saealib.RegressionSurrogate#

class saealib.RegressionSurrogate[source]#

Bases: Surrogate

Marker base class for absolute-fitness (regression) surrogates.

Subclass this when train_y represents real-valued objective outputs and predict().value is a continuous fitness estimate.

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

RegressionSurrogate.contract()#

Return the surrogate model contract.

abstractmethod RegressionSurrogate.fit(train_x, train_y)#

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) For n_obj == 1, shape (n_samples,) is also accepted.

Return type:

None

RegressionSurrogate.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

abstractmethod RegressionSurrogate.predict(test_x)#

Predict using the surrogate model.

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

RegressionSurrogate.with_post_fit(fn)#

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