from __future__ import annotations
import warnings
from typing import TYPE_CHECKING, Any
from scipy import optimize
from ..live_plotter import LiveOptimizationPlotter
from .base import OptimizerGeneric
if TYPE_CHECKING:
from ...problem import OptimizationProblem
[docs]
class SHGO(OptimizerGeneric):
"""Simplicity Homology Global Optimization (SHGO).
Args:
problem (OptimizationProblem): The optimization problem to be solved.
Methods:
optimize(workers=-1, *args, **kwargs): Runs the SHGO algorithm.
"""
def __init__(self, problem: OptimizationProblem):
"""Initializes a new instance of the SHGO class.
Args:
problem (OptimizationProblem): The optimization problem to be
solved.
"""
super().__init__(problem)
[docs]
def optimize(
self,
workers: int = -1,
plot: bool = False,
callback: Any = None,
*args,
**kwargs,
):
"""Runs the SHGO algorithm. Note that the SHGO algorithm accepts the same
arguments as the scipy.optimize.shgo function.
Args:
workers (int): Number of parallel workers to use. Set to -1 to use
all available CPU processors. Default is -1.
plot: If True, update live plots during optimization.
callback (callable): A callable called after each iteration.
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
Returns:
result (OptimizeResult): The optimization result.
Raises:
ValueError: If any variable in the problem does not have bounds.
"""
x0 = [var.value for var in self.problem.variables]
self._x.append(x0)
bounds = tuple([var.bounds for var in self.problem.variables])
if any(None in bound for bound in bounds):
raise ValueError("SHGO requires all variables have bounds.")
live_plotter: LiveOptimizationPlotter | None = None
if plot:
live_plotter = LiveOptimizationPlotter(self)
live_plotter.initialize()
def _wrapped_callback(*args: Any, **kwargs: Any) -> None:
if callback is not None:
callback(*args, **kwargs)
if live_plotter is not None:
live_plotter.update()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
result = optimize.shgo(
self._fun,
bounds=bounds,
workers=workers,
callback=_wrapped_callback,
**kwargs,
)
# The last function evaluation is not necessarily the lowest.
# Update all lens variables to their optimized values
for idvar, var in enumerate(self.problem.variables):
var.update(result.x[idvar])
self.problem.update_optics()
if live_plotter is not None:
live_plotter.update()
live_plotter.finalize()
return result