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Thompson

smac.acquisition.function.thompson #

TS #

TS()

Bases: AbstractAcquisitionFunction

Thompson Sampling

Warning#

Thompson Sampling can only be used together with RandomSearch. Please do not use LocalAndSortedRandomSearch to optimize the TS acquisition function!

:math:`TS(X) ~ \mathcal{N}(\mu(\mathbf{X}),\sigma(\mathbf{X}))' Returns -TS(X) as the acquisition_function optimizer maximizes the acquisition value.

Parameters#

xi : float, defaults to 0.0 TS does not require xi here, we only wants to make it consistent with other acquisition functions.

Source code in smac/acquisition/function/abstract_acquisition_function.py 
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def __init__(self) -> None:
    self._model: AbstractModel | None = None

meta property #

meta: dict[str, Any]

Returns the meta data of the created object.

model property writable #

model: AbstractModel | None

Return the used surrogate model in the acquisition function.

__call__ #

__call__(configurations: list[Configuration]) -> 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

Source code in 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

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.

Source code in 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)