Acquisition Functions

Acquisition Functions#

Acquisition functions determine which points to evaluate next in Bayesian optimization.

class hyperoptax.acquisition.BaseAcquisition[source]#

Bases: object

Base class for acquisition functions.

get_argmax(mean, std, seen_mask, n_points=1)[source]#
Parameters:
class hyperoptax.acquisition.UCB(kappa=2.0)[source]#

Bases: BaseAcquisition

Upper Confidence Bound acquisition function.

Parameters:

kappa (float)

__init__(kappa=2.0)[source]#
Parameters:

kappa (float)

class hyperoptax.acquisition.EI(xi=0.01)[source]#

Bases: BaseAcquisition

Expected Improvement acquisition function.

Parameters:

xi (float)

__init__(xi=0.01)[source]#
Parameters:

xi (float)

class hyperoptax.acquisition.PI(xi=0.01)[source]#

Bases: BaseAcquisition

Probability of Improvement acquisition function.

Parameters:

xi (float)

__init__(xi=0.01)[source]#
Parameters:

xi (float)

class hyperoptax.acquisition.BaseHallucination[source]#

Bases: object

Base class for Kriging Believer hallucination strategies.

Any callable with signature (mean, std, key, y_max) -> scalar can be used as a hallucination strategy — subclassing is optional.

class hyperoptax.acquisition.MeanHallucination[source]#

Bases: BaseHallucination

Classical Kriging Believer: hallucinate with GP posterior mean.

class hyperoptax.acquisition.SampleHallucination[source]#

Bases: BaseHallucination

Randomized Kriging Believer (RKB): hallucinate with a posterior sample.

arXiv 2603.01470.

class hyperoptax.acquisition.UCBHallucination(kappa=2.0)[source]#

Bases: BaseHallucination

Optimistic hallucination: mean + kappa * std.

Parameters:

kappa (float)

__init__(kappa=2.0)[source]#
Parameters:

kappa (float)

class hyperoptax.acquisition.ConstantHallucination(value=None)[source]#

Bases: BaseHallucination

Ginsbourger et al. 2010: hallucinate with y_max or a fixed constant.

If value is None, uses the current best observed value (y_max). Otherwise uses the fixed value regardless of observations.

Parameters:

value (float | None)

__init__(value=None)[source]#
Parameters:

value (float | None)

Usage Examples#

Upper Confidence Bound (UCB)#

from hyperoptax.acquisition import UCB

# Create UCB acquisition function
acq = UCB(kappa=2.0)

Expected Improvement (EI)#

from hyperoptax.acquisition import EI

# Create EI acquisition function
acq = EI(xi=0.01)
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