saealib.SurrogateManager#

class saealib.SurrogateManager[source]#

Bases: ABC

Abstract base class for surrogate managers.

A SurrogateManager is narrowed to training and prediction management It coordinates TrainingSet construction, model fitting, and prediction, and returns a SurrogatePrediction for callers.

last_accuracy#

Accuracy metrics computed after the most recent fit() call. None until the first fit or when no evaluator is configured.

Type:: SurrogateAccuracy or None

Methods

`__init__`
`contract`	Return the surrogate-manager contract.
`fit`	Pre-fit the surrogate on the archive.
`on_generation_end`	End-of-generation hook; override to update internal state.
`predict`	Predict candidate solutions using the surrogate model.
`with_on_generation_end`	Return a copy of this manager with `fn` appended to the hook.

Method Details

SurrogateManager.__init__()#

SurrogateManager.contract()[source]#

Return the surrogate-manager contract.

Return type:: ComponentContract

SurrogateManager.fit(archive, ctx=None)[source]#

Pre-fit the surrogate on the archive.

Call once before a sequence of predict(..., refit=False) calls when the archive does not change between calls (e.g. the surrogate-only inner loop of GenerationBasedStrategy). The default implementation is a no-op; override in managers that maintain a fitted surrogate model.

Parameters:

archive (Archive) – Archive of evaluated solutions used for surrogate training.
ctx (OptimizationState or None, optional) – Current optimization context.

Return type:

None

SurrogateManager.on_generation_end(gen, archive, ctx=None)[source]#

End-of-generation hook; override to update internal state.

Parameters:

gen (int)
archive (Archive)
ctx (OptimizationState | None)

Return type:

None

abstractmethod SurrogateManager.predict(candidates_x, archive, ctx=None, *, refit=True)[source]#

Predict candidate solutions using the surrogate model.

Parameters:

candidates_x (np.ndarray) – Candidate design variable matrix. shape: (n_candidates, dim)
archive (Archive) – Archive of evaluated solutions used for surrogate training.
ctx (OptimizationState or None, optional) – Current optimization context. Passed to TrainingSet.build for strategies that require comparator or population access.
refit (bool, optional) – If True (default), fit the surrogate before predicting. Pass False after an explicit fit() call to skip redundant re-fitting when the archive has not changed.

Returns:

One batched prediction covering every row of candidates_x. Acquisition scoring is not performed here — pass the result to an AcquisitionFunction.

Return type:

SurrogatePrediction

SurrogateManager.with_on_generation_end(fn)[source]#

Return a copy of this manager with fn appended to the hook.

Parameters:: fn (callable) – fn(gen, archive, ctx) -> None
Returns:: Shallow copy with the hook registered.
Return type:: SurrogateManager