PrunedSearchTreeScenario
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 search tree of branching children per node whose step check rejects rejection_rate.
Notes
Equation: result = PrunedSearchTreeModel.solve(…) with the declared branching, rejection_rate, depth inputs.
Assumptions: The inputs instantiate the assumptions documented by mlsysim.solvers.PrunedSearchTreeModel.solve; accessing result evaluates that solver again.
Excludes: Any extra mechanism beyond PrunedSearchTreeModel; this record supplies configuration and does not collect performance measurements.
References
Derivation: This configuration forwards its stored inputs to mlsysim.solvers.PrunedSearchTreeModel.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.StepCheckedSearchTree
>>> round(float(case.result.effective_branching), 3)
1.6Attributes
| Name | Description |
|---|---|
| branching | |
| depth | |
| rejection_rate | |
| result | Modeled result from mlsysim.solvers.PrunedSearchTreeModel.solve using this |
| wide_branching | |
| wide_depth |
branching
Type: int.
Declared default: required.
rejection_rate
Type: float.
Declared default: required.
depth
Type: int.
Declared default: required.
wide_branching
Type: Optional[int].
Declared default: None.
wide_depth
Type: Optional[int].
Declared default: None.
result
engine.scenario_types.PrunedSearchTreeScenario.result
Modeled result from mlsysim.solvers.PrunedSearchTreeModel.solve using this scenario’s declared inputs; see that method for units, limits, and feasibility.
Equation: Delegates the declared fields to mlsysim.solvers.PrunedSearchTreeModel.solve; returned quantities and feasibility follow that contract.