Source code for smac.multi_objective.parego

from __future__ import annotations

from typing import Any

import numpy as np

from smac.multi_objective.abstract_multi_objective_algorithm import (
    AbstractMultiObjectiveAlgorithm,
)
from smac.scenario import Scenario


[docs]class ParEGO(AbstractMultiObjectiveAlgorithm): """ParEGO implementation based on https://www.cs.bham.ac.uk/~jdk/UKCI-2015.pdf. Parameters ---------- scenario : Scenario rho : float, defaults to 0.05 A small positive value. seed : int | None, defaults to None """ def __init__( self, scenario: Scenario, rho: float = 0.05, seed: int | None = None, ): super(ParEGO, self).__init__() if seed is None: seed = scenario.seed self._n_objectives = scenario.count_objectives() self._seed = seed self._rng = np.random.RandomState(seed) self._rho = rho # Will be set on starting an SMBO iteration self._theta: np.ndarray | None = None @property def meta(self) -> dict[str, Any]: # noqa: D102 meta = super().meta meta.update( { "name": self.__class__.__name__, "rho": self._rho, "seed": self._seed, } ) return meta
[docs] def update_on_iteration_start(self) -> None: # noqa: D102 self._theta = self._rng.rand(self._n_objectives) # Normalize so that all theta values sum up to 1 self._theta = self._theta / (np.sum(self._theta) + 1e-10)
[docs] def __call__(self, values: list[float]) -> float: # noqa: D102 # Weight the values if self._theta is None: raise ValueError("Iteration not yet initalized; Call `update_on_iteration_start()` first") theta_f = self._theta * values return float(np.max(theta_f, axis=0) + self._rho * np.sum(theta_f, axis=0))