def _iterate(self, param: parametrization.Parametrization,
iteration: int) -> None:
"""Run the optimization.
This function actually runs the optimization.
Args:
param: Parametrization to use to run.
"""
iter_num = iteration
# Make a callback function for the optimization that logs the data.
def _callback(_) -> None:
nonlocal iter_num
# Increase iteration count.
iter_num += 1
# Log the data.
transformation_state = {
ScipyOptimizer.EVENT_ITERATION: iter_num,
ScipyOptimizer.EVENT_STATE:
ScipyOptimizer.EVENT_STATE_OPTIMIZING
}
self.logger.write(
event=transformation_state,
param=param,
monitor_list=self.monitor_lists.callback_monitors)
# Run optimization.
opt_options = self.optimization_options.to_native()
# If restoring, decrease maximum number of iterations based on
# number of iterations already performed.
if "maxiter" in opt_options:
opt_options["maxiter"] -= iteration
scipy_optimizer = optim.ScipyOptimizer(method=self.optimizer,
options=opt_options)
if self.monitor_lists.callback_monitors:
scipy_optimizer(opt=self._opt_prob,
param=param,
callback=_callback)
else:
scipy_optimizer(opt=self._opt_prob, param=param)
def fit2eps(self, eps_bg, S, eps):
from spins.invdes.problem import Fit2Eps, OptimizationProblem
import spins.invdes.optimization as optim
# make objective
obj = Fit2Eps(eps_bg, S, eps)
obj = OptimizationProblem(obj)
# optimize continuous
opt_cont = optim.ScipyOptimizer(
method='L-BFGS-B', options={
'maxiter': 200,
'maxcor': 10
})
iter_num = 0
def callback(_):
nonlocal iter_num
iter_num += 1
opt_cont(obj, self, callback=callback)
