"""
KNN-CMA-ES optimizer. CMA-ES is enhanced with a KNN metamodel similar to the one from LMM-CMA-ES.
"""
from ..data_classes import Bounds, PointList
from ..functions import ObjectiveFunction
from ..functions.surrogate import KNNSurrogateObjectiveFunction
from ..metamodels import ApproximateRankingMetamodel
from .cma_es import CmaEs
from .optimizer import Optimizer
[docs]
class KnnCmaEs(CmaEs):
"""
KNN-CMA-ES optimizer. CMA-ES is enhanced with a KNN metamodel similar
to the one from LMM-CMA-ES.
"""
# pylint: disable=super-init-not-called, non-parent-init-called
[docs]
def __init__(
self,
population_size: int,
sigma0: float,
num_neighbors: int,
buffer_size: int,
):
"""
Class constructor.
Args:
population_size: Size of the population.
sigma0: Starting value of the sigma,
num_neighbors: Number of neighbors used by KNN metamodel.
buffer_size: Number of last evaluated points provided to KNN metamodel.
"""
# buffer cannot be smaller than the number of neighbors
buffer_size = max(buffer_size, num_neighbors)
# Skipping super().__init__ and calling grandparent init instead.
Optimizer.__init__(
self,
f"knn{num_neighbors}b{buffer_size}-cma-es",
population_size,
{
"sigma0": sigma0,
"num_neighbors": num_neighbors,
"buffer_size": buffer_size,
},
)
# 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.
"""
metamodel = ApproximateRankingMetamodel(
self.metadata.population_size,
self.metadata.population_size // 2,
function,
KNNSurrogateObjectiveFunction(
self.metadata.hyperparameters["num_neighbors"]
),
buffer_size=self.metadata.hyperparameters["buffer_size"],
)
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())
if (
len(metamodel.train_set)
< self.metadata.hyperparameters["num_neighbors"]
):
xy_pairs = metamodel.evaluate(solutions)
else:
metamodel.adapt(solutions)
xy_pairs = metamodel(solutions)
x, y = xy_pairs.pairs()
es.tell(x, y)
return metamodel.get_log()