SequentialCanaryScenario
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 canary monitor: Wald’s SPRT between acceptance p_base and a specified degraded p_degraded at targets alpha (false hold) and beta (missed degradation), on fraction of eligible tasks arriving at arrival_rate, labels label_delay late.
Notes
Equation: result evaluates the configured solver; its contract owns the governing equations.
Assumptions: The shared SPRT model compares fixed baseline/degraded probabilities at the supplied sample rate.
Excludes: Dependence, drifting alternatives, and a proof that target error rates hold for operational data.
References
Derivation: The scenario binds declared inputs to SequentialCanaryModel. Those implementations document their derivations and sources.
Examples
Execute the registered worked inputs; this is a modeled calculation.
>>> from mlsysim import Scenarios
>>> case = Scenarios.AcceptanceCanaryMonitor
>>> value = case.result
>>> print(f"{value.decision_time:.3f~P}")
15.579 hAttributes
| Name | Description |
|---|---|
| alpha | |
| arrival_rate | |
| beta | |
| fraction | |
| label_delay | |
| p_base | |
| p_degraded | |
| result | Evaluate the configured inputs with mlsysim.solvers.SequentialCanaryModel.solve. |
p_base
Type: float.
Declared default: required.
p_degraded
Type: float.
Declared default: required.
alpha
Type: float.
Declared default: required.
beta
Type: float.
Declared default: required.
arrival_rate
Type: Quantity.
Declared default: required.
fraction
Type: float.
Declared default: required.
label_delay
Type: Quantity.
Declared default: required.
result
engine.scenario_types.SequentialCanaryScenario.result
Evaluate the configured inputs with mlsysim.solvers.SequentialCanaryModel.solve.
The returned typed result retains the solver’s documented units, assumptions, and feasibility semantics; this property adds no measurement or safety claim. Equation: result = SequentialCanaryModel().solve(...) using the fields shown below.