BestOfNPoolScenario
Agents scenarios · Implementation: mlsysim/engine/scenario_definitions/agents.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
Pools of independent candidates at pass_rate, and a mixed workload with the same average.
Notes
Equation: result = BestOfNPoolModel.solve(…) with the declared pass_rate, pool_sizes, easy_pass_rate inputs.
Assumptions: The inputs instantiate the assumptions documented by mlsysim.solvers.BestOfNPoolModel.solve; accessing result evaluates that solver again.
Excludes: Any extra mechanism beyond BestOfNPoolModel; this record supplies configuration and does not collect performance measurements.
References
Derivation: This configuration forwards its stored inputs to mlsysim.solvers.BestOfNPoolModel.solve. The solver contract records the equations and their provenance.
Examples
The named scenario supplies illustrative inputs; the output is calculated.
>>> from mlsysim import Scenarios
>>> case = Scenarios.IndependentCandidatePools
>>> round(float(case.result.last_gain), 3)
0.025Attributes
| Name | Description |
|---|---|
| easy_pass_rate | |
| hard_pass_rate | |
| pass_rate | |
| pool_sizes | |
| result | Modeled result from mlsysim.solvers.BestOfNPoolModel.solve using this |
pass_rate
Type: float.
Declared default: required.
pool_sizes
Type: List[int].
Declared default: required.
easy_pass_rate
Type: float.
Declared default: required.
hard_pass_rate
Type: float.
Declared default: required.
result
engine.scenario_types.BestOfNPoolScenario.result
Modeled result from mlsysim.solvers.BestOfNPoolModel.solve using this scenario’s declared inputs; see that method for units, limits, and feasibility.
Equation: Delegates the declared fields to mlsysim.solvers.BestOfNPoolModel.solve; returned quantities and feasibility follow that contract.