python-model-code-generator

To generate Python modeling code, you provide a validated problem parse, a candidate method pool, and cleaned data. The tool transforms these inputs into executable Python scripts that run planned models and output structured results.

A run_summary.json file tracks traceability and reproducibility in modeling workflows. It is automatically generated alongside structured results under the results directory to record experiment outputs from baseline, main, and improved methods.

Automating end-to-end modeling requires implementing approved candidate methods as Python scripts that read cleaned data, execute models, and save outputs in an experiments/roundN/ structure. This ensures every baseline and main candidate run is tracked.

No, the Python code generator does not select models, clean raw data, review code, run final QA, or write paper sections. It strictly requires pre-validated inputs, including cleaned data and a candidate method pool, to produce runnable scripts.

Yes, generating portable artifacts for downstream reports and robustness checks is a core function. The tool outputs executable scripts, structured experiment results, and a run_summary.json specifically designed to support these downstream validations.

You need a validated problem parse, a candidate method pool, and cleaned workspace data. These enforced inputs allow the generator to automatically produce runnable Python modeling code and structured experiment tracking outputs.