Acquisition¶

Acquisition functions for each backend.

botorch_acquisition ¶

BoTorchAcquisition ¶

BoTorchAcquisition(search_space, model=None, acq_func='ucb', maximize=True, random_state=42, acq_func_kwargs=None, batch_size=1, ref_point=None, directions=None, objective_names=None, outcome_constraints=None)

Bases: BaseAcquisition

Acquisition function implementation using BoTorch.

Supported acquisition functions: - 'ei': Expected Improvement - 'logei': Log Expected Improvement (numerically stable) - 'pi': Probability of Improvement - 'logpi': Log Probability of Improvement (numerically stable) - 'ucb': Upper Confidence Bound - 'qei': Batch Expected Improvement (for q>1) - 'qucb': Batch Upper Confidence Bound (for q>1) - 'qipv' or 'qnipv': q-Negative Integrated Posterior Variance (exploratory)

Initialize the BoTorch acquisition function.

Parameters:

Name	Description	Default
`search_space`	The search space (SearchSpace object)	required
`model`	A trained model (BoTorchModel)	`None`
`acq_func`	Acquisition function type (see class docstring for options)	`'ucb'`
`maximize`	Whether to maximize (True) or minimize (False) the objective	`True`
`random_state`	Random state for reproducibility	`42`
`acq_func_kwargs`	Dictionary of additional arguments for the acquisition function	`None`
`batch_size`	Number of points to select at once (q)	`1`
`ref_point`	Reference point for MOBO hypervolume (list of floats)	`None`
`directions`	Per-objective direction list ('maximize'/'minimize')	`None`
`objective_names`	List of objective names (for multi-objective)	`None`
`outcome_constraints`	List of outcome constraint callables for MOBO	`None`

Raises:

Type	Description
`ValueError`	If acq_func is not a valid acquisition function name

update_model ¶

update_model(model)

Update the underlying model.

select_next ¶

select_next(candidate_points=None, context_values=None, hold=None, observed_values=None)

Suggest the next experiment point(s) using BoTorch optimization.

Parameters:

Name	Description	Default
`candidate_points`	Candidate points to evaluate (optional). Cannot be combined with `hold`: evaluating given candidates never moves a variable, so a held value would be silently ignored. Passing both raises `ValueError`.	`None`
`context_values`	Older name for `observed_values`.	`None`
`hold`	Dict of controllable variable name -> value to keep fixed for this call. The returned point carries the held values.	`None`
`observed_values`	Dict of observed variable name -> measured value. Required when observed variables are registered.	`None`

Returns:

Type	Description
	Dictionary with the selected point or list of points

update ¶

update(X=None, y=None)

Update the acquisition function with new observations.

Parameters:

Name	Type	Description	Default
`X`		Features of new observations	`None`
`y`		Target values of new observations	`None`

find_optimum ¶

find_optimum(model=None, maximize=None, random_state=None)

Find the point where the model predicts the optimal value.

This uses the same approach as regret plot predictions: generate a grid in the original variable space, predict using the model's standard pipeline, and find the argmax/argmin. This ensures categorical variables are handled correctly through proper encoding/decoding.

skopt_acquisition ¶

SkoptAcquisition ¶

SkoptAcquisition(search_space, model=None, acq_func='ei', maximize=True, random_state=42, acq_func_kwargs=None)

Bases: BaseAcquisition

Simple acquisition function implementation using scikit-optimize (skopt).

Supported acquisition functions: - 'ei' or 'EI': Expected Improvement - 'pi' or 'PI': Probability of Improvement
- 'ucb' or 'UCB': Upper Confidence Bound (mapped to LCB in skopt) - 'gp_hedge': GP-Hedge (portfolio of acquisition functions)

Initialize the acquisition function.

Parameters:

Name	Description	Default
`search_space`	The search space (SearchSpace object or list of skopt dimensions)	required
`model`	A trained model (SklearnModel or compatible)	`None`
`acq_func`	Acquisition function ('ei', 'pi', 'ucb', or 'gp_hedge')	`'ei'`
`maximize`	Whether to maximize (True) or minimize (False) the objective	`True`
`random_state`	Random state for reproducibility	`42`
`acq_func_kwargs`	Dictionary of additional arguments for the acquisition function	`None`

Raises:

Type	Description
`ValueError`	If acq_func is not a valid acquisition function name

update ¶

update(X, y, **kwargs)

Update the optimizer with new observations.

select_next ¶

select_next(candidate_points=None, **kwargs)

Suggest the next experiment point.

find_optimum ¶

find_optimum(model, maximize=True, random_state=42)

Find the point where the model predicts the optimal value.

Parameters:

Name	Description	Default
`model`	Trained model with predict method	required
`maximize`	Whether to maximize (True) or minimize (False) the objective	`True`
`random_state`	Random seed for reproducibility	`42`

Returns:

Name	Type	Description
`dict`		Contains 'x_opt' (optimal point), 'value' (predicted value), 'std' (standard deviation at optimum)