saealib.SklearnLGBMSurrogate#

class saealib.SklearnLGBMSurrogate(**kwargs)[source]#

LightGBM surrogate.

Convenience wrapper around SklearnSurrogate using lightgbm.LGBMRegressor.

Parameters:: **kwargs (object) – Keyword arguments forwarded to lightgbm.LGBMRegressor.
Raises:: ImportError – If lightgbm 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

SklearnLGBMSurrogate.__init__(**kwargs)[source]#

SklearnLGBMSurrogate.contract()#

Return the surrogate model contract.

SklearnLGBMSurrogate.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) or (n_samples,).

Return type:

None

SklearnLGBMSurrogate.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

SklearnLGBMSurrogate.predict(test_x)#

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

SklearnLGBMSurrogate.with_post_fit(fn)#

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