saealib.SklearnXGBSurrogate

class saealib.SklearnXGBSurrogate(**kwargs)

ベースクラス: SklearnSurrogate

XGBoost surrogate.

Convenience wrapper around SklearnSurrogate using xgboost.XGBRegressor.

パラメータ:

**kwargs (object) -- Keyword arguments forwarded to xgboost.XGBRegressor.

例外:

ImportError -- If xgboost 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

SklearnXGBSurrogate.__init__(**kwargs)

パラメータ:

kwargs (object)

戻り値の型:

None

SklearnXGBSurrogate.contract()

Return the surrogate model contract.

戻り値の型:

ComponentContract

SklearnXGBSurrogate.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) or (n_samples,).

戻り値の型:

None

SklearnXGBSurrogate.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

SklearnXGBSurrogate.predict(test_x)

Predict using the surrogate model.

パラメータ:

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

戻り値:

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

戻り値の型:

SurrogatePrediction

SklearnXGBSurrogate.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