Source code for smac.multi_objective.aggregation_strategy

from __future__ import annotations

from typing import Any

import numpy as np

from smac.multi_objective.abstract_multi_objective_algorithm import (
from smac.scenario import Scenario

[docs]class MeanAggregationStrategy(AbstractMultiObjectiveAlgorithm): """A class to mean-aggregate multi-objective costs to a single cost. Parameters ---------- scenario : Scenario objective_weights : list[float] | None, defaults to None Weights for an weighted average. Must be of the same length as the number of objectives. """ def __init__( self, scenario: Scenario, objective_weights: list[float] | None = None, ): super(MeanAggregationStrategy, self).__init__() if objective_weights is not None and scenario.count_objectives() != len(objective_weights): raise ValueError("Number of objectives and number of weights must be equal.") self._objective_weights = objective_weights @property def meta(self) -> dict[str, Any]: """Returns the meta data of the created object.""" return { "name": self.__class__.__name__, "objective_weights": self._objective_weights, }
[docs] def __call__(self, values: list[float]) -> float: # noqa: D102 return float(np.average(values, axis=0, weights=self._objective_weights))