"""
LMM-CMA-ES optimizer: CMA-ES with local polynomial regression metamodel.
"""
from ..data_classes import Bounds, PointList
from ..functions import ObjectiveFunction
from ..functions.surrogate import LocallyWeightedPolynomialRegression
from ..metamodels import ApproximateRankingMetamodel
from .cma_es import CmaEs
from .optimizer import Optimizer
[docs]
class LmmCmaEs(CmaEs):
"""
LMM-CMA-ES optimizer: CMA-ES with local polynomial regression metamodel.
"""
[docs]
def __init__(
self,
population_size: int,
sigma0: float,
polynomial_dim: int,
):
"""
Class constructor.
Args:
population_size: Size of the population.
sigma0: Starting value of the sigma,
polynomial_dim: Dimension of the polynomial regression.
"""
# Skipping super().__init__ and calling grandparent init instead.
# pylint: disable=super-init-not-called, non-parent-init-called
Optimizer.__init__(
self,
"lmm-cma-es",
population_size,
{
"sigma0": sigma0,
"polynomial_dim": polynomial_dim,
},
)
# pylint: disable=duplicate-code
[docs]
def optimize(
self,
function: ObjectiveFunction,
bounds: Bounds,
call_budget: int,
tolerance: float,
target: float = 0.0,
) -> PointList:
"""
Run a single optimization of provided objective function.
Args:
function: Objective function to optimize.
bounds: Search space of the function.
call_budget: Max number of calls to the objective function.
tolerance: Tolerance of y value to count a solution as acceptable.
target: Objective function value target, default 0.
Returns:
Results log from the optimization.
"""
num_neighbors = function.metadata.dim * (function.metadata.dim + 3) + 2
metamodel = ApproximateRankingMetamodel(
self.metadata.population_size,
self.metadata.population_size // 2,
function,
LocallyWeightedPolynomialRegression(
self.metadata.hyperparameters["polynomial_dim"], num_neighbors
),
)
es = self._spawn_cmaes(
bounds,
function.metadata.dim,
self.metadata.population_size,
self.metadata.hyperparameters["sigma0"],
)
while not self._stop(
es,
metamodel.get_log(),
self.metadata.population_size,
call_budget,
target,
tolerance,
):
solutions = PointList.from_list(es.ask())
metamodel.surrogate_function.set_covariance_matrix(es.C)
metamodel.adapt(solutions)
xy_pairs = metamodel(solutions)
x, y = xy_pairs.pairs()
es.tell(x, y)
return metamodel.get_log()