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集成采集函数

smac.acquisition.function.integrated_acquisition_function #

IntegratedAcquisitionFunction #

IntegratedAcquisitionFunction(
    acquisition_function: AbstractAcquisitionFunction,
)

基类: AbstractAcquisitionFunction

通过对模型超参数进行边际化来计算集成采集函数

有关更多详细信息，请参阅 Jasper Snoek 等人的论文“机器学习算法的实用贝叶斯优化”（papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf）。

参数#

acquisition_function : AbstractAcquisitionFunction 要集成的采集函数。

属性#

_acquisition_function : AbstractAcquisitionFunction 要集成的采集函数。 _functions: list[AbstractAcquisitionFunction] 包含 n（n = 模型数量）个采集函数副本。 _eta : float 当前最优函数值。

源代码位于 smac/acquisition/function/integrated_acquisition_function.py 
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def __init__(self, acquisition_function: AbstractAcquisitionFunction) -> None:
    super().__init__()
    self._acquisition_function: AbstractAcquisitionFunction = acquisition_function
    self._functions: list[AbstractAcquisitionFunction] = []
    self._eta: float | None = None

model property writable #

model: AbstractModel | None

返回采集函数中使用的代理模型。

__call__ #

__call__(configurations: list[Configuration]) -> ndarray

计算给定配置的采集值。

参数#

configurations : list[Configuration] 应评估采集函数的配置。

返回值#

np.ndarray [N, 1] X 的采集值

源代码位于 smac/acquisition/function/abstract_acquisition_function.py 
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def __call__(self, configurations: list[Configuration]) -> np.ndarray:
    """Compute the acquisition value for a given configuration.

    Parameters
    ----------
    configurations : list[Configuration]
        The configurations where the acquisition function should be evaluated.

    Returns
    -------
    np.ndarray [N, 1]
        Acquisition values for X
    """
    X = convert_configurations_to_array(configurations)
    if len(X.shape) == 1:
        X = X[np.newaxis, :]

    acq = self._compute(X)
    if np.any(np.isnan(acq)):
        idx = np.where(np.isnan(acq))[0]
        acq[idx, :] = -np.finfo(float).max

    return acq

update #

update(model: AbstractModel, **kwargs: Any) -> None

更新计算所需的采集函数属性。

此方法将在拟合模型后、最大化采集函数之前调用。例如，EI 使用它来更新当前的 fmin。默认实现仅更新已有的采集函数属性。

调用 _update 来更新采集函数属性。

参数#

model : AbstractModel 用于拟合数据的模型。 kwargs : Any 更新特定采集函数的附加参数。

源代码位于 smac/acquisition/function/abstract_acquisition_function.py 
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def update(self, model: AbstractModel, **kwargs: Any) -> None:
    """Update the acquisition function attributes required for calculation.

    This method will be called after fitting the model, but before maximizing the acquisition
    function. As an examples, EI uses it to update the current fmin. The default implementation only updates the
    attributes of the acquisition function which are already present.

    Calls `_update` to update the acquisition function attributes.

    Parameters
    ----------
    model : AbstractModel
        The model which was used to fit the data.
    kwargs : Any
        Additional arguments to update the specific acquisition function.
    """
    self.model = model
    self._update(**kwargs)