saealib.RegressionSurrogate

class saealib.RegressionSurrogate

ベースクラス: 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.__init__()

RegressionSurrogate.contract()

Return the surrogate model contract.

戻り値の型:

ComponentContract

abstractmethod RegressionSurrogate.fit(train_x, train_y)

Fit the surrogate model.

パラメータ:

• 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.

戻り値の型:

None

RegressionSurrogate.post_fit(train_x, train_y, ctx=None)

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

パラメータ:

• 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.

戻り値の型:

None

abstractmethod RegressionSurrogate.predict(test_x)

Predict using the surrogate model.

パラメータ:

test_x (np.ndarray) -- Input data for prediction. shape: (n_samples, n_features)

戻り値:

prediction.value shape: (n_samples, n_obj) prediction.std shape: (n_samples, n_obj) or None

戻り値の型:

SurrogatePrediction

RegressionSurrogate.with_post_fit(fn)

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

パラメータ:

fn (callable) -- fn(train_x, train_y, ctx) -> None

戻り値:

Shallow copy with the hook registered.

戻り値の型:

Surrogate