Benchmark Jupyter Notebooks

Welcome to the pyMAISE benchmarked Jupyter Notebooks! These notebooks include the machine learning benchmarks for reactor control, reactor physics, fuel performance, and heat conduction data sets. Follow the MIT Reactor notebook for an introductory tutorial with pyMAISE. The other notebooks focus on the models and methods instead of applying pyMAISE.

Regression

• MIT Reactor
• Reactor Physics
• Fuel Performance
• Heat Conduction
• BWR Micro Core
• HTGR Micro-Core Quadrant Power
• NEACRP C1 Rod Ejection Accident
• Critical Heat Flux (CHF) Prediction

Classification

• Binary Anomaly Detection

Creating Your Benchmark

pyMAISE aims to be a medium for AI/ML researchers to benchmark their data sets and models with standard ML methods; subsequently, we encourage you to contribute if you are interested. Here, we outline the procedure for creating a pyMAISE benchmark. Please read the Dev Guide before continuing.

1. On the pyMAISE GitHub, open a Benchmark Request under Issues. Please describe the data set you plan to use in the benchmark and link any relevant resources, such as a link to the published paper (if there is one).

2. Perform the following:

   1. Add the data to pyMAISE/datasets/.

   2. Add a load function to pyMAISE/datasets/_handler.py and include a description of the data. This load function should return xarray.DataArray.

   3. Create and run a Jupyter notebook for the benchmark in docs/source/benchmarks/.

   4. Add the relative path to the notebook to docs/source/benchmarks.rst under the toctree.

   5. Add a blurb about the data to the user guid under the preprocessing section in docs/souce/user_guide.rst.

   6. Add the load function to the toctree in docs/source/pymaise_api.rst under Data Sets.

   7. Add the nbviwer link to README.md. Ensure this link is for the develop branch.

   8. If a published paper exists for the data set, add the BibTeX citation to docs/source/data_refs.bib.

3. Once these steps are completed, you can push the benchmark, ensuring to adhere to the workflow outlined in the Dev Guide, and create a pull request.

A reviewer will ensure the validity of the benchmark and data. They will offer feedback and possible revisions for you. Thank you for contributing!