saealib.SklearnSurrogate#

class saealib.SklearnSurrogate(estimator)[source]#

Bases: RegressionSurrogate

Surrogate adapter for scikit-learn compatible estimators.

Wraps any estimator that implements fit(X, y) and predict(X). Multi-objective problems are handled by fitting one cloned estimator per objective, following the same pattern as RBFSurrogate.

Parameters:: estimator (sklearn estimator) – A scikit-learn compatible regressor instance (e.g. SVR()). The estimator is cloned once per objective on the first fit call.
Raises:: ImportError – If scikit-learn is not installed.

Methods

`__init__`
`contract`	Return the surrogate model contract.
`fit`	Fit the surrogate model.
`post_fit`	Post-fit lifecycle hook; override to inject custom processing.
`predict`	Predict using the surrogate model.
`with_post_fit`	Return a copy of this surrogate with `fn` appended to the post-fit hook.

Method Details

SklearnSurrogate.__init__(estimator)[source]#

Parameters:: estimator (object)
Return type:: None

SklearnSurrogate.contract()#

Return the surrogate model contract.

Return type:: ComponentContract

SklearnSurrogate.fit(train_x, train_y)[source]#

Fit the surrogate model.

Parameters:

train_x (np.ndarray) – Training input data. shape: (n_samples, n_features)
train_y (np.ndarray) – Training output data. shape: (n_samples, n_obj) or (n_samples,).

Return type:

None

SklearnSurrogate.post_fit(train_x, train_y, ctx=None)#

Post-fit lifecycle hook; override to inject custom processing.

Parameters:

train_x (np.ndarray) – Training input data used for this fit. shape: (n_samples, n_features)
train_y (np.ndarray) – Training output data used for this fit. shape: (n_samples, n_obj)
ctx (OptimizationState or None, optional) – Current optimization context.

Return type:

None

SklearnSurrogate.predict(test_x)[source]#

Predict using the surrogate model.

Parameters:: test_x (np.ndarray) – Input data. shape: (n_samples, n_features) or (n_features,)
Returns:: prediction.value shape: (n_samples, n_obj) prediction.std is None
Return type:: SurrogatePrediction

SklearnSurrogate.with_post_fit(fn)#

Return a copy of this surrogate with fn appended to the post-fit hook.

Parameters:: fn (callable) – fn(train_x, train_y, ctx) -> None
Returns:: Shallow copy with the hook registered.
Return type:: Surrogate