김경종
1.3 KiB
1.3 KiB
Step 1: model-notebooks
읽어야 할 파일
/AGENTS.md/docs/theory/02_response_surface_methodology.md/docs/theory/03_gaussian_process_kriging.md/docs/theory/04_random_forest.md/docs/theory/05_gradient_boosting.md/docs/theory/06_mlp_neural_network.md/src/femsurrogate/surrogates//src/femsurrogate/plotting/
작업
모델별 notebook 5개를 만든다.
notebooks/01_response_surface_surrogate.ipynbnotebooks/02_gaussian_process_kriging_surrogate.ipynbnotebooks/03_random_forest_surrogate.ipynbnotebooks/04_gradient_boosting_surrogate.ipynbnotebooks/05_mlp_surrogate.ipynb
각 notebook은 같은 dataset, target, split seed를 쓰고 metric JSON과 prediction CSV를 저장한다.
Acceptance Criteria
uv run jupyter nbconvert --to notebook --execute notebooks/01_response_surface_surrogate.ipynb
uv run jupyter nbconvert --to notebook --execute notebooks/02_gaussian_process_kriging_surrogate.ipynb
uv run jupyter nbconvert --to notebook --execute notebooks/03_random_forest_surrogate.ipynb
uv run jupyter nbconvert --to notebook --execute notebooks/04_gradient_boosting_surrogate.ipynb
uv run jupyter nbconvert --to notebook --execute notebooks/05_mlp_surrogate.ipynb
uv run ruff check .
금지사항
- 모델별로 다른 split을 쓰지 마라.