Configuration Values
Configuration values describe how an experiment should run.
Examples:
- seed;
- number of trials;
- sample size;
- train-test split size;
- output path.
A dataclass can keep these values together.
Pass configuration into the experiment
Configuration controls the run
Ready to run.
The function does not hide its important choices. They are fields on
SimulationConfig.
Configuration is not result
Keep configuration and output separate:
The config says what to do. The result says what happened.
Exercise: Configuration field
Which configuration field makes a randomized run repeatable?
Answer it first, then check.
Hint
Look for the field passed to np.random.default_rng(...).
Solution
The field is seed. It records the value used to initialize the random number
generator.