ProbeVsAnswerScenario
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.
A generated explanation of answer_tokens against a prepared test of probe_time.
Notes
Equation: result = ProbeVsAnswerModel.solve(…) with the declared floors, answer_tokens, probe_time inputs.
Assumptions: The inputs instantiate the assumptions documented by mlsysim.solvers.ProbeVsAnswerModel.solve; accessing result evaluates that solver again.
Excludes: Any extra mechanism beyond ProbeVsAnswerModel; this record supplies configuration and does not collect performance measurements.
References
Derivation: This configuration forwards its stored inputs to mlsysim.solvers.ProbeVsAnswerModel.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.ProbeVsGeneratedExplanation
>>> round(float(case.result.token_time.to('s').magnitude), 3)
0.021Attributes
| Name | Description |
|---|---|
| answer_tokens | |
| floors | |
| output_price | |
| probe_time | |
| result | Modeled result from mlsysim.solvers.ProbeVsAnswerModel.solve using this |
floors
Type: DecodeScenario.
Declared default: required.
answer_tokens
Type: int.
Declared default: required.
probe_time
Type: Quantity.
Declared default: required.
output_price
Type: Quantity.
Declared default: required.
result
engine.scenario_types.ProbeVsAnswerScenario.result
Modeled result from mlsysim.solvers.ProbeVsAnswerModel.solve using this scenario’s declared inputs; see that method for units, limits, and feasibility.
Equation: Delegates the declared fields to mlsysim.solvers.ProbeVsAnswerModel.solve; returned quantities and feasibility follow that contract.