def _do_optimize_delayed_update(self,
fun,
max_evals,
workers=mp.cpu_count()):
self.fun = fun
self.max_evals = max_evals
evaluator = Evaluator(self.fun)
evaluator.start(workers)
evals_x = {}
self.iterations = 0
self.evals = 0
self.p = 0
self.improves = deque()
for _ in range(workers): # fill queue with initial population
p, x = self.ask_one()
evaluator.pipe[0].send((self.evals, x))
evals_x[self.evals] = p, x # store x
self.evals += 1
while True: # read from pipe, tell de and create new x
evals, y = evaluator.pipe[0].recv()
p, x = evals_x[evals] # retrieve evaluated x
del evals_x[evals]
self.tell_one(p, y, x) # tell evaluated x
if self.stop != 0 or self.evals >= self.max_evals:
break # shutdown worker if stop criteria met
p, x = self.ask_one() # create new x
evaluator.pipe[0].send((self.evals, x))
evals_x[self.evals] = p, x # store x
self.evals += 1
evaluator.stop()
return self.best_x, self.best_value, self.evals, self.iterations, self.stop
def do_optimize_delayed_update(self, fun, workers=mp.cpu_count()):
evaluator = Evaluator(fun)
evaluator.start(workers)
evals_x = {}
self.evals = 0
for _ in range(workers): # fill queue
x = self.ask_one()
evaluator.pipe[0].send((self.evals, x))
evals_x[self.evals] = x # store x
self.evals += 1
while True: # read from pipe, tell es and create new x
evals, y = evaluator.pipe[0].recv()
x = evals_x[evals] # retrieve evaluated x
del evals_x[evals]
stop = self.tell_one(y, x) # tell evaluated x
if stop != 0 or self.evals >= self.max_evaluations:
break # shutdown worker if stop criteria met
x = self.ask_one() # create new x
evaluator.pipe[0].send((self.evals, x))
evals_x[self.evals] = x # store x
self.evals += 1
evaluator.stop()
return self.best_x, self.best_value, evals, self.iterations, self.stop
class parallel(object):
"""Convert an objective function for parallel execution for cmaes.minimize.
Parameters
----------
fun : objective function mapping a list of float arguments to a float value.
represents a function mapping a list of lists of float arguments to a list of float values
by applying the input function using parallel processes. stop needs to be called to avoid
a resource leak"""
def __init__(self, fun, workers=mp.cpu_count()):
self.evaluator = Evaluator(fun)
self.evaluator.start(workers)
def __call__(self, xs):
return eval_parallel(xs, self.evaluator)
def stop(self):
self.evaluator.stop()