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name: refactoring-03-config-reproducibility description: Use when improving run configuration, seeding, and reproducibility for Python research code.

Refactoring 03: Config and Reproducibility

Goal

Make experiments reproducible by centralizing configuration, controlling randomness, and recording run metadata.

Sequence

• Order: 03
• Previous: refactoring-02-dependencies-env
• Next: refactoring-04-data-io-validation

Workflow

• Centralize runtime parameters into a config object or file (YAML/TOML/argparse).
  • Success: All run parameters are set through a single config surface.
• Seed all RNGs (Python, NumPy, framework) and make the seed a first class parameter.
  • Success: Runs are repeatable with the same seed.
• Record metadata: config snapshot, git commit hash, and environment info with outputs.
  • Success: Each run output includes config and environment metadata.
• Create a consistent output directory layout for artifacts and metrics.
  • Success: Outputs follow a documented directory structure.
• Remove hidden global state and implicit defaults where possible.
  • Success: Behavior is driven by explicit parameters.

Guardrails

• Keep config changes backward compatible when possible.
• Do not add heavy config frameworks unless required.
• Favor explicit parameters over environment variables.