py
  function clean_workspace (line 43) | def clean_workspace(workspace_root: Path) -> None:
  function backup_folder (line 64) | def backup_folder(path: str | Path) -> Path:
  class DataScienceRDLoop (line 87) | class DataScienceRDLoop(RDLoop):
    method __init__ (line 96) | def __init__(self, PROP_SETTING: BasePropSetting):
    method direct_exp_gen (line 139) | async def direct_exp_gen(self, prev_out: dict[str, Any]):
    method coding (line 154) | def coding(self, prev_out: dict[str, Any]):
    method running (line 177) | def running(self, prev_out: dict[str, Any]):
    method feedback (line 187) | def feedback(self, prev_out: dict[str, Any]) -> ExperimentFeedback:
    method record (line 211) | def record(self, prev_out: dict[str, Any]):
    method _check_exit_conditions_on_step (line 353) | def _check_exit_conditions_on_step(self, loop_id: Optional[int] = None...
    method load (line 359) | def load(
    method dump (line 373) | def dump(self, path: str | Path) -> None:

FILE: rdagent/scenarios/data_science/proposal/exp_gen/base.py
  class DSHypothesis (line 13) | class DSHypothesis(Hypothesis):
    method __init__ (line 14) | def __init__(
    method __str__ (line 37) | def __str__(self) -> str:
  class DSTrace (line 55) | class DSTrace(Trace[DataScienceScen, KnowledgeBase]):
    method __init__ (line 56) | def __init__(self, scen: DataScienceScen, knowledge_base: KnowledgeBas...
    method should_inject_diversity (line 66) | def should_inject_diversity(self, current_selection: tuple[int, ...] |...
    method register_uncommitted_exp (line 81) | def register_uncommitted_exp(self, exp: DSExperiment, loop_id: int):
    method deregister_uncommitted_exp (line 84) | def deregister_uncommitted_exp(self, loop_id: int):
    method set_sota_exp_to_submit (line 88) | def set_sota_exp_to_submit(self, exp: DSExperiment) -> None:
    method sub_trace_count (line 92) | def sub_trace_count(self) -> int:
    method get_leaves (line 95) | def get_leaves(self) -> list[int, ...]:
    method get_sibling_exps (line 114) | def get_sibling_exps(self, current_selection: tuple[int, ...] | None =...
    method sync_dag_parent_and_hist (line 139) | def sync_dag_parent_and_hist(
    method retrieve_search_list (line 165) | def retrieve_search_list(
    method next_incomplete_component (line 194) | def next_incomplete_component(
    method has_component (line 212) | def has_component(
    method experiment_and_feedback_list_after_init (line 221) | def experiment_and_feedback_list_after_init(
    method sota_experiment_fb (line 267) | def sota_experiment_fb(
    method sota_experiment (line 287) | def sota_experiment(
    method last_successful_exp (line 297) | def last_successful_exp(
    method last_exp (line 312) | def last_exp(
    method last_exp_fb (line 323) | def last_exp_fb(
    method last_runnable_exp_fb (line 336) | def last_runnable_exp_fb(

FILE: rdagent/scenarios/data_science/proposal/exp_gen/diversity_strategy.py
  class DiversityContextStrategy (line 10) | class DiversityContextStrategy(ABC):
    method should_inject (line 17) | def should_inject(self, trace: DSTrace, local_selection: tuple[int, .....
  class InjectAtRootStrategy (line 32) | class InjectAtRootStrategy(DiversityContextStrategy):
    method should_inject (line 37) | def should_inject(self, trace: DSTrace, local_selection: tuple[int, .....
  class InjectUntilSOTAGainedStrategy (line 42) | class InjectUntilSOTAGainedStrategy(DiversityContextStrategy):
    method should_inject (line 48) | def should_inject(self, trace: DSTrace, local_selection: tuple[int, .....
  class AlwaysInjectStrategy (line 61) | class AlwaysInjectStrategy(DiversityContextStrategy):
    method should_inject (line 66) | def should_inject(self, trace: DSTrace, local_selection: tuple[int, .....

FILE: rdagent/scenarios/data_science/proposal/exp_gen/draft/draft.py
  class DSDraftExpGen (line 26) | class DSDraftExpGen(ExpGen):
    method _init_task_gen (line 27) | def _init_task_gen(
    method gen (line 61) | def gen(
  class DSDraftV2ExpGen (line 132) | class DSDraftV2ExpGen(ExpGen):
    method __init__ (line 133) | def __init__(self, *args, **kwargs):
    method tag_gen (line 137) | def tag_gen(self, scenario_desc: str) -> str:
    method knowledge_gen (line 150) | def knowledge_gen(self) -> str:
    method hypothesis_gen (line 157) | def hypothesis_gen(
    method task_gen (line 183) | def task_gen(
    method gen (line 241) | def gen(

FILE: rdagent/scenarios/data_science/proposal/exp_gen/idea_pool.py
  class DSIdea (line 18) | class DSIdea:
    method __init__ (line 19) | def __init__(self, raw_knowledge: Dict | str) -> None:
    method __str__ (line 41) | def __str__(self) -> str:
    method to_formatted_str (line 52) | def to_formatted_str(self) -> str:
  class DSKnowledgeBase (line 56) | class DSKnowledgeBase(UndirectedGraph):
    method __init__ (line 57) | def __init__(self, path: str | Path | None = None, idea_pool_json_path...
    method add_idea (line 64) | def add_idea(self, idea: List[DSIdea] | DSIdea) -> None:
    method build_idea_pool (line 97) | def build_idea_pool(self, idea_pool_json_path: str | Path):
    method sample_ideas (line 116) | def sample_ideas(
    method update_pickled_problem (line 182) | def update_pickled_problem(self, problems: Dict, pickled_problem_name:...

FILE: rdagent/scenarios/data_science/proposal/exp_gen/merge.py
  class MergeExpGen (line 26) | class MergeExpGen(ExpGen):
    method gen (line 27) | def gen(
  class ExpGen2Hypothesis (line 99) | class ExpGen2Hypothesis(DSProposalV2ExpGen):
    method hypothesis_gen (line 101) | def hypothesis_gen(
    method get_exp_index (line 131) | def get_exp_index(self, trace: DSTrace) -> int:
    method gen (line 148) | def gen(
  class ExpGen2TraceAndMerge (line 239) | class ExpGen2TraceAndMerge(ExpGen):
    method __init__ (line 241) | def __init__(self, *args, **kwargs):
    method gen (line 246) | def gen(
  class MergeExpGen_MultiTrace (line 275) | class MergeExpGen_MultiTrace(ExpGen):
    method gen (line 276) | def gen(
  class ExpGen2TraceAndMergeV2 (line 354) | class ExpGen2TraceAndMergeV2(ExpGen):
    method __init__ (line 355) | def __init__(self, *args, **kwargs):
    method reset_exp_gen_version (line 361) | def reset_exp_gen_version(self, version: str = "v2"):
    method gen (line 370) | def gen(
  class ExpGen2TraceAndMergeV3 (line 414) | class ExpGen2TraceAndMergeV3(ExpGen):
    method __init__ (line 415) | def __init__(self, *args, **kwargs):
    method gen (line 420) | def gen(

FILE: rdagent/scenarios/data_science/proposal/exp_gen/naive.py
  class NaiveExpGen (line 14) | class NaiveExpGen(ExpGen):
    method gen (line 15) | def gen(

FILE: rdagent/scenarios/data_science/proposal/exp_gen/package_info.py
  function get_installed_packages (line 5) | def get_installed_packages():
  function print_filtered_packages (line 9) | def print_filtered_packages(installed_packages, filtered_packages):
  function get_python_packages (line 24) | def get_python_packages():

FILE: rdagent/scenarios/data_science/proposal/exp_gen/planner/__init__.py
  class DSExperimentPlan (line 9) | class DSExperimentPlan(ExperimentPlan):
    method __init__ (line 15) | def __init__(self):
  class DSExpPlannerHandCraft (line 22) | class DSExpPlannerHandCraft(ExpPlanner[DSExperimentPlan]):
    method plan (line 27) | def plan(self, trace: DSTrace) -> DSExperimentPlan:

FILE: rdagent/scenarios/data_science/proposal/exp_gen/proposal.py
  function get_component (line 85) | def get_component(name: str) -> Dict[str, Any]:
  class ScenarioChallengeCategory (line 98) | class ScenarioChallengeCategory(str, Enum):
  class ScenarioChallengeDetail (line 103) | class ScenarioChallengeDetail(BaseModel):
  class ScenarioAnalysis (line 121) | class ScenarioAnalysis(BaseModel):
  class ScenarioChallenges (line 134) | class ScenarioChallenges(BaseModel):
  class TraceAnalysisDetail (line 145) | class TraceAnalysisDetail(BaseModel):
  class TraceAnalysis (line 155) | class TraceAnalysis(BaseModel):
  class TraceChallengeDetail (line 168) | class TraceChallengeDetail(BaseModel):
  class TraceChallenges (line 195) | class TraceChallenges(BaseModel):
  class HypothesisComponent (line 210) | class HypothesisComponent(str, Enum):
  class HypothesisEvaluationReasoningScore (line 218) | class HypothesisEvaluationReasoningScore(BaseModel):
  class HypothesisEvaluation (line 225) | class HypothesisEvaluation(BaseModel):
  class HypothesisDetail (line 243) | class HypothesisDetail(BaseModel):
  class HypothesisSimple (line 260) | class HypothesisSimple(BaseModel):
  class HypothesisList (line 267) | class HypothesisList(BaseModel):
  class CodingSketch (line 276) | class CodingSketch(BaseModel):
  function draft_exp_in_decomposition (line 306) | def draft_exp_in_decomposition(scen: Scenario, trace: DSTrace) -> None |...
  class DSProposalV1ExpGen (line 317) | class DSProposalV1ExpGen(ExpGen):
    method gen (line 318) | def gen(
  class DSProposalV2ExpGen (line 492) | class DSProposalV2ExpGen(ExpGen):
    method __init__ (line 493) | def __init__(self, *args, **kwargs):
    method identify_scenario_problem (line 497) | def identify_scenario_problem(
    method identify_feedback_problem (line 532) | def identify_feedback_problem(
    method identify_problem (line 569) | def identify_problem(
    method hypothesis_gen (line 609) | def hypothesis_gen(
    method hypothesis_critique (line 705) | def hypothesis_critique(
    method hypothesis_rewrite (line 774) | def hypothesis_rewrite(
    method compute_top_scores (line 859) | def compute_top_scores(
    method select_hypothesis (line 893) | def select_hypothesis(
    method _cosine_similarity_matrix_torch (line 924) | def _cosine_similarity_matrix_torch(self, A, B):
    method _prob_dis_torch (line 932) | def _prob_dis_torch(
    method _get_path (line 987) | def _get_path(self, node, parent_nodes):
    method _get_current_exp_score_list (line 995) | def _get_current_exp_score_list(self, trace, competition):
    method _llm_select_extra_hypo (line 1028) | def _llm_select_extra_hypo(self, trace: DSTrace) -> list[tuple[str, fl...
    method hypothesis_select_with_llm (line 1055) | def hypothesis_select_with_llm(
    method hypothesis_rank (line 1158) | def hypothesis_rank(
    method task_gen (line 1184) | def task_gen(
    method get_all_hypotheses (line 1283) | def get_all_hypotheses(self, problem_dict: dict, hypothesis_dict: dict...
    method gen (line 1300) | def gen(

FILE: rdagent/scenarios/data_science/proposal/exp_gen/router/__init__.py
  class ParallelMultiTraceExpGen (line 36) | class ParallelMultiTraceExpGen(ExpGen):
    method __init__ (line 45) | def __init__(self, *args, **kwargs):
    method gen (line 58) | def gen(
    method reset (line 67) | def reset(self) -> None:
    method async_gen (line 70) | async def async_gen(self, trace: DSTrace, loop: LoopBase) -> DSExperim...

FILE: rdagent/scenarios/data_science/proposal/exp_gen/select/expand.py
  class LatestCKPSelector (line 13) | class LatestCKPSelector(CheckpointSelector):
    method __init__ (line 18) | def __init__(
    method get_selection (line 23) | def get_selection(self, trace: Trace) -> tuple[int, ...]:
  class LimitTimeCKPSelector (line 28) | class LimitTimeCKPSelector(CheckpointSelector):
    method __init__ (line 33) | def __init__(
    method set_time_limit (line 41) | def set_time_limit(self):
    method get_selection (line 52) | def get_selection(self, trace: Trace) -> tuple[int, ...]:
  class SOTAJumpCKPSelector (line 100) | class SOTAJumpCKPSelector(CheckpointSelector):
    method __init__ (line 107) | def __init__(
    method get_selection (line 118) | def get_selection(self, trace: Trace) -> tuple[int, ...]:
  class BackJumpCKPSelector (line 156) | class BackJumpCKPSelector(CheckpointSelector):
    method __init__ (line 164) | def __init__(
    method get_selection (line 175) | def get_selection(self, trace: Trace) -> tuple[int, ...]:

FILE: rdagent/scenarios/data_science/proposal/exp_gen/select/submit.py
  class GlobalSOTASelector (line 43) | class GlobalSOTASelector(SOTAexpSelector):
    method __init__ (line 48) | def __init__(self):
    method get_sota_exp_to_submit (line 51) | def get_sota_exp_to_submit(self, trace: Trace, **kwargs) -> DSExperime...
  class AutoSOTAexpSelector (line 58) | class AutoSOTAexpSelector(SOTAexpSelector):
    method __init__ (line 64) | def __init__(self):
    method get_sota_exp_to_submit (line 68) | def get_sota_exp_to_submit(self, trace: Trace, **kwargs) -> DSExperime...
    method collect_sota_candidates (line 130) | def collect_sota_candidates(self, trace: Trace) -> list:
  class BestValidSelector (line 158) | class BestValidSelector(SOTAexpSelector):
    method __init__ (line 164) | def __init__(self, num_candidates: int = 1, use_decision: bool = True,...
    method get_sota_exp_to_submit (line 178) | def get_sota_exp_to_submit(self, trace: Trace, **kwargs) -> DSExperime...
    method collect_sota_candidates (line 187) | def collect_sota_candidates(self, trace: Trace) -> list[DSExperiment] ...
  class ValidationSelector (line 263) | class ValidationSelector(SOTAexpSelector):
    method __init__ (line 271) | def __init__(
    method get_sota_exp_to_submit (line 289) | def get_sota_exp_to_submit(self, trace: Trace) -> DSExperiment | None:
    method print_code (line 345) | def print_code(self, data_py_code: str, grade_py_code: str):
    method _prepare_validation_scripts (line 353) | def _prepare_validation_scripts(
    method _generate_and_run_script (line 429) | def _generate_and_run_script(
  function process_experiment (line 521) | def process_experiment(
  function _parsing_score (line 575) | def _parsing_score(grade_stdout: str) -> Optional[float]:
  function check_hit (line 602) | def check_hit(selected_exp: DSExperiment, trace: Trace, sota_result: Dic...
  function try_get_loop_id (line 622) | def try_get_loop_id(trace: Trace, exp: DSExperiment):
  function extract_tar (line 629) | def extract_tar(tar_path: str, to_dir: str = "log") -> str:
  function evaluate_one_trace (line 639) | def evaluate_one_trace(
  function select_on_existing_trace (line 744) | def select_on_existing_trace(

FILE: rdagent/scenarios/data_science/proposal/exp_gen/trace_scheduler.py
  class TraceScheduler (line 18) | class TraceScheduler(ABC):
    method next (line 25) | async def next(self, trace: DSTrace) -> tuple[int, ...]:
    method reset (line 43) | def reset(self) -> None:
  class BaseScheduler (line 50) | class BaseScheduler(TraceScheduler):
    method __init__ (line 51) | def __init__(self):
    method next (line 55) | async def next(self, trace: DSTrace) -> tuple[int, ...]:
    method process_uncommitted_nodes (line 85) | def process_uncommitted_nodes(self, trace: DSTrace) -> None:
    method select (line 93) | def select(self, trace: DSTrace) -> tuple[int, ...] | None:
    method reset (line 97) | def reset(self) -> None:
  class RoundRobinScheduler (line 102) | class RoundRobinScheduler(BaseScheduler):
    method __init__ (line 110) | def __init__(self, max_trace_num: int, *args, **kwargs):
    method select (line 116) | def select(self, trace: DSTrace) -> tuple[int, ...] | None:
  class ProbabilisticScheduler (line 139) | class ProbabilisticScheduler(BaseScheduler):
    method __init__ (line 145) | def __init__(self, max_trace_num: int, temperature: float = 1.0, *args...
    method calculate_potential (line 160) | def calculate_potential(self, trace: DSTrace, leaf_id: int) -> float:
    method _softmax_probabilities (line 174) | def _softmax_probabilities(self, potentials: list[float]) -> list[float]:
    method select (line 201) | def select(self, trace: DSTrace) -> tuple[int, ...] | None:
  class TraceLengthScheduler (line 232) | class TraceLengthScheduler(ProbabilisticScheduler):
    method __init__ (line 240) | def __init__(self, max_trace_num: int, temperature: float = 1.0, inver...
    method calculate_potential (line 253) | def calculate_potential(self, trace: DSTrace, leaf_id: int) -> float:
  class SOTABasedScheduler (line 267) | class SOTABasedScheduler(ProbabilisticScheduler):
    method __init__ (line 272) | def __init__(self, max_trace_num: int, temperature: float = 1.0, inver...
    method calculate_potential (line 283) | def calculate_potential(self, trace: DSTrace, leaf_id: int) -> float:
  class RandomScheduler (line 304) | class RandomScheduler(ProbabilisticScheduler):
    method calculate_potential (line 309) | def calculate_potential(self, trace: DSTrace, leaf_id: int) -> float:
  class MCTSScheduler (line 316) | class MCTSScheduler(ProbabilisticScheduler):
    method __init__ (line 331) | def __init__(self, max_trace_num: int, temperature: float = 1.0, *args...
    method _get_q (line 344) | def _get_q(self, node_id: int) -> float:
    method _get_u (line 352) | def _get_u(self, node_id: int) -> float:
    method select (line 358) | def select(self, trace: DSTrace) -> tuple[int, ...] | None:
    method observe_feedback (line 396) | def observe_feedback(self, trace: DSTrace, new_idx: int) -> None:
    method reset (line 420) | def reset(self) -> None:
    method process_uncommitted_nodes (line 431) | def process_uncommitted_nodes(self, trace: DSTrace) -> None:

FILE: rdagent/scenarios/data_science/proposal/exp_gen/utils.py
  function get_component (line 56) | def get_component(name: str) -> Dict[str, Any]:
  class CodingSketch (line 69) | class CodingSketch(BaseModel):
  function get_packages (line 94) | def get_packages(pkgs: list[str] | None = None) -> str:

FILE: rdagent/scenarios/data_science/scen/__init__.py
  class DataScienceScen (line 27) | class DataScienceScen(Scenario):
    method __init__ (line 30) | def __init__(self, competition: str) -> None:
    method reanalyze_competition_description (line 70) | def reanalyze_competition_description(self):
    method _get_description (line 76) | def _get_description(self):
    method _get_direction (line 89) | def _get_direction(self):
    method _analysis_competition_description (line 92) | def _analysis_competition_description(self):
    method real_debug_timeout (line 141) | def real_debug_timeout(self):
    method recommend_debug_timeout (line 152) | def recommend_debug_timeout(self):
    method real_full_timeout (line 155) | def real_full_timeout(self):
    method recommend_full_timeout (line 176) | def recommend_full_timeout(self):
    method increase_timeout (line 179) | def increase_timeout(self):
    method background (line 184) | def background(self) -> str:
    method rich_style_description (line 197) | def rich_style_description(self) -> str:
    method get_competition_full_desc (line 203) | def get_competition_full_desc(self) -> str:
    method get_scenario_all_desc (line 220) | def get_scenario_all_desc(self, eda_output=None) -> str:
    method get_runtime_environment (line 246) | def get_runtime_environment(self) -> str:
    method _get_data_folder_description (line 253) | def _get_data_folder_description(self) -> str:
  class KaggleScen (line 259) | class KaggleScen(DataScienceScen):
    method __init__ (line 268) | def __init__(self, competition: str) -> None:
    method _get_description (line 272) | def _get_description(self):
    method _get_direction (line 275) | def _get_direction(self):
    method rich_style_description (line 279) | def rich_style_description(self) -> str:

FILE: rdagent/scenarios/data_science/scen/utils.py
  class FileTreeGenerationError (line 91) | class FileTreeGenerationError(Exception):
  class MaxLinesExceededError (line 97) | class MaxLinesExceededError(FileTreeGenerationError):
  class DirectoryPermissionError (line 103) | class DirectoryPermissionError(FileTreeGenerationError):
  function get_file_len_size (line 109) | def get_file_len_size(f: Path) -> Tuple[int, str]:
  function preview_df (line 122) | def preview_df(df: pd.DataFrame, file_name: str, simple=True, show_nan_c...
  function preview_csv (line 177) | def preview_csv(p: Path, file_name: str, simple=True, show_nan_columns=F...
  function preview_parquet (line 183) | def preview_parquet(p: Path, file_name: str, simple=True, show_nan_colum...
  function preview_json (line 189) | def preview_json(p: Path, file_name: str):
  function _walk (line 272) | def _walk(path: Path):
  class FileTreeGenerator (line 292) | class FileTreeGenerator:
    method __init__ (line 297) | def __init__(
    method _build_lookup_set (line 322) | def _build_lookup_set(self):
    method generate_tree (line 338) | def generate_tree(self, path: Union[str, Path]) -> str:
    method _add_line (line 409) | def _add_line(self, text: str) -> None:
    method _process_directory (line 424) | def _process_directory(self, path: Path, depth: int, prefix: str, base...
    method _process_subdirectories (line 471) | def _process_subdirectories(self, dirs: List[Path], depth: int, prefix...
    method _process_single_directory (line 491) | def _process_single_directory(self, d: Path, depth: int, prefix: str, ...
    method _process_files (line 513) | def _process_files(self, all_files: List[Path], depth: int, prefix: st...
    method _categorize_files (line 533) | def _categorize_files(self, files: List[Path]) -> Tuple[List[Path], Li...
    method _get_size_str (line 549) | def _get_size_str(self, file_path: Path) -> str:
  class DataFolderDescriptor (line 558) | class DataFolderDescriptor:
    method __init__ (line 563) | def __init__(self, tree_generator: FileTreeGenerator = None):
    method describe_folder (line 572) | def describe_folder(
    method _select_files_for_preview (line 647) | def _select_files_for_preview(
  function file_tree_v2 (line 689) | def file_tree_v2(path: Union[str, Path], max_lines: int = 200, priority_...
  function describe_data_folder_v2 (line 695) | def describe_data_folder_v2(

FILE: rdagent/scenarios/data_science/test_eval.py
  class NoTestEvalError (line 9) | class NoTestEvalError(Exception):
  class TestEvalBase (line 13) | class TestEvalBase:
    method eval (line 17) | def eval(self, competition: str, workspace: FBWorkspace) -> str:
    method valid (line 21) | def valid(self, competition: str, workspace: FBWorkspace) -> tuple[str...
    method enabled (line 25) | def enabled(self, competition) -> bool:
    method get_sample_submission_name (line 29) | def get_sample_submission_name(self, competition: str) -> str:
    method is_sub_enabled (line 47) | def is_sub_enabled(self, competition: str) -> bool:
  class TestEval (line 62) | class TestEval(TestEvalBase):
    method __init__ (line 65) | def __init__(self) -> None:
    method eval (line 69) | def eval(self, competition: str, workspace: FBWorkspace) -> str:
    method valid (line 84) | def valid(self, competition: str, workspace: FBWorkspace) -> tuple[str...
    method enabled (line 101) | def enabled(self, competition) -> bool:
  class MLETestEval (line 107) | class MLETestEval(TestEvalBase):
    method __init__ (line 110) | def __init__(self) -> None:
    method eval (line 117) | def eval(self, competition: str, workspace: FBWorkspace) -> str:
    method valid (line 126) | def valid(self, competition: str, workspace: FBWorkspace) -> tuple[str...
    method enabled (line 138) | def enabled(self, competition) -> bool:
  function get_test_eval (line 142) | def get_test_eval() -> TestEvalBase:

FILE: rdagent/scenarios/finetune/benchmark/benchmark.py
  function get_model_inference_config (line 49) | def get_model_inference_config(base_model_name: str, gpu_count: int) -> ...
  function detect_model_type (line 94) | def detect_model_type(model_path: str) -> bool:
  function run_benchmark (line 115) | def run_benchmark(
  function get_benchmark_ranges (line 331) | def get_benchmark_ranges() -> tuple[str, str]:

FILE: rdagent/scenarios/finetune/benchmark/data/adaptor.py
  class BenchmarkConfig (line 20) | class BenchmarkConfig:

FILE: rdagent/scenarios/finetune/benchmark/data/default.py
  function _to_bool (line 22) | def _to_bool(value: Any) -> bool:
  function _is_correct (line 38) | def _is_correct(sample: Dict) -> bool:
  function _format_value (line 62) | def _format_value(value: Any) -> str:
  function _format_prompt (line 71) | def _format_prompt(prompt: Any) -> str:
  function _extract_tag_content (line 111) | def _extract_tag_content(prompt: Any, tag_name: str) -> str:
  function _get_question (line 137) | def _get_question(sample: Dict, pred_entry: Dict) -> str:
  function _get_gold (line 160) | def _get_gold(sample: Dict, pred_entry: Dict) -> str:
  function _get_prediction (line 182) | def _get_prediction(sample: Dict, pred_entry: Dict) -> str:
  function extract_error_samples (line 208) | def extract_error_samples(

FILE: rdagent/scenarios/finetune/benchmark/data/financeiq_gen.py
  function download_financeiq_dataset (line 15) | def download_financeiq_dataset() -> None:
  function extract_error_samples (line 55) | def extract_error_samples(results_base: Path, max_samples: int = 10) -> ...

FILE: rdagent/scenarios/finetune/benchmark/merge/merge.py
  function is_blackwell_gpu (line 12) | def is_blackwell_gpu() -> bool:
  function check_if_merging_needed (line 29) | def check_if_merging_needed(model_path: str | Path) -> bool:
  function merge_model (line 50) | def merge_model(env, workspace_path: Path, base_model_path: str, adapter...

FILE: rdagent/scenarios/finetune/datasets/__init__.py
  class DatasetConfig (line 23) | class DatasetConfig:
  function _remove_eval_splits (line 35) | def _remove_eval_splits(out_dir: str) -> None:
  function prepare (line 71) | def prepare(name: str, force: bool = False) -> str:
  function prepare_all (line 113) | def prepare_all(force: bool = False) -> dict[str, str]:

FILE: rdagent/scenarios/finetune/datasets/chemcot/__init__.py
  function normalize_rcr (line 7) | def normalize_rcr(out_dir: str) -> None:

FILE: rdagent/scenarios/finetune/datasets/financeiq/split.py
  function get_split_indices (line 7) | def get_split_indices(
  function split_financeiq_dataset (line 26) | def split_financeiq_dataset(data_dir: str, split: Literal["train", "test...

FILE: rdagent/scenarios/finetune/dev/feedback.py
  class FTExperiment2Feedback (line 28) | class FTExperiment2Feedback(Experiment2Feedback):
    method __init__ (line 31) | def __init__(self, scen: Scenario, version: str = "exp_feedback") -> N...
    method generate_feedback (line 35) | def generate_feedback(

FILE: rdagent/scenarios/finetune/download/hf.py
  function _ensure_parent (line 7) | def _ensure_parent(path: Path) -> None:
  function _get_hf_token (line 11) | def _get_hf_token(token: Optional[str] = None) -> Optional[str]:
  function download_dataset (line 21) | def download_dataset(
  function download_model (line 66) | def download_model(

FILE: rdagent/scenarios/finetune/experiment/experiment.py
  class FTExperiment (line 13) | class FTExperiment(Experiment[Task, FTWorkspace, FTWorkspace]):
    method __init__ (line 14) | def __init__(self, sub_tasks: list[Task], *args, **kwargs) -> None:
    method is_ready_to_run (line 27) | def is_ready_to_run(self) -> bool:

FILE: rdagent/scenarios/finetune/experiment/workspace.py
  class FTWorkspace (line 27) | class FTWorkspace(FBWorkspace):
    method __init__ (line 37) | def __init__(self, *args, **kwargs):
    method run (line 48) | def run(

FILE: rdagent/scenarios/finetune/loop.py
  class LLMFinetuneRDLoop (line 14) | class LLMFinetuneRDLoop(RDLoop):
    method __init__ (line 20) | def __init__(self, PROP_SETTING: LLMFinetunePropSetting):
    method direct_exp_gen (line 32) | async def direct_exp_gen(self, prev_out: dict[str, Any]):
    method coding (line 39) | def coding(self, prev_out: dict[str, Any]):
    method feedback (line 46) | def feedback(self, prev_out: dict[str, Any]):
    method record (line 57) | def record(self, prev_out: dict[str, Any]):

FILE: rdagent/scenarios/finetune/proposal/proposal.py
  class FTHypothesis (line 24) | class FTHypothesis(Hypothesis):
    method __init__ (line 27) | def __init__(
    method __str__ (line 43) | def __str__(self) -> str:
  class LLMFinetuneExpGen (line 56) | class LLMFinetuneExpGen(ExpGen):
    method __init__ (line 62) | def __init__(self, scen: LLMFinetuneScen):
    method gen (line 65) | def gen(self, trace: Trace) -> FTExperiment:
    method _gen_hypothesis (line 74) | def _gen_hypothesis(self, trace: Trace, base_model: str, parent_exp: F...

FILE: rdagent/scenarios/finetune/proposal/trace.py
  class FTTrace (line 23) | class FTTrace(Trace["LLMFinetuneScen", KnowledgeBase]):
    method __init__ (line 30) | def __init__(self, scen: "LLMFinetuneScen", knowledge_base: KnowledgeB...
    method sota_benchmark (line 36) | def sota_benchmark(self) -> dict | None:
    method get_experiment_info (line 49) | def get_experiment_info(self, exp: "FTExperiment") -> dict[str, Any]:
    method sota_info (line 75) | def sota_info(self) -> dict[str, Any] | None:

FILE: rdagent/scenarios/finetune/scen/docker_scripts/extract_parameters.py
  function extract_field_info (line 31) | def extract_field_info(field):
  function extract_params (line 51) | def extract_params(cls):
  function extract_base_params (line 56) | def extract_base_params(cls):
  function save_parameters (line 73) | def save_parameters(base_dir):
  function main (line 109) | def main():

FILE: rdagent/scenarios/finetune/scen/llama_factory_manager.py
  class LLaMAFactoryManager (line 99) | class LLaMAFactoryManager:
    method __init__ (line 102) | def __init__(self):
    method extract_info_from_docker (line 107) | def extract_info_from_docker(self) -> Dict:
    method _load_extracted_data (line 154) | def _load_extracted_data(self) -> Dict:
    method get_info (line 172) | def get_info(self) -> Dict:
    method methods (line 179) | def methods(self) -> List[str]:
    method models (line 184) | def models(self) -> List[str]:
    method hf_models (line 189) | def hf_models(self) -> List[str]:
    method peft_methods (line 195) | def peft_methods(self) -> List[str]:
    method training_stages (line 201) | def training_stages(self) -> Dict[str, str]:
    method templates (line 206) | def templates(self) -> List[str]:
    method is_peft_method (line 210) | def is_peft_method(self, method: str) -> bool:
    method get_parameters (line 214) | def get_parameters(self, param_type: Optional[str] = None) -> Dict:
    method _format_param_line (line 221) | def _format_param_line(self, param_name: str, param_info: dict, max_he...
    method _format_params_dict (line 238) | def _format_params_dict(self, params_dict: dict, max_help_len: int | N...
    method format_shared_params (line 246) | def format_shared_params(self, max_help_len: int | None = DEFAULT_HELP...
    method format_method_specific_params (line 263) | def format_method_specific_params(self, method: str, max_help_len: int...

FILE: rdagent/scenarios/finetune/scen/memory_estimator.py
  class MemoryEstimator (line 10) | class MemoryEstimator:
    method __init__ (line 32) | def __init__(
    method from_model_name (line 52) | def from_model_name(
    method _base_memory (line 73) | def _base_memory(self, method: str) -> float:
    method _activation_factor (line 87) | def _activation_factor(self, method: str) -> float:
    method _find_max_seq_len (line 91) | def _find_max_seq_len(self, method: str, batch_size: int = 1) -> int:
    method estimate (line 105) | def estimate(self) -> dict[str, int]:
    method format (line 110) | def format(self, estimates: dict[str, int] = None) -> str:

FILE: rdagent/scenarios/finetune/scen/scenario.py
  class LLMFinetuneScen (line 26) | class LLMFinetuneScen(DataScienceScen):
    method __init__ (line 29) | def __init__(self) -> None:
    method benchmark_hash (line 74) | def benchmark_hash(self, model_name, benchmark_name) -> str:
    method run_baseline_model_evaluation (line 78) | def run_baseline_model_evaluation(self, model_name, benchmark_name) ->...
    method real_full_timeout (line 112) | def real_full_timeout(self):
    method _generate_memory_report (line 115) | def _generate_memory_report(self) -> str:
    method _validate_and_prepare_environment (line 150) | def _validate_and_prepare_environment(self):
    method _initialize_llama_factory (line 164) | def _initialize_llama_factory(self):
    method _select_relevant_datasets (line 175) | def _select_relevant_datasets(self) -> list[str]:
    method _llm_select_datasets (line 201) | def _llm_select_datasets(self) -> tuple[list[str], str]:
    method _prepare_dataset_config (line 231) | def _prepare_dataset_config(self) -> dict:
    method metric_direction (line 280) | def metric_direction(self) -> bool:
    method get_scenario_all_desc (line 284) | def get_scenario_all_desc(self, enable_dataset_description: bool = Fal...

FILE: rdagent/scenarios/finetune/scen/utils.py
  function _find_data_files (line 21) | def _find_data_files(dataset_path: Path, max_files: int = 50) -> list[Pa...
  function _truncate_long_values (line 40) | def _truncate_long_values(obj, max_length: int = 3000):
  function _compute_column_stats (line 67) | def _compute_column_stats(data: list[dict]) -> dict[str, dict]:
  function _load_dataset_for_stats (line 138) | def _load_dataset_for_stats(data_files: list[Path], max_samples: int = 5...
  class FinetuneDatasetDescription (line 187) | class FinetuneDatasetDescription(dict):
    method __str__ (line 190) | def __str__(self) -> str:
  class FinetuneFileDescription (line 216) | class FinetuneFileDescription(dict):
    method __str__ (line 219) | def __str__(self) -> str:
  class FinetuneDatasetDescriptor (line 230) | class FinetuneDatasetDescriptor:
    method _generate_file_tree (line 233) | def _generate_file_tree(self, dataset_path: Path) -> str:
    method _count_samples_in_file (line 242) | def _count_samples_in_file(self, data_file: Path) -> int:
    method _generate_stats (line 275) | def _generate_stats(self, dataset_path: Path, include_column_stats: bo...
    method hash_dataset_path (line 334) | def hash_dataset_path(
    method describe_dataset_folder (line 348) | def describe_dataset_folder(
    method get_dataset_stats (line 425) | def get_dataset_stats(self, dataset_path: Path) -> dict[str, Any]:
    method _walk (line 429) | def _walk(self, dir_path: Path, depth: int, max_depth: int, target_nam...
    method _read_dataset_readme (line 445) | def _read_dataset_readme(self, dataset_path: Path, max_chars: int = 50...
    method _extract_samples_for_template (line 467) | def _extract_samples_for_template(self, data_files: list[Path], max_sa...
    method describe_model (line 502) | def describe_model(self, base_model_name: str = None, ft_file_path: st...
    method describe_file_json (line 579) | def describe_file_json(self, data_file: Path, max_samples: int = 3) ->...
    method describe_file_jsonl (line 594) | def describe_file_jsonl(self, data_file: Path, max_samples: int = 3) -...
    method describe_file_csv (line 616) | def describe_file_csv(self, data_file: Path, max_samples: int = 3) -> ...
    method describe_file_parquet (line 634) | def describe_file_parquet(self, data_file: Path, max_samples: int = 3)...
    method describe_data_file (line 652) | def describe_data_file(self, data_file: Path) -> FinetuneFileDescription:
    method _discover_subtasks (line 677) | def _discover_subtasks(self, dataset_dir: Path) -> dict:
    method analyze_dataset (line 712) | def analyze_dataset(self, dataset_dir: Path) -> dict:
  function _read_single_dataset_readme (line 774) | def _read_single_dataset_readme(dataset_path: Path, max_chars: int = 200...
  function check_all_dataset_in_info (line 830) | def check_all_dataset_in_info(ft_file_path, existing_config, max_depth: ...
  function generate_dataset_info_config (line 863) | def generate_dataset_info_config(target_dataset_list: list, ft_file_path...

FILE: rdagent/scenarios/finetune/train/eval.py
  function extract_loss_history (line 30) | def extract_loss_history(output_path) -> Dict[str, List[Dict[str, Any]]]:
  class FTRunnerEvaluator (line 78) | class FTRunnerEvaluator(CoSTEEREvaluator):
    method evaluate (line 81) | def evaluate(
    method _generate_llm_feedback (line 239) | def _generate_llm_feedback(
    method _run_full_data_processing (line 303) | def _run_full_data_processing(self, implementation: FBWorkspace):

FILE: rdagent/scenarios/finetune/train/runner.py
  class FTRunnerSettings (line 31) | class FTRunnerSettings(FTCoderCoSTEERSettings):
    class Config (line 34) | class Config:
  class FTRunnerEvolvingStrategy (line 38) | class FTRunnerEvolvingStrategy(MultiProcessEvolvingStrategy):
    method implement_one_task (line 45) | def implement_one_task(
  class LLMFinetuneRunner (line 63) | class LLMFinetuneRunner(CoSTEER):
    method __init__ (line 66) | def __init__(
    method develop (line 95) | def develop(self, exp):
    method get_develop_max_seconds (line 110) | def get_develop_max_seconds(self) -> int | None:
    method compare_and_pick_fb (line 114) | def compare_and_pick_fb(self, base_fb, new_fb) -> bool:

FILE: rdagent/scenarios/finetune/utils.py
  function ensure_ft_assets_exist (line 9) | def ensure_ft_assets_exist(

FILE: rdagent/scenarios/general_model/scenario.py
  class GeneralModelScenario (line 8) | class GeneralModelScenario(Scenario):
    method __init__ (line 9) | def __init__(self) -> None:
    method background (line 18) | def background(self) -> str:
    method source_data (line 22) | def source_data(self) -> str:
    method output_format (line 26) | def output_format(self) -> str:
    method interface (line 30) | def interface(self) -> str:
    method simulator (line 34) | def simulator(self) -> str:
    method rich_style_description (line 38) | def rich_style_description(self) -> str:
    method get_scenario_all_desc (line 41) | def get_scenario_all_desc(
    method get_runtime_environment (line 54) | def get_runtime_environment(self):

FILE: rdagent/scenarios/kaggle/developer/coder.py
  class KGModelFeatureSelectionCoder (line 35) | class KGModelFeatureSelectionCoder(Developer[KGModelExperiment]):
    method develop (line 36) | def develop(self, exp: KGModelExperiment) -> KGModelExperiment:

FILE: rdagent/scenarios/kaggle/developer/feedback.py
  class KGExperiment2Feedback (line 16) | class KGExperiment2Feedback(Experiment2Feedback):
    method process_results (line 17) | def process_results(self, current_result, sota_result):
    method generate_feedback (line 40) | def generate_feedback(self, exp: Experiment, trace: Trace) -> Hypothes...

FILE: rdagent/scenarios/kaggle/developer/runner.py
  class KGCachedRunner (line 17) | class KGCachedRunner(CachedRunner[ASpecificExp]):
    method get_cache_key (line 18) | def get_cache_key(self, exp: ASpecificExp) -> str:
    method assign_cached_result (line 28) | def assign_cached_result(self, exp: Experiment, cached_res: Experiment...
    method init_develop (line 41) | def init_develop(self, exp: KGFactorExperiment | KGModelExperiment) ->...
  class KGModelRunner (line 60) | class KGModelRunner(KGCachedRunner[KGModelExperiment]):
    method develop (line 62) | def develop(self, exp: KGModelExperiment) -> KGModelExperiment:
  class KGFactorRunner (line 94) | class KGFactorRunner(KGCachedRunner[KGFactorExperiment]):
    method develop (line 96) | def develop(self, exp: KGFactorExperiment) -> KGFactorExperiment:

FILE: rdagent/scenarios/kaggle/experiment/kaggle_experiment.py
  class KGModelExperiment (line 37) | class KGModelExperiment(ModelExperiment[ModelTask, KGFBWorkspace, ModelF...
    method __init__ (line 38) | def __init__(self, *args, source_feature_size: int = None, **kwargs) -...
  class KGFactorExperiment (line 61) | class KGFactorExperiment(FeatureExperiment[FactorTask, KGFBWorkspace, Fa...
    method __init__ (line 62) | def __init__(self, *args, source_feature_size: int = None, **kwargs) -...

FILE: rdagent/scenarios/kaggle/experiment/scenario.py
  class KGScenario (line 36) | class KGScenario(Scenario):
    method __init__ (line 37) | def __init__(self, competition: str) -> None:
    method _analysis_competition_description (line 73) | def _analysis_competition_description(self):
    method get_competition_full_desc (line 101) | def get_competition_full_desc(self) -> str:
    method background (line 114) | def background(self) -> str:
    method source_data (line 134) | def source_data(self) -> str:
    method output_format (line 167) | def output_format(self, tag=None) -> str:
    method interface (line 182) | def interface(self, tag=None) -> str:
    method simulator (line 197) | def simulator(self, tag=None) -> str:
    method rich_style_description (line 215) | def rich_style_description(self) -> str:
    method get_scenario_all_desc (line 242) | def get_scenario_all_desc(

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/fea_share_preprocess.py
  function prepreprocess (line 11) | def prepreprocess():
  function preprocess_fit (line 31) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 58) | def preprocess_transform(X: pd.DataFrame, preprocessor, label_encoders):
  function preprocess_script (line 77) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/model_randomforest.py
  function fit (line 13) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 26) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/model_xgboost.py
  function fit (line 9) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 27) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/train.py
  function compute_metrics_for_classification (line 18) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 24) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/covid19-global-forecasting-week-1/fea_share_preprocess.py
  function prepreprocess (line 9) | def prepreprocess():
  function preprocess_script (line 45) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/covid19-global-forecasting-week-1/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/covid19-global-forecasting-week-1/model/model_xgboost.py
  function fit (line 5) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 27) | def predict(models, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/covid19-global-forecasting-week-1/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/covid19-global-forecasting-week-1/train.py
  function compute_rmsle (line 17) | def compute_rmsle(y_true, y_pred):
  function import_module_from_path (line 22) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/fea_share_preprocess.py
  function prepreprocess (line 9) | def prepreprocess():
  function preprocess_script (line 26) | def preprocess_script():
  function clean_and_impute_data (line 54) | def clean_and_impute_data(X_train, X_valid, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/model/model_nn.py
  class NeuralNetwork (line 12) | class NeuralNetwork(nn.Module):
    method __init__ (line 13) | def __init__(self, input_channels, num_classes):
    method forward (line 23) | def forward(self, x):
  function fit (line 34) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 82) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/model/model_xgboost.py
  function fit (line 9) | def fit(X_train, y_train, X_valid, y_valid):
  function predict (line 30) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/digit-recognizer/train.py
  function compute_metrics_for_classification (line 17) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 22) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/feedback-prize-english-language-learning/fea_share_preprocess.py
  function preprocess_script (line 9) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/feedback-prize-english-language-learning/feature/feature.py
  class IdentityFeature (line 10) | class IdentityFeature:
    method fit (line 11) | def fit(self, train_df: pd.DataFrame):
    method transform (line 18) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/feedback-prize-english-language-learning/model/model_randomforest.py
  function fit (line 6) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 19) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/feedback-prize-english-language-learning/model/model_xgboost.py
  function is_sparse_df (line 10) | def is_sparse_df(df: pd.DataFrame) -> bool:
  function fit (line 15) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 30) | def predict(model, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/feedback-prize-english-language-learning/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/feedback-prize-english-language-learning/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/feedback-prize-english-language-learning/train.py
  function import_module_from_path (line 11) | def import_module_from_path(module_name, module_path):
  function MCRMSE (line 18) | def MCRMSE(y_true, y_pred):

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/fea_share_preprocess.py
  function prepreprocess (line 9) | def prepreprocess():
  function preprocess_script (line 26) | def preprocess_script():
  function clean_and_impute_data (line 50) | def clean_and_impute_data(X_train, X_valid, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/model/model_randomforest.py
  function fit (line 13) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 31) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/model/model_xgboost.py
  function fit (line 9) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 29) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/train.py
  function compute_metrics_for_classification (line 17) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 23) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/forest-cover-type-prediction/train_past.py
  function import_module_from_path (line 19) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/meta_tpl_deprecated/fea_share_preprocess.py
  function prepreprocess (line 11) | def prepreprocess():
  function preprocess_fit (line 31) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 63) | def preprocess_transform(X: pd.DataFrame, preprocessor):
  function preprocess_script (line 83) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/meta_tpl_deprecated/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/meta_tpl_deprecated/model/model_nn.py
  class FeatureInteractionModel (line 13) | class FeatureInteractionModel(nn.Module):
    method __init__ (line 14) | def __init__(self, num_features):
    method forward (line 23) | def forward(self, x):
  function fit (line 32) | def fit(X_train, y_train, X_valid, y_valid):
  function predict (line 67) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/meta_tpl_deprecated/model/model_randomforest.py
  function select (line 13) | def select(X: pd.DataFrame) -> pd.DataFrame:
  function fit (line 21) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 43) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/meta_tpl_deprecated/model/model_xgboost.py
  function select (line 9) | def select(X: pd.DataFrame) -> pd.DataFrame:
  function fit (line 14) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 34) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/meta_tpl_deprecated/train.py
  function compute_metrics_for_classification (line 18) | def compute_metrics_for_classification(y_true, y_pred):
  function compute_metrics_for_classification (line 24) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 30) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/fea_share_preprocess.py
  function prepreprocess (line 12) | def prepreprocess():
  function preprocess_script (line 29) | def preprocess_script():
  function clean_and_impute_data (line 53) | def clean_and_impute_data(X_train, X_valid, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/feature/feature.py
  class DatetimeFeature (line 9) | class DatetimeFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/model/model_linear.py
  function fit (line 13) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 31) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/model/select_linear.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/new-york-city-taxi-fare-prediction/train.py
  function compute_metrics_for_classification (line 17) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 23) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/fea_share_preprocess.py
  function prepreprocess (line 12) | def prepreprocess():
  function preprocess_fit (line 37) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 62) | def preprocess_transform(X: pd.DataFrame, preprocessor, numerical_cols, ...
  function preprocess_script (line 70) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/model/model_randomforest.py
  function fit (line 7) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 26) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/model/model_xgboost.py
  function fit (line 5) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 25) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/optiver-realized-volatility-prediction/train.py
  function compute_rmspe (line 17) | def compute_rmspe(y_true, y_pred):
  function import_module_from_path (line 23) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/fea_share_preprocess.py
  function preprocess_script (line 8) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/model/model_randomforest.py
  function fit (line 5) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 22) | def predict(model, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/model/model_xgboost.py
  function fit (line 9) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 29) | def predict(model, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e11/train.py
  function import_module_from_path (line 12) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/fea_share_preprocess.py
  function preprocess_script (line 8) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/model/model_randomforest.py
  function fit (line 5) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 22) | def predict(model, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/model/model_xgboost.py
  function fit (line 9) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 29) | def predict(model, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e14/train.py
  function import_module_from_path (line 12) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/fea_share_preprocess.py
  function preprocess_script (line 9) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/model/model_randomforest.py
  function fit (line 5) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 22) | def predict(model, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/model/model_xgboost.py
  function fit (line 9) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 29) | def predict(model, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e16/train.py
  function import_module_from_path (line 12) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/fea_share_preprocess.py
  function prepreprocess (line 11) | def prepreprocess():
  function preprocess_fit (line 48) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 67) | def preprocess_transform(X: pd.DataFrame, preprocessor):
  function preprocess_script (line 80) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/model/model_randomforest.py
  function fit (line 13) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 26) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/model/model_xgboost.py
  function fit (line 10) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 32) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s3e26/train.py
  function compute_metrics_for_classification (line 18) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 25) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/fea_share_preprocess.py
  function prepreprocess (line 10) | def prepreprocess():
  function preprocess_fit (line 22) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 38) | def preprocess_transform(X: pd.DataFrame, preprocessor, numerical_cols):
  function preprocess_script (line 47) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/model/model_randomforest.py
  function fit (line 7) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 26) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/model/model_xgboost.py
  function fit (line 5) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 28) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e5/train.py
  function compute_r2 (line 17) | def compute_r2(y_true, y_pred):
  function import_module_from_path (line 22) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/fea_share_preprocess.py
  function prepreprocess (line 11) | def prepreprocess():
  function preprocess_fit (line 31) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 62) | def preprocess_transform(X: pd.DataFrame, preprocessor, numerical_cols, ...
  function preprocess_script (line 71) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/model/model_randomforest.py
  function fit (line 13) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 31) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/model/model_xgboost.py
  function fit (line 9) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 27) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e8/train.py
  function compute_metrics_for_classification (line 18) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 24) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/fea_share_preprocess.py
  function prepreprocess (line 11) | def prepreprocess():
  function preprocess_fit (line 23) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 48) | def preprocess_transform(X: pd.DataFrame, preprocessor, numerical_cols, ...
  function preprocess_script (line 57) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/model/model_randomforest.py
  function fit (line 7) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 26) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/model/model_xgboost.py
  function fit (line 5) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 25) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/playground-series-s4e9/train.py
  function compute_rmse (line 18) | def compute_rmse(y_true, y_pred):
  function import_module_from_path (line 25) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/fea_share_preprocess.py
  function prepreprocess (line 11) | def prepreprocess():
  function preprocess_fit (line 69) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 88) | def preprocess_transform(X: pd.DataFrame, preprocessor):
  function preprocess_script (line 101) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/model/model_randomforest.py
  function fit (line 12) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 25) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/model/model_xgboost.py
  function fit (line 10) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 32) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/sf-crime/train.py
  function compute_metrics_for_classification (line 18) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 25) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/fea_share_preprocess.py
  function prepreprocess (line 11) | def prepreprocess():
  function preprocess_fit (line 31) | def preprocess_fit(X_train: pd.DataFrame):
  function preprocess_transform (line 58) | def preprocess_transform(X: pd.DataFrame, preprocessor, label_encoders):
  function preprocess_script (line 77) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/model/model_randomforest.py
  function fit (line 13) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 26) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/model/model_xgboost.py
  function fit (line 9) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 27) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/spaceship-titanic/train.py
  function compute_metrics_for_classification (line 18) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 24) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/statoil-iceberg-classifier-challenge/fea_share_preprocess.py
  function prepreprocess (line 8) | def prepreprocess():
  function preprocess_script (line 53) | def preprocess_script():

FILE: rdagent/scenarios/kaggle/experiment/templates/statoil-iceberg-classifier-challenge/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/statoil-iceberg-classifier-challenge/model/model_xgboost.py
  function fit (line 10) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 32) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/statoil-iceberg-classifier-challenge/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/statoil-iceberg-classifier-challenge/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/statoil-iceberg-classifier-challenge/train.py
  function compute_metrics_for_classification (line 18) | def compute_metrics_for_classification(y_true, y_pred):
  function import_module_from_path (line 23) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/fea_share_preprocess.py
  function prepreprocess (line 12) | def prepreprocess():
  function preprocess_script (line 29) | def preprocess_script():
  function clean_and_impute_data (line 62) | def clean_and_impute_data(X_train, X_valid, X_test):

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/feature/feature.py
  class IdentityFeature (line 9) | class IdentityFeature:
    method fit (line 10) | def fit(self, train_df: pd.DataFrame):
    method transform (line 16) | def transform(self, X: pd.DataFrame):

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/model/model_randomforest.py
  function fit (line 13) | def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame...
  function predict (line 31) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/model/model_xgboost.py
  function fit (line 9) | def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFr...
  function predict (line 30) | def predict(model, X):

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/model/select_lightgbm.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/model/select_nn.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/model/select_randomforest.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/model/select_xgboost.py
  function select (line 4) | def select(X: pd.DataFrame) -> pd.DataFrame:

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground-series-dec-2021/train.py
  function import_module_from_path (line 17) | def import_module_from_path(module_name, module_path):

FILE: rdagent/scenarios/kaggle/experiment/templates/tabular-playground

[
  {
    "path": ".bumpversion.cfg",
    "chars": 98,
    "preview": "[bumpversion]\ncurrent_version = 0.0.0\ncommit = True\ntag = True\n\n[bumpversion:file:pyproject.toml]\n"
  },
  {
    "path": ".commitlintrc.js",
    "chars": 920,
    "preview": "module.exports = {\n    extends: [\"@commitlint/config-conventional\"],\n    rules: {\n        // Configuration Format: [leve"
  },
  {
    "path": ".devcontainer/Dockerfile",
    "chars": 361,
    "preview": "# 1. Pull down your Azure Container Registry image\nFROM rdagentappregistry.azurecr.io/rd-agent-mle:20250623\n\n# 2. (Optio"
  },
  {
    "path": ".devcontainer/README.md",
    "chars": 1620,
    "preview": "# Introduction\n\n!!!!!This dev container is not for public development!!!!!!\n!!!!!Please don't use it if you are just a p"
  },
  {
    "path": ".devcontainer/devcontainer.json",
    "chars": 776,
    "preview": "{\n  \"name\": \"rd-agent-mle DevContainer\",\n  \"build\": {\n    \"dockerfile\": \"Dockerfile\",\n    \"context\": \"..\"\n  },\n  \"worksp"
  },
  {
    "path": ".devcontainer/env",
    "chars": 945,
    "preview": "# Global configs:\n\nMAX_RETRY=12000\nRETRY_WAIT_SECONDS=5\nTIMEOUT_FAIL_LIMIT=100\n\n# litellm\n# CHAT_MODEL=gpt-4o\n# CHAT_TEM"
  },
  {
    "path": ".github/FUNDING.yml",
    "chars": 25,
    "preview": "github:\n  - MIIC-finance\n"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/bug-report.md",
    "chars": 1034,
    "preview": "---\nname: \"\\U0001F41B Bug Report\"\nabout: Submit a bug report to help us improve RD-Agent\nlabels: bug\n\n---\n\n## 🐛 Bug Desc"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/documentation.md",
    "chars": 201,
    "preview": "---\nname: \"\\U0001F4D6 Documentation\"\nabout: Report an issue related to documentation\n\n---\n\n## 📖 Documentation\n\n<!-- Plea"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/feature-request.md",
    "chars": 594,
    "preview": "---\nname: \"\\U0001F31FFeature Request\"\nabout: Request for a new RD-Agent feature\nlabels: enhancement\n\n---\n\n## 🌟 Feature D"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/question.md",
    "chars": 315,
    "preview": "---\nname: \"❓Questions & Help\"\nabout: Have some questions? We can offer help.\nlabels: question\n\n---\n\n## ❓ Questions and H"
  },
  {
    "path": ".github/PULL_REQUEST_TEMPLATE.md",
    "chars": 2114,
    "preview": "<!--- Thank you for submitting a Pull Request! In order to make our work smoother. -->\n<!--- please make sure your Pull "
  },
  {
    "path": ".github/dependabot.yml",
    "chars": 404,
    "preview": "updates:\n  - commit-message:\n      prefix: build(actions)\n    directory: /\n    package-ecosystem: github-actions\n    sch"
  },
  {
    "path": ".github/workflows/ci.yml",
    "chars": 1967,
    "preview": "concurrency:\n  cancel-in-progress: true\n  group: ${{ github.workflow }}-${{ github.ref }}\njobs:\n  ci:\n    if: ${{ !cance"
  },
  {
    "path": ".github/workflows/pr.yml",
    "chars": 877,
    "preview": "name: Lint pull request title\n\non:\n  pull_request:\n    types:\n      - opened\n      - synchronize\n      - reopened\n      "
  },
  {
    "path": ".github/workflows/readthedocs-preview.yml",
    "chars": 378,
    "preview": "concurrency:\n  cancel-in-progress: true\n  group: ${{ github.workflow }}-${{ github.ref }}\njobs:\n  documentation-links:\n "
  },
  {
    "path": ".github/workflows/release.yml",
    "chars": 1563,
    "preview": "name: Release\non:\n  push:\n    branches:\n      - main\npermissions:\n  contents: read\njobs:\n  release_and_publish:\n    perm"
  },
  {
    "path": ".gitignore",
    "chars": 2377,
    "preview": "# Custom\n*.swp\n.DS_Store\nPipfile\npublic\nrelease-notes.md\ntypescript*\ntmp/\n.ai/\n\n# Byte-compiled / optimized / DLL files\n"
  },
  {
    "path": ".readthedocs.yaml",
    "chars": 1496,
    "preview": "# .readthedocs.yml\n# Read the Docs configuration file\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html fo"
  },
  {
    "path": ".streamlit/config.toml",
    "chars": 38,
    "preview": "[client]\nshowSidebarNavigation = false"
  },
  {
    "path": "CHANGELOG.md",
    "chars": 97692,
    "preview": "# Changelog\n\n## [0.8.0](https://github.com/microsoft/RD-Agent/compare/v0.7.0...v0.8.0) (2025-11-03)\n\n\n### Features\n\n* ad"
  },
  {
    "path": "CODE_OF_CONDUCT.md",
    "chars": 444,
    "preview": "# Microsoft Open Source Code of Conduct\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https://op"
  },
  {
    "path": "CONTRIBUTING.md",
    "chars": 1808,
    "preview": "# Contributing to RD-Agent\n\nWe welcome contributions and suggestions to improve RD-Agent. Whether it's solving an issue,"
  },
  {
    "path": "LICENSE",
    "chars": 1141,
    "preview": "    MIT License\n\n    Copyright (c) Microsoft Corporation.\n\n    Permission is hereby granted, free of charge, to any pers"
  },
  {
    "path": "Makefile",
    "chars": 9528,
    "preview": ".PHONY: clean deepclean install init-qlib-env dev constraints black isort mypy ruff toml-sort lint pre-commit test-run t"
  },
  {
    "path": "README.md",
    "chars": 32164,
    "preview": "<h4 align=\"center\">\n  <img src=\"docs/_static/logo.png\" alt=\"RA-Agent logo\" style=\"width:70%; \">\n  \n  <a href=\"https://rd"
  },
  {
    "path": "SECURITY.md",
    "chars": 2656,
    "preview": "<!-- BEGIN MICROSOFT SECURITY.MD V0.0.9 BLOCK -->\n\n## Security\n\nMicrosoft takes the security of our software products an"
  },
  {
    "path": "SUPPORT.md",
    "chars": 1244,
    "preview": "# TODO: The maintainer of this repo has not yet edited this file\r\n\r\n**REPO OWNER**: Do you want Customer Service & Suppo"
  },
  {
    "path": "TODO.md",
    "chars": 523,
    "preview": "We encourage to set the TODOs in code. But some TODOs are more global.\nSo we place it here.\n\n\n- [ ] Aligning the naming "
  },
  {
    "path": "constraints/3.10.txt",
    "chars": 78,
    "preview": "azure-identity==1.17.1\ndill==0.3.9\npillow==10.4.0\npsutil==6.1.0\nscipy==1.14.1\n"
  },
  {
    "path": "constraints/3.11.txt",
    "chars": 78,
    "preview": "azure-identity==1.17.1\ndill==0.3.9\npillow==10.4.0\npsutil==6.1.0\nscipy==1.14.1\n"
  },
  {
    "path": "docs/Makefile",
    "chars": 633,
    "preview": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the "
  },
  {
    "path": "docs/_static/RD2bench.json",
    "chars": 35405,
    "preview": "{\r\n  \"alpha053_15\": {\r\n    \"description\": \"Reversal class factor, negative delta of a ratio involving close, low, and hi"
  },
  {
    "path": "docs/api_reference.rst",
    "chars": 181,
    "preview": "=============\nAPI Reference\n=============\n\nHere you can find all ``RDAgent``'s interfaces.\n\n\nRD Loop\n=======\n\nResearch\n-"
  },
  {
    "path": "docs/changelog.md",
    "chars": 55,
    "preview": "# Changelog\n\n## [Unreleased]\n<!-- insertion marker -->\n"
  },
  {
    "path": "docs/conf.py",
    "chars": 2226,
    "preview": "# Configuration file for the Sphinx documentation builder.\n#\n# For the full list of built-in configuration values, see t"
  },
  {
    "path": "docs/development.rst",
    "chars": 1988,
    "preview": "=========================\nFor Development\n=========================\n\nIf you want to try the latest version or contribute"
  },
  {
    "path": "docs/index.rst",
    "chars": 708,
    "preview": ".. RDAgent documentation master file, created by\n   sphinx-quickstart on Mon Jul 15 04:27:50 2024.\n   You can adapt this"
  },
  {
    "path": "docs/installation_and_configuration.rst",
    "chars": 21921,
    "preview": "==============================\nInstallation and Configuration\n==============================\n\nInstallation\n============\n"
  },
  {
    "path": "docs/introduction.rst",
    "chars": 1243,
    "preview": "=========================\nIntroduction\n=========================\n\n\n\nIn modern industry, research and development (R&D) i"
  },
  {
    "path": "docs/make.bat",
    "chars": 799,
    "preview": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sp"
  },
  {
    "path": "docs/policy.rst",
    "chars": 1473,
    "preview": "======\nPolicy\n======\n\nThis project welcomes contributions and suggestions.  Most contributions require you to agree to a"
  },
  {
    "path": "docs/project_framework_introduction.rst",
    "chars": 1369,
    "preview": "===============================\nFramework Design & Components\n===============================\n\nFramework & Components\n=="
  },
  {
    "path": "docs/requirements.txt",
    "chars": 47,
    "preview": "sphinx\nsphinx_rtd_theme\nfuro\nimportlib.metadata"
  },
  {
    "path": "docs/research/benchmark.rst",
    "chars": 4158,
    "preview": "==============================\nBenchmark\n==============================\n\nIntroduction\n=============\n\nBenchmarking the ca"
  },
  {
    "path": "docs/research/catalog.rst",
    "chars": 1344,
    "preview": "===========\nResearch\n===========\n\nTo achieve the good effects and improve R&D capabilities, we face multiple challenges,"
  },
  {
    "path": "docs/research/dev.rst",
    "chars": 1613,
    "preview": "==============================\nDevelopment\n==============================\n\n\nRelated Paper\n-------------\n\n- `Collaborativ"
  },
  {
    "path": "docs/scens/catalog.rst",
    "chars": 1283,
    "preview": "=========================\nScenarios\n=========================\n\nScenario lists\n=========================\n\nIn the two key "
  },
  {
    "path": "docs/scens/data_agent_fin.rst",
    "chars": 5718,
    "preview": ".. _data_agent_fin:\n\n=====================\nFinance Data Agent\n=====================\n\n\n**🤖 Automated Quantitative Trading"
  },
  {
    "path": "docs/scens/data_copilot_fin.rst",
    "chars": 6679,
    "preview": ".. _data_copilot_fin:\n\n=====================\nFinance Data Copilot\n=====================\n\n\n**🤖 Automated Quantitative Tra"
  },
  {
    "path": "docs/scens/data_science.rst",
    "chars": 26898,
    "preview": ".. _data_science_agent:\n\n=======================\nData Science Agent\n=======================\n\n**🤖 Automated Feature Engin"
  },
  {
    "path": "docs/scens/finetune.rst",
    "chars": 7007,
    "preview": ".. _finetune_agent:\n\n=============================\nFine-tuning an Existing Model\n=============================\n\n## **🎯 S"
  },
  {
    "path": "docs/scens/model_agent_fin.rst",
    "chars": 6541,
    "preview": ".. _model_agent_fin:\n\n=======================\nFinance Model Agent\n=======================\n\n**🤖 Automated Quantitative Tr"
  },
  {
    "path": "docs/scens/model_copilot_general.rst",
    "chars": 4031,
    "preview": ".. _model_copilot_general:\n\n======================\nGeneral Model Copilot\n======================\n\n**🤖 Automated Model Res"
  },
  {
    "path": "docs/scens/quant_agent_fin.rst",
    "chars": 5197,
    "preview": ".. _quant_agent_fin:\n\n=====================\nFinance Quant Agent\n=====================\n\n\n**🥇The First Data-Centric Quant "
  },
  {
    "path": "docs/ui.rst",
    "chars": 1638,
    "preview": "==============\nUser Interface\n==============\n\n\nIntroduction\n============\n\nRD-Agent will generate some logs during the R&"
  },
  {
    "path": "pyproject.toml",
    "chars": 2612,
    "preview": "[build-system]\nbuild-backend = \"setuptools.build_meta\"\nrequires = [\n  \"setuptools\",\n  \"setuptools-scm\",\n]\n\n[project]\naut"
  },
  {
    "path": "rdagent/app/CI/README.md",
    "chars": 852,
    "preview": "# CI 检查\n\n`.github/workflows/ci.yml`配置了提交时自动运行`Makefile`: 91~103行的命令，可以在这调整执行的命令\n\n在`.env`中设置`USE_CHAT_CACHE=True`可以让第二次修复"
  },
  {
    "path": "rdagent/app/CI/ci.ipynb",
    "chars": 357,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": "
  },
  {
    "path": "rdagent/app/CI/prompts.yaml",
    "chars": 5078,
    "preview": "generate_lint_command_template: |\n  Please generate a command to lint or format a {language} repository.\n  Here are some"
  },
  {
    "path": "rdagent/app/CI/run.py",
    "chars": 33019,
    "preview": "from __future__ import annotations\n\nimport datetime\nimport json\nimport re\nimport shlex\nimport subprocess\nimport time\nfro"
  },
  {
    "path": "rdagent/app/benchmark/factor/analysis.py",
    "chars": 7724,
    "preview": "import json\nimport pickle\nfrom pathlib import Path\n\nimport fire\nimport matplotlib.pyplot as plt\nimport numpy as np\nimpor"
  },
  {
    "path": "rdagent/app/benchmark/factor/eval.py",
    "chars": 1255,
    "preview": "from rdagent.app.qlib_rd_loop.conf import FACTOR_PROP_SETTING\nfrom rdagent.components.benchmark.conf import BenchmarkSet"
  },
  {
    "path": "rdagent/app/benchmark/model/README.md",
    "chars": 707,
    "preview": "# Tasks\n\n## Task Extraction\nFrom paper to task.\n```bash\n# python rdagent/app/model_implementation/task_extraction.py\n# I"
  },
  {
    "path": "rdagent/app/benchmark/model/eval.py",
    "chars": 1526,
    "preview": "from pathlib import Path\n\nfrom rdagent.components.coder.model_coder import ModelCoSTEER\nfrom rdagent.components.loader.t"
  },
  {
    "path": "rdagent/app/cli.py",
    "chars": 5990,
    "preview": "\"\"\"\nCLI entrance for all rdagent application.\n\nThis will\n- make rdagent a nice entry and\n- autoamtically load dotenv\n\"\"\""
  },
  {
    "path": "rdagent/app/data_science/conf.py",
    "chars": 8431,
    "preview": "from pathlib import Path\nfrom typing import Literal\n\nfrom pydantic_settings import SettingsConfigDict\n\nfrom rdagent.app."
  },
  {
    "path": "rdagent/app/data_science/debug.py",
    "chars": 146,
    "preview": "import fire\n\nfrom rdagent.scenarios.data_science.debug.data import create_debug_data\n\nif __name__ == \"__main__\":\n    fir"
  },
  {
    "path": "rdagent/app/data_science/loop.py",
    "chars": 3429,
    "preview": "import asyncio\nfrom pathlib import Path\nfrom typing import Optional\n\nimport fire\n\nfrom rdagent.app.data_science.conf imp"
  },
  {
    "path": "rdagent/app/finetune/data_science/conf.py",
    "chars": 1698,
    "preview": "import os\n\nfrom pydantic_settings import SettingsConfigDict\n\nfrom rdagent.app.data_science.conf import DS_RD_SETTING\nfro"
  },
  {
    "path": "rdagent/app/finetune/data_science/loop.py",
    "chars": 1211,
    "preview": "import asyncio\nfrom pathlib import Path\n\nimport fire\n\nfrom rdagent.app.data_science.conf import DS_RD_SETTING\nfrom rdage"
  },
  {
    "path": "rdagent/app/finetune/data_science/scen.py",
    "chars": 757,
    "preview": "from pathlib import Path\n\nfrom rdagent.app.data_science.conf import DS_RD_SETTING\nfrom rdagent.core.scenario import Scen"
  },
  {
    "path": "rdagent/app/finetune/data_science/tpl/components/coder/data_science/pipeline/prompts.yaml",
    "chars": 304,
    "preview": "pipeline_coder:\n  system: |-\n    {% include \"rdagent.components.coder.data_science.pipeline.prompts:pipeline_coder.syste"
  },
  {
    "path": "rdagent/app/finetune/data_science/tpl/scenarios/data_science/proposal/exp_gen/prompts_v2.yaml",
    "chars": 270,
    "preview": "task_gen:\n  system: |-\n    {% include \"rdagent.scenarios.data_science.proposal.exp_gen.prompts_v2:task_gen.system\" %}\n  "
  },
  {
    "path": "rdagent/app/finetune/llm/README.md",
    "chars": 5767,
    "preview": "# LLM Fine-tuning (FT) 场景运行指南\n\n本文档介绍如何运行 RD-Agent 的 LLM Fine-tuning 场景。\n\n## 简介\n\nFT 场景用于自动化优化大语言模型在特定 benchmark 上的表现。系统会自"
  },
  {
    "path": "rdagent/app/finetune/llm/conf.py",
    "chars": 5207,
    "preview": "from pathlib import Path\n\nfrom pydantic_settings import SettingsConfigDict\n\nfrom rdagent.core.conf import ExtendedBaseSe"
  },
  {
    "path": "rdagent/app/finetune/llm/job/README.md",
    "chars": 1932,
    "preview": "# FT Job Runner\n\n批量并行运行多个 LLM 微调任务的脚本。\n\n## 快速开始\n\n```bash\n# 1. 准备环境配置\ncp .env.template .env\n# 编辑 .env，填入 API key 等配置\n\n# 2"
  },
  {
    "path": "rdagent/app/finetune/llm/job/run_ft_job.sh",
    "chars": 6937,
    "preview": "#!/bin/bash\n# Run multiple FT tasks in parallel under a single job directory\n#\n# Usage: ./run_ft_job.sh [tasks.json]\n#\n#"
  },
  {
    "path": "rdagent/app/finetune/llm/job/scenarios.json",
    "chars": 17543,
    "preview": "{\n  \"_comment\": \"Benchmark scenarios for FT tasks. Used by run_ft_job.sh and UI.\",\n\n  \"aime24\": {\n    \"category\": \"math\""
  },
  {
    "path": "rdagent/app/finetune/llm/job/tasks.json.example",
    "chars": 384,
    "preview": "{\n  \"tasks\": [\n    {\n      \"model\": \"Qwen/Qwen3-8B\",\n      \"benchmark\": \"aime25\",\n      \"gpus\": \"0,1\"\n    },\n    {\n     "
  },
  {
    "path": "rdagent/app/finetune/llm/loop.py",
    "chars": 4124,
    "preview": "\"\"\"\nLLM Fine-tuning Entry Point\n\nStandard RDLoop entry point for LLM fine-tuning, consistent with data science implement"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/__init__.py",
    "chars": 29,
    "preview": "# FT (Fine-tune) scenario UI\n"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/app.py",
    "chars": 7625,
    "preview": "\"\"\"\nFT (Fine-tune) Timeline Viewer\nHierarchical view: Session > Loop > Stage > EvoLoop > Events\n\nRun:\n    streamlit run "
  },
  {
    "path": "rdagent/app/finetune/llm/ui/benchmarks/__init__.py",
    "chars": 2198,
    "preview": "\"\"\"Benchmark processors for core metric extraction.\n\nEach benchmark has its own processor that knows how to extract\nthe "
  },
  {
    "path": "rdagent/app/finetune/llm/ui/benchmarks/base.py",
    "chars": 1670,
    "preview": "\"\"\"Base class for benchmark core metric extraction.\"\"\"\n\nfrom abc import ABC, abstractmethod\n\n\nclass BenchmarkProcessor(A"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/benchmarks/bioprobench.py",
    "chars": 1965,
    "preview": "\"\"\"BioProBench benchmark processor.\"\"\"\n\nfrom .base import BenchmarkProcessor\n\n\nclass BioProBenchProcessor(BenchmarkProce"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/benchmarks/chemcotbench.py",
    "chars": 3796,
    "preview": "\"\"\"ChemCotBench benchmark processor.\"\"\"\n\nfrom .base import BenchmarkProcessor\n\n\nclass ChemCotBenchProcessor(BenchmarkPro"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/benchmarks/financeiq.py",
    "chars": 914,
    "preview": "\"\"\"FinanceIQ benchmark processor.\"\"\"\n\nfrom .base import BenchmarkProcessor\n\n\nclass FinanceIQProcessor(BenchmarkProcessor"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/benchmarks/panorama.py",
    "chars": 1550,
    "preview": "\"\"\"Panorama benchmark processor.\"\"\"\n\nfrom .base import BenchmarkProcessor\n\n\nclass PanoramaProcessor(BenchmarkProcessor):"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/benchmarks/tablebench.py",
    "chars": 1816,
    "preview": "\"\"\"TableBench benchmark processor.\"\"\"\n\nfrom .base import BenchmarkProcessor\n\n\nclass TableBenchProcessor(BenchmarkProcess"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/components.py",
    "chars": 27850,
    "preview": "\"\"\"\nFT UI Components - Hierarchical Event Renderers\n\"\"\"\n\nimport re\nfrom pathlib import Path\nfrom typing import Any\n\nimpo"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/config.py",
    "chars": 1457,
    "preview": "\"\"\"\nFT UI Configuration Constants\n\nCentralized configuration for FT Timeline Viewer.\n\"\"\"\n\nfrom typing import Literal\n\n# "
  },
  {
    "path": "rdagent/app/finetune/llm/ui/data_loader.py",
    "chars": 15846,
    "preview": "\"\"\"\nFT UI Data Loader\nLoad pkl logs and convert to hierarchical timeline structure\n\"\"\"\n\nimport re\nfrom dataclasses impor"
  },
  {
    "path": "rdagent/app/finetune/llm/ui/ft_summary.py",
    "chars": 22187,
    "preview": "\"\"\"\nFT Job Summary View\nDisplay summary table for all tasks in a job directory\n\"\"\"\n\nimport pickle\nfrom pathlib import Pa"
  },
  {
    "path": "rdagent/app/finetune/share/eval.py",
    "chars": 1971,
    "preview": "from pathlib import Path\n\nfrom rdagent.components.coder.CoSTEER.evaluators import (\n    CoSTEEREvaluator,\n    CoSTEERSin"
  },
  {
    "path": "rdagent/app/finetune/share/prompts.yaml",
    "chars": 1146,
    "preview": "prev_model_eval:\n  system: |-\n    You are a data scientist tasked with evaluating code generation. \n\n    You will receiv"
  },
  {
    "path": "rdagent/app/general_model/general_model.py",
    "chars": 1572,
    "preview": "import fire\n\nfrom rdagent.components.coder.model_coder.task_loader import (\n    ModelExperimentLoaderFromPDFfiles,\n)\nfro"
  },
  {
    "path": "rdagent/app/kaggle/conf.py",
    "chars": 3714,
    "preview": "from pydantic_settings import SettingsConfigDict\n\nfrom rdagent.core.conf import ExtendedBaseSettings\n\n\nclass KaggleBaseP"
  },
  {
    "path": "rdagent/app/kaggle/loop.py",
    "chars": 6454,
    "preview": "import subprocess\nfrom typing import Any\n\nimport fire\n\nfrom rdagent.app.kaggle.conf import KAGGLE_IMPLEMENT_SETTING\nfrom"
  },
  {
    "path": "rdagent/app/qlib_rd_loop/conf.py",
    "chars": 6166,
    "preview": "from typing import Optional\n\nfrom pydantic_settings import SettingsConfigDict\n\nfrom rdagent.components.workflow.conf imp"
  },
  {
    "path": "rdagent/app/qlib_rd_loop/factor.py",
    "chars": 1999,
    "preview": "\"\"\"\nFactor workflow with session control\n\"\"\"\n\nimport asyncio\nfrom pathlib import Path\nfrom typing import Any, Optional\n\n"
  },
  {
    "path": "rdagent/app/qlib_rd_loop/factor_from_report.py",
    "chars": 6894,
    "preview": "import asyncio\nimport json\nfrom pathlib import Path\nfrom typing import Any, Dict, Tuple\n\nimport fire\n\nfrom rdagent.app.q"
  },
  {
    "path": "rdagent/app/qlib_rd_loop/model.py",
    "chars": 1350,
    "preview": "\"\"\"\nModel workflow with session control\n\"\"\"\n\nimport asyncio\n\nimport fire\n\nfrom rdagent.app.qlib_rd_loop.conf import MODE"
  },
  {
    "path": "rdagent/app/qlib_rd_loop/prompts.yaml",
    "chars": 612,
    "preview": "hypothesis_generation:\n  system: |-\n    You are an expert in financial analysis. Your task is to generate a well-reasone"
  },
  {
    "path": "rdagent/app/qlib_rd_loop/quant.py",
    "chars": 6856,
    "preview": "\"\"\"\nQuant (Factor & Model) workflow with session control\n\"\"\"\n\nimport asyncio\nfrom typing import Any\n\nimport fire\n\nfrom r"
  },
  {
    "path": "rdagent/app/rl/conf.py",
    "chars": 1508,
    "preview": "from pathlib import Path\n\nfrom pydantic_settings import SettingsConfigDict\n\nfrom rdagent.core.conf import ExtendedBaseSe"
  },
  {
    "path": "rdagent/app/rl/loop.py",
    "chars": 1916,
    "preview": "\"\"\"\nRL Post-training Entry Point\n\"\"\"\n\nimport asyncio\nfrom typing import Optional\n\nimport typer\nfrom typing_extensions im"
  },
  {
    "path": "rdagent/app/rl/ui/__init__.py",
    "chars": 26,
    "preview": "\"\"\"RL Post-training UI\"\"\"\n"
  },
  {
    "path": "rdagent/app/rl/ui/app.py",
    "chars": 6040,
    "preview": "\"\"\"\nRL Post-training Timeline Viewer\nHierarchical view: Session > Loop > Stage > Events\n\nRun:\n    streamlit run rdagent/"
  },
  {
    "path": "rdagent/app/rl/ui/components.py",
    "chars": 10381,
    "preview": "\"\"\"\nRL UI Components - Event Renderers\nSimplified version without EvoLoop\n\"\"\"\n\nfrom typing import Any\n\nimport streamlit "
  },
  {
    "path": "rdagent/app/rl/ui/config.py",
    "chars": 952,
    "preview": "\"\"\"\nRL UI Configuration Constants\n\"\"\"\n\nfrom typing import Literal\n\n# Event type definition\nEventType = Literal[\n    \"sce"
  },
  {
    "path": "rdagent/app/rl/ui/data_loader.py",
    "chars": 9126,
    "preview": "\"\"\"\nRL UI Data Loader\nLoad pkl logs and convert to hierarchical timeline structure\nSimplified version: no EvoLoop (RL do"
  },
  {
    "path": "rdagent/app/rl/ui/rl_summary.py",
    "chars": 6041,
    "preview": "\"\"\"\nRL Job Summary View\nDisplay summary table for all tasks in a job directory\n\"\"\"\n\nimport pickle\nfrom pathlib import Pa"
  },
  {
    "path": "rdagent/app/utils/ape.py",
    "chars": 1270,
    "preview": "\"\"\"\nThis is the preliminary version of the APE (Automated Prompt Engineering)\n\"\"\"\n\nimport pickle\nfrom pathlib import Pat"
  },
  {
    "path": "rdagent/app/utils/health_check.py",
    "chars": 5842,
    "preview": "import os\nimport socket\n\nimport docker\nimport fire\nimport litellm\nfrom litellm import completion, embedding\nfrom litellm"
  },
  {
    "path": "rdagent/app/utils/info.py",
    "chars": 3151,
    "preview": "import importlib.metadata\nimport platform\nimport sys\nfrom pathlib import Path\n\nimport docker\nimport requests\nfrom packag"
  },
  {
    "path": "rdagent/app/utils/prompts.yaml",
    "chars": 5140,
    "preview": "ape:\n  system: |-\n    We'll provide you with a pair of Chat QA about data science.\n    We are creating solutions for a K"
  },
  {
    "path": "rdagent/app/utils/ws.py",
    "chars": 1833,
    "preview": "from typing import Optional\n\nimport typer\n\nfrom rdagent.app.data_science.conf import DS_RD_SETTING\nfrom rdagent.componen"
  },
  {
    "path": "rdagent/app/utils/ws_ft.py",
    "chars": 1568,
    "preview": "from typing import Optional\n\nimport typer\n\nfrom rdagent.app.finetune.llm.conf import FT_RD_SETTING\nfrom rdagent.componen"
  },
  {
    "path": "rdagent/components/agent/__init__.py",
    "chars": 66,
    "preview": "\"\"\"\nSome agent that can be shared across different scenarios.\n\"\"\"\n"
  },
  {
    "path": "rdagent/components/agent/base.py",
    "chars": 2255,
    "preview": "from abc import abstractmethod\n\nimport nest_asyncio\nfrom prefect import task\nfrom prefect.cache_policies import INPUTS\nf"
  },
  {
    "path": "rdagent/components/agent/context7/__init__.py",
    "chars": 1943,
    "preview": "from typing import Optional\n\nfrom pydantic_ai.mcp import MCPServerStreamableHTTP\n\nfrom rdagent.components.agent.base imp"
  },
  {
    "path": "rdagent/components/agent/context7/conf.py",
    "chars": 801,
    "preview": "\"\"\"\nThe context7 is based on a modified version of the context7.\n\nYou can follow the instructions to install it\n\n    mkd"
  },
  {
    "path": "rdagent/components/agent/context7/prompts.yaml",
    "chars": 3154,
    "preview": "# Context7 MCP Enhanced Query Prompts\n\nsystem_prompt: |-\n  You are a helpful assistant.\n  You help to user to search doc"
  },
  {
    "path": "rdagent/components/agent/mcp/__init__.py",
    "chars": 433,
    "preview": "\"\"\"\nHere are a list of MCP servers.\n\nThe MCP server is a individual RESTful API. So the only following things are includ"
  },
  {
    "path": "rdagent/components/agent/rag/__init__.py",
    "chars": 685,
    "preview": "from pydantic_ai.mcp import MCPServerStreamableHTTP\n\nfrom rdagent.components.agent.base import PAIAgent\nfrom rdagent.com"
  },
  {
    "path": "rdagent/components/agent/rag/conf.py",
    "chars": 418,
    "preview": "\"\"\"\nSettings for RAG agent.\n\nTODO: how run the RAG mcp server\n\"\"\"\n\nfrom pydantic_settings import BaseSettings, SettingsC"
  },
  {
    "path": "rdagent/components/benchmark/__init__.py",
    "chars": 139,
    "preview": "\"\"\"Shared benchmark evaluation utilities.\"\"\"\n\nfrom pathlib import Path\n\n# 共享配置目录\nBENCHMARK_CONFIGS_DIR = Path(__file__)."
  },
  {
    "path": "rdagent/components/benchmark/conf.py",
    "chars": 986,
    "preview": "from dataclasses import field\nfrom pathlib import Path\nfrom typing import Optional\n\nfrom rdagent.core.conf import Extend"
  },
  {
    "path": "rdagent/components/benchmark/configs/__init__.py",
    "chars": 51,
    "preview": "\"\"\"Shared OpenCompass benchmark configurations.\"\"\"\n"
  },
  {
    "path": "rdagent/components/benchmark/configs/models.yaml",
    "chars": 2628,
    "preview": "# Model Inference Parameters Configuration\n# Used by benchmark.py to determine inference settings for different models\n\n"
  },
  {
    "path": "rdagent/components/benchmark/configs/opencompass_template.yaml",
    "chars": 3998,
    "preview": "# Auto-generated OpenCompass Config for RD-Agent Benchmark\n# DO NOT EDIT MANUALLY - Generated by benchmark.py\n\ntemplate:"
  },
  {
    "path": "rdagent/components/benchmark/eval_method.py",
    "chars": 7712,
    "preview": "from collections import defaultdict\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple, Union\n\nimport pandas "
  },
  {
    "path": "rdagent/components/benchmark/example.json",
    "chars": 4318,
    "preview": "{\n    \"Turnover_Rate_Factor\": {\n        \"description\": \"A traditional factor based on 20-day average turnover rate, adju"
  },
  {
    "path": "rdagent/components/benchmark/utils.py",
    "chars": 2598,
    "preview": "\"\"\"Utilities shared by benchmark evaluators.\"\"\"\n\nfrom __future__ import annotations\n\nimport importlib\nimport logging\nimp"
  },
  {
    "path": "rdagent/components/coder/CoSTEER/__init__.py",
    "chars": 7284,
    "preview": "from copy import deepcopy\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom rdagent.components.coder.CoSTEER."
  },
  {
    "path": "rdagent/components/coder/CoSTEER/config.py",
    "chars": 1142,
    "preview": "from typing import Union\n\nfrom rdagent.core.conf import ExtendedBaseSettings\n\n\nclass CoSTEERSettings(ExtendedBaseSetting"
  },
  {
    "path": "rdagent/components/coder/CoSTEER/evaluators.py",
    "chars": 13957,
    "preview": "import json\nfrom abc import abstractmethod\nfrom copy import deepcopy\nfrom dataclasses import dataclass, field\nfrom typin"
  },
  {
    "path": "rdagent/components/coder/CoSTEER/evolvable_subjects.py",
    "chars": 1158,
    "preview": "from rdagent.core.evolving_framework import EvolvableSubjects\nfrom rdagent.core.experiment import Experiment, FBWorkspac"
  },
  {
    "path": "rdagent/components/coder/CoSTEER/evolving_strategy.py",
    "chars": 7268,
    "preview": "from __future__ import annotations\n\nfrom abc import abstractmethod\nfrom typing import Callable, Generator\n\nfrom rdagent."
  },
  {
    "path": "rdagent/components/coder/CoSTEER/knowledge_management.py",
    "chars": 49156,
    "preview": "from __future__ import annotations\n\nimport copy\nimport json\nimport pickle\nimport random\nimport re\nfrom itertools import "
  },
  {
    "path": "rdagent/components/coder/CoSTEER/prompts.yaml",
    "chars": 498,
    "preview": "\nanalyze_component_prompt_v1_system: |-\n  User is getting a new task that might consist of the components below (given i"
  },
  {
    "path": "rdagent/components/coder/CoSTEER/task.py",
    "chars": 430,
    "preview": "from rdagent.core.experiment import Task\n\n\nclass CoSTEERTask(Task):\n    def __init__(self, base_code: str = None, *args,"
  },
  {
    "path": "rdagent/components/coder/data_science/conf.py",
    "chars": 2764,
    "preview": "from typing import Literal\n\nfrom rdagent.app.data_science.conf import DS_RD_SETTING\nfrom rdagent.components.coder.CoSTEE"
  },
  {
    "path": "rdagent/components/coder/data_science/ensemble/__init__.py",
    "chars": 6221,
    "preview": "\"\"\"\nFile structure\n- ___init__.py: the entrance/agent of coder\n- evaluator.py\n- conf.py\n- exp.py: everything under the e"
  },
  {
    "path": "rdagent/components/coder/data_science/ensemble/conf.py",
    "chars": 101,
    "preview": "# Configuration file for ensemble component\n# Currently empty as no specific configuration is needed\n"
  },
  {
    "path": "rdagent/components/coder/data_science/ensemble/eval.py",
    "chars": 3914,
    "preview": "import json\nimport re\nfrom pathlib import Path\n\nfrom jinja2 import Environment, StrictUndefined\n\nfrom rdagent.app.data_s"
  },
  {
    "path": "rdagent/components/coder/data_science/ensemble/eval_tests/ensemble_test.txt",
    "chars": 5154,
    "preview": "\"\"\"\nTests for `ensemble_workflow` in ensemble.py\n\nA qualified ensemble_workflow implementation should:\n- Return predicti"
  },
  {
    "path": "rdagent/components/coder/data_science/ensemble/exp.py",
    "chars": 400,
    "preview": "import pickle\nimport site\nimport traceback\nfrom pathlib import Path\nfrom typing import Dict, Optional\n\nfrom rdagent.comp"
  },
  {
    "path": "rdagent/components/coder/data_science/ensemble/prompts.yaml",
    "chars": 4991,
    "preview": "ensemble_coder:\n  system: |-\n    You are a world-class data scientist and machine learning engineer with deep expertise "
  },
  {
    "path": "rdagent/components/coder/data_science/ensemble/test.py",
    "chars": 1679,
    "preview": "\"\"\"\nHelper functions for testing the ensemble coder(CoSTEER-based) component.\n\"\"\"\n\nimport sys\nfrom pathlib import Path\n\n"
  },
  {
    "path": "rdagent/components/coder/data_science/feature/__init__.py",
    "chars": 5574,
    "preview": "from pathlib import Path\n\nfrom rdagent.app.data_science.conf import DS_RD_SETTING\nfrom rdagent.components.coder.CoSTEER."
  },
  {
    "path": "rdagent/components/coder/data_science/feature/eval.py",
    "chars": 3284,
    "preview": "from pathlib import Path\n\nfrom rdagent.components.coder.CoSTEER.evaluators import (\n    CoSTEEREvaluator,\n    CoSTEERSin"
  },
  {
    "path": "rdagent/components/coder/data_science/feature/eval_tests/feature_test.txt",
    "chars": 4036,
    "preview": "\"\"\"\nTests for `feat_eng` in feature.py\n\"\"\"\n\n\nfrom copy import deepcopy\nimport sys\nimport numpy as np\nimport pandas as pd"
  },
  {
    "path": "rdagent/components/coder/data_science/feature/exp.py",
    "chars": 399,
    "preview": "import pickle\nimport site\nimport traceback\nfrom pathlib import Path\nfrom typing import Dict, Optional\n\nfrom rdagent.comp"
  },
  {
    "path": "rdagent/components/coder/data_science/feature/prompts.yaml",
    "chars": 5751,
    "preview": "feature_coder:\n  system: |-\n    You are a world-class data scientist and machine learning engineer with deep expertise i"
  },
  {
    "path": "rdagent/components/coder/data_science/feature/test.py",
    "chars": 1372,
    "preview": "\"\"\"\nHelper functions for testing the feature coder(CoSTEER-based) component.\n- Does the developer loop work correctly\n\nI"
  },
  {
    "path": "rdagent/components/coder/data_science/model/__init__.py",
    "chars": 7189,
    "preview": "from pathlib import Path\n\nfrom rdagent.app.data_science.conf import DS_RD_SETTING\nfrom rdagent.components.coder.CoSTEER."
  },
  {
    "path": "rdagent/components/coder/data_science/model/eval.py",
    "chars": 4980,
    "preview": "\"\"\"\nBeyond previous tests\n-\n\"\"\"\n\nimport json\nimport re\nfrom pathlib import Path\n\nfrom rdagent.app.data_science.conf impo"
  },
  {
    "path": "rdagent/components/coder/data_science/model/eval_tests/model_test.txt",
    "chars": 3571,
    "preview": "\"\"\"\nTests for `model_workflow` in model01.py\n\"\"\"\nimport sys\nimport time\n\nfrom feature import feat_eng\nfrom load_data imp"
  },
  {
    "path": "rdagent/components/coder/data_science/model/exp.py",
    "chars": 614,
    "preview": "from typing import Dict, Optional\n\nfrom rdagent.components.coder.CoSTEER.task import CoSTEERTask\n\n\n# Because we use isin"
  },
  {
    "path": "rdagent/components/coder/data_science/model/prompts.yaml",
    "chars": 8149,
    "preview": "model_coder:\n  system: |-\n    You are a world-class data scientist and machine learning engineer with deep expertise in "
  },
  {
    "path": "rdagent/components/coder/data_science/model/test.py",
    "chars": 2499,
    "preview": "\"\"\"\nGenerate dataset to test the model workflow output\n\"\"\"\n\nfrom pathlib import Path\n\nfrom rdagent.components.coder.CoST"
  },
  {
    "path": "rdagent/components/coder/data_science/pipeline/__init__.py",
    "chars": 6167,
    "preview": "\"\"\"\n\nLoop should not large change exclude\n- Action Choice[current data loader & spec]\n- other should share\n    - Propose"
  },
  {
    "path": "rdagent/components/coder/data_science/pipeline/eval.py",
    "chars": 17545,
    "preview": "# tess successfully running.\n# (GPT) if it aligns with the spec & rationality of the spec.\nimport json\nimport re\nfrom da"
  },
  {
    "path": "rdagent/components/coder/data_science/pipeline/eval_tests/submission_format_test.txt",
    "chars": 3819,
    "preview": "import hashlib\nfrom pathlib import Path\nimport pandas as pd\n\n\ndef calculate_md5(file_path):\n    with open(file_path, \"rb"
  },
  {
    "path": "rdagent/components/coder/data_science/pipeline/exp.py",
    "chars": 439,
    "preview": "from rdagent.components.coder.CoSTEER.task import CoSTEERTask\n\n\n# Because we use isinstance to distinguish between diffe"
  },
  {
    "path": "rdagent/components/coder/data_science/pipeline/prompts.yaml",
    "chars": 23903,
    "preview": "pipeline_coder:\n  system: |-\n    You are a grandmaster-level data scientist and machine learning engineer with deep expe"
  },
  {
    "path": "rdagent/components/coder/data_science/raw_data_loader/README.md",
    "chars": 275,
    "preview": "# CoSTEER\n\n- subworkspace使用主experiment_workspace `RD-Agent/rdagent/scenarios/data_science/experiment/experiment.py`\n\n## "
  },
  {
    "path": "rdagent/components/coder/data_science/raw_data_loader/__init__.py",
    "chars": 9766,
    "preview": "\"\"\"\n\nLoop should not large change exclude\n- Action Choice[current data loader & spec]\n- other should share\n    - Propose"
  },
  {
    "path": "rdagent/components/coder/data_science/raw_data_loader/conf.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "rdagent/components/coder/data_science/raw_data_loader/eval.py",
    "chars": 4106,
    "preview": "# tess successfully running.\n# (GPT) if it aligns with the spec & rationality of the spec.\nimport json\nimport re\nfrom pa"
  },
  {
    "path": "rdagent/components/coder/data_science/raw_data_loader/eval_tests/data_loader_test.txt",
    "chars": 2677,
    "preview": "\"\"\"\nTests for `load_data` in load_data.py\n\"\"\"\n\nimport pickle\n\nimport pandas as pd\nfrom load_data import load_data\n\nimpor"
  },
  {
    "path": "rdagent/components/coder/data_science/raw_data_loader/exp.py",
    "chars": 250,
    "preview": "from rdagent.components.coder.CoSTEER.task import CoSTEERTask\n\n\n# Because we use isinstance to distinguish between diffe"
  },
  {
    "path": "rdagent/components/coder/data_science/raw_data_loader/prompts.yaml",
    "chars": 20957,
    "preview": "\nspec:\n  system: |-\n    You are a world-class data scientist and machine learning engineer with deep expertise in statis"
  },
  {
    "path": "rdagent/components/coder/data_science/raw_data_loader/test.py",
    "chars": 995,
    "preview": "\"\"\"\nHelper functions for testing the raw_data_loader coder(CoSTEER-based) component.\n- Does the developer loop work corr"
  },
  {
    "path": "rdagent/components/coder/data_science/share/doc.py",
    "chars": 1267,
    "preview": "\"\"\"\nDevelopers concentrating on writing documents for a workspace\n\"\"\"\n\nfrom rdagent.core.developer import Developer\nfrom"
  },
  {
    "path": "rdagent/components/coder/data_science/share/ds_costeer.py",
    "chars": 345,
    "preview": "from rdagent.components.coder.CoSTEER import CoSTEER\n\n\nclass DSCoSTEER(CoSTEER):\n    def get_develop_max_seconds(self) -"
  },
  {
    "path": "rdagent/components/coder/data_science/share/eval.py",
    "chars": 8236,
    "preview": "import re\nfrom pathlib import Path\nfrom typing import Literal\n\nimport pandas as pd\n\nfrom rdagent.app.data_science.conf i"
  },
  {
    "path": "rdagent/components/coder/data_science/share/notebook.py",
    "chars": 5401,
    "preview": "\"\"\"\nHandles conversion from a Python file to a Jupyter notebook.\n\"\"\"\n\nimport argparse\nfrom typing import Optional\n\nimpor"
  },
  {
    "path": "rdagent/components/coder/data_science/share/prompts.yaml",
    "chars": 6150,
    "preview": "dump_model_coder:\n  guideline: |-\n    Your code will be executed in a inference mode with following command: \n    ```bas"
  },
  {
    "path": "rdagent/components/coder/data_science/share/util.py",
    "chars": 13851,
    "preview": "import ast\nimport io\nimport re\nimport tokenize\nfrom itertools import zip_longest\nfrom typing import List, Optional, Set,"
  },
  {
    "path": "rdagent/components/coder/data_science/utils.py",
    "chars": 275,
    "preview": "import re\n\n\ndef remove_eda_part(stdout: str) -> str:\n    \"\"\"Data Science scenario have a LLM-based EDA feature. We can r"
  },
  {
    "path": "rdagent/components/coder/data_science/workflow/__init__.py",
    "chars": 5355,
    "preview": "from rdagent.app.data_science.conf import DS_RD_SETTING\nfrom rdagent.components.coder.CoSTEER.evaluators import (\n    Co"
  },
  {
    "path": "rdagent/components/coder/data_science/workflow/eval.py",
    "chars": 7532,
    "preview": "import json\nimport re\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom rdagent.app.data_science.conf import DS_RD_SET"
  },
  {
    "path": "rdagent/components/coder/data_science/workflow/eval_tests/submission_format_test.txt",
    "chars": 3253,
    "preview": "from pathlib import Path\nimport pandas as pd\nimport hashlib\n\ndef calculate_md5(file_path):\n    with open(file_path, \"rb\""
  },
  {
    "path": "rdagent/components/coder/data_science/workflow/exp.py",
    "chars": 517,
    "preview": "import pickle\nimport site\nimport traceback\nfrom pathlib import Path\nfrom typing import Dict, Optional\n\nfrom rdagent.comp"
  },
  {
    "path": "rdagent/components/coder/data_science/workflow/prompts.yaml",
    "chars": 6675,
    "preview": "workflow_coder:\n  system: |-\n    You are a world-class data scientist and machine learning engineer with deep expertise "
  },
  {
    "path": "rdagent/components/coder/data_science/workflow/test.py",
    "chars": 2289,
    "preview": "\"\"\"\nGenerate dataset to test the workflow output\n\"\"\"\n\nfrom pathlib import Path\n\nfrom rdagent.components.coder.CoSTEER.co"
  },
  {
    "path": "rdagent/components/coder/factor_coder/__init__.py",
    "chars": 1297,
    "preview": "from rdagent.components.coder.CoSTEER import CoSTEER\nfrom rdagent.components.coder.CoSTEER.evaluators import CoSTEERMult"
  },
  {
    "path": "rdagent/components/coder/factor_coder/config.py",
    "chars": 1675,
    "preview": "import os\nfrom typing import Optional\n\nfrom pydantic_settings import SettingsConfigDict\n\nfrom rdagent.components.coder.C"
  },
  {
    "path": "rdagent/components/coder/factor_coder/eva_utils.py",
    "chars": 21733,
    "preview": "import io\nimport json\nfrom abc import abstractmethod\nfrom typing import Dict, Tuple\n\nimport pandas as pd\n\nfrom rdagent.c"
  },
  {
    "path": "rdagent/components/coder/factor_coder/evaluators.py",
    "chars": 6221,
    "preview": "import re\n\nfrom rdagent.components.coder.CoSTEER.evaluators import (\n    CoSTEEREvaluator,\n    CoSTEERMultiFeedback,\n   "
  },
  {
    "path": "rdagent/components/coder/factor_coder/evolving_strategy.py",
    "chars": 8615,
    "preview": "from __future__ import annotations\n\nimport json\nimport re\nfrom typing import Dict\n\nfrom rdagent.components.coder.CoSTEER"
  },
  {
    "path": "rdagent/components/coder/factor_coder/factor.py",
    "chars": 9527,
    "preview": "from __future__ import annotations\n\nimport subprocess\nimport uuid\nfrom pathlib import Path\nfrom typing import Tuple, Uni"
  },
  {
    "path": "rdagent/components/coder/factor_coder/factor_execution_template.txt",
    "chars": 379,
    "preview": "import os\n\nimport numpy as np\nimport pandas as pd\nfrom factor import feature_engineering_cls\n\nif os.path.exists(\"X_valid"
  },
  {
    "path": "rdagent/components/coder/factor_coder/prompts.yaml",
    "chars": 12502,
    "preview": "\nevaluator_code_feedback_v1_system: |-\n  User is trying to implement some factors in the following scenario:\n  {{ scenar"
  },
  {
    "path": "rdagent/components/coder/finetune/__init__.py",
    "chars": 16543,
    "preview": "\"\"\"\nLLM Fine-tuning CoSTEER Implementation\n\nThis module provides fine-tuning specific components for the CoSTEER framewo"
  },
  {
    "path": "rdagent/components/coder/finetune/conf.py",
    "chars": 14145,
    "preview": "import json\nimport os\nimport re\nimport shutil\nfrom pathlib import Path\nfrom typing import Any, Literal\n\nfrom rdagent.app"
  },
  {
    "path": "rdagent/components/coder/finetune/eval.py",
    "chars": 16472,
    "preview": "\"\"\"\nLLM Fine-tuning Evaluation Components\n\nProvides simplified evaluation: parameter filtering + micro-batch testing.\nNo"
  }
]

// ... and 635 more files (download for full content)