LossNormalizationScenario
Training scenarios · Implementation: mlsysim/engine/scenario_definitions/training.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
A long trajectory split into focus_pieces records, batched with other_examples short ones.
Notes
Equation: Composition of mlsysim.solvers.LossNormalizationModel.solve.
Assumptions: Inputs are routed to LossNormalizationModel.solve; their documented regimes and units apply to this configuration.
Excludes: Additional mechanisms beyond the linked solver contracts; the scenario does not collect measurements.
References
Derivation: This scenario binds named inputs to LossNormalizationModel. The linked solver contracts own equations, sources, and result-unit definitions.
Examples
Evaluate the named configuration and display a modeled result.
>>> from mlsysim import Scenarios
>>> case = Scenarios.RepairTraceWithLookupWeighting
>>> value = case.result.focus_token_share
>>> print(f"{value:.3g}")
0.97Attributes
| Name | Description |
|---|---|
| focus_pieces | |
| focus_targets | |
| other_examples | |
| other_targets | |
| result | Evaluate the stated loss normalization case through LossNormalizationModel.solve; returns its typed result and quantity units. |
focus_targets
Type: int.
Declared default: required.
other_examples
Type: int.
Declared default: required.
other_targets
Type: int.
Declared default: required.
focus_pieces
Type: int.
Declared default: 1.
result
engine.scenario_types.LossNormalizationScenario.result
Evaluate the stated loss normalization case through LossNormalizationModel.solve; returns its typed result and quantity units.
Calculation: mlsysim.solvers.LossNormalizationModel.solve.