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Base class for acquisition functions.
tfp.experimental.bayesopt.acquisition.AcquisitionFunction(
predictive_distribution, observations, seed=None
)
Acquisition Functions are (relatively) inexpensive functions that guide Bayesian Optimization search. Typically, their values at points will correspond to how desirable it is to evaluate the function at that point. This desirability can come in the form of improving information about the black box function (exploration) or trying to find an extrema given past evaluations (exploitation).
TFP acquisition functions are callable objects that may be instantiated with
subclass-specific parameters. This design enables expensive one-time
computation to be run in __init__
before the acquisition function is called
repeatedly, for example during optimization. The AcquisitionFunction
base
class is instantiated with a predictive distribution (typically an instance of
tfd.GaussianProcessRegressionModel
, tfd.StudentTProcessRegressionModel
, or
tfp.experimental.distributions.MultiTaskGaussianProcessRegressionModel
),
previously-observed function values, and an optional random seed. The
__call__
method evaluates the acquisition function.
Methods
__call__
__call__(
**kwargs
)
Call self as a function.