AdaptationPaybackScenario
Economics scenarios · Implementation: mlsysim/engine/scenario_definitions/economics.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
A one-time adaptation that cuts each task’s model usage, against its incremental cost.
Notes
Equation: The result property delegates to the named solver documented below; it converts declared scenario units before passing the inputs.
Assumptions: Base/adapted usage, one-time adaptation costs, and monthly task volumes are supplied.
Excludes: Forecasting quality changes, demand growth, and ongoing adaptation maintenance.
References
Derivation: the scenario composes mlsysim.solvers.AdaptationPaybackModel. Their contracts supply the underlying equations; property equations show the remaining unit conversions and accounting.
Examples
>>> from mlsysim import Scenarios
>>> scenario = Scenarios.CodeReviewAdaptationPayback
>>> value = scenario.result
>>> type(value).__name__
'AdaptationPaybackResult'Attributes
| Name | Description |
|---|---|
| adapted | |
| base | |
| data_cost | |
| evaluation_cost | |
| low_volume | |
| monthly_volume | |
| result | Return the typed accounting result from AdaptationPaybackModel using this scenario’s |
| training_cost |
base
Type: Dict[str, Any].
Declared default: required.
adapted
Type: Dict[str, Any].
Declared default: required.
data_cost
Type: Quantity.
Declared default: required.
training_cost
Type: Quantity.
Declared default: required.
evaluation_cost
Type: Quantity.
Declared default: required.
monthly_volume
Type: int.
Declared default: required.
low_volume
Type: int.
Declared default: required.
result
engine.scenario_types.AdaptationPaybackScenario.result
Return the typed accounting result from AdaptationPaybackModel using this scenario’s declared inputs; units and exclusions follow that solver contract.
Equation: AdaptationPaybackModel().solve(base, adapted, data_cost, training_cost, evaluation_cost, monthly_volume, low_volume).