ExplanationLogScenario

Statistics scenarios · Implementation: mlsysim/engine/scenario_definitions/statistics.py.

Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.

Logging an explanation with every prediction: predictions_per_day for days_per_month, each a map_size_low to map_size_high attribution map (or tabular attributions of attribution_bytes_low to attribution_bytes_high per feature).

Notes

Equation: Prediction count times days gives monthly count; multiplying by map bytes gives stored volume.

Assumptions: Fixed daily prediction count, month length, and explanation sizes.

Excludes: Compression, replication, retention beyond one month, and explanation-generation compute.

References

Derivation: Prediction count times days gives monthly count; multiplying by map bytes gives stored volume. Individual property contracts identify the exact accounting expression or reusable calculation. Registry entries supply the scenario inputs; this class does not turn them into measured outcomes.

Examples

Evaluate a named scenario using its declared inputs.

>>> from mlsysim import Scenarios
>>> scenario = Scenarios.ImageExplanationLog
>>> round(float(scenario.predictions_per_month), 4)
30000000.0