desdeo_emo.surrogatemodels.Problem

Module Contents

Classes

surrogateProblem

The base class for the problems.
class desdeo_emo.surrogatemodels.Problem.surrogateProblem(performance_evaluator)[source]

Bases: desdeo_problem.problem.ProblemBase

The base class for the problems.

All other problem classes should be derived from this.

nadir

Nadir values for the problem, initiated = None

Type:

np.ndarray

ideal

Ideal values for the problem, initiated = None

Type:

np.ndarray

nadir_fitness

Fitness values for nadir, initiated = None

Type:

np.ndarray

ideal_fitness

Fitness values for ideal, initiated = None

Type:

np.ndarray

__n_of_objectives

Number of objectives, initiated = 0

Type:

int

__n_of_variables

Number of variables, initiated = 0

Type:

int

__decision_vectors

Array of decision variable vectors, initiated = None

Type:

np.ndarray

__objective_vectors

Array of objective variable vectors, initiated = None

Type:

np.ndarray

evaluate(model_parameters, use_surrogates=False)[source]

Abstract method to evaluate problem.

Evaluates the problem using an ensemble of input vectors. Uses surrogate models if available. Otherwise, it uses the true evaluator.

Parameters:

  • decision_vectors (np.ndarray) – An array of decision variable

  • vectors. (input) –

  • use_surrogate (bool) – A bool to control whether to use the true, potentially

  • objectives. (expensive function or a surrogate model to evaluate the) –

Returns:

Dict with the following keys:
’objectives’ (np.ndarray): The objective function values for each input

vector.

’constraints’ (Union[np.ndarray, None]): The constraint values of the

problem corresponding each input vector.

’fitness’ (np.ndarray): Equal to objective values if objective is to be

minimized. Multiplied by (-1) if objective to be maximized.

’uncertainity’ (Union[np.ndarray, None]): The uncertainity in the

objective values.

Return type:

(Dict)

evaluate_constraint_values()[source]

Abstract method to evaluate constraint values.

Evaluate just the constraint function values using the attributes decision_vectors and objective_vectors

Note

Currently not supported by ScalarMOProblem

get_variable_bounds()[source]

Abstract method to get variable bounds

get_objective_names()[source]