jijzepttools.blackbox_optimization.surrogate_model.interface

This module provides a unified interface for surrogate models used in blackbox optimization. It includes parameter classes, abstract interface, and concrete implementations for Factorization Machine (FM) and Bayesian Optimization of Combinatorial Structures (BOCS).

Classes:

• SurrogateModelParams: Base class for all surrogate model parameters

• FMParams: Parameters for Factorization Machine

• BOCSParams: Parameters for BOCS

• SurrogateModel: Abstract base class for surrogate models

• FMSurrogateModel: Factorization Machine implementation

• BOCSSurrogateModel: BOCS implementation

Classes

SurrogateModelParams	Base class for all surrogate model parameters.
SurrogateModel	Abstract base class for surrogate models in blackbox optimization.

Module Contents

class SurrogateModelParams

Base class for all surrogate model parameters.

This class serves as a base for parameter classes used by different surrogate models (e.g., FMParams, BOCSParams). Each concrete implementation should define the specific parameters needed for that model type.

This provides a unified interface for parameter classes of different surrogate models, allowing for type-safe and extensible parameter handling.

class SurrogateModel

Bases: abc.ABC

Abstract base class for surrogate models in blackbox optimization.

This interface provides a unified way to interact with different surrogate models such as Factorization Machine and BOCS. The typical workflow is: 1. Instantiate a concrete surrogate model with parameters 2. Fit the model to observed data using fit() 3. Create optimization objective using create_optimization_model()

abstract fit(x: numpy.ndarray, y: numpy.ndarray) → None

Fit the surrogate model to observed data.

Parameters:

• x (np.ndarray, shape (n_samples, n_features)) – Input features from blackbox function evaluations.

• y (np.ndarray, shape (n_samples,)) – Target values for a single objective.

abstract surrogate_to_ommx_objective(decision_variables: list[ommx.v1.DecisionVariable])

Create an OMMX expression representing the surrogate model objective.

Parameters:

decision_variables (list[ommx.v1.DecisionVariable]) – List of decision variables from the OMMX instance.