def prepare_optimization(self):
# Wait until (presumably) all workers are awake and ready
# to avoid unfair distribution of tasks.
time.sleep(1)
printf("Waiting for initial connection")
self.__fetch_constraints()
printf("Received initial data from workers")
def prepare_optimization(self):
# Wait until (presumably) all workers are awake and ready
# to avoid unfair distribution of tasks.
time.sleep(1)
printf("Waiting for initial connection")
self.__fetch_constraints()
printf("Received initial data from workers")
def __network_mode(self, workers):
printf("Starting optimization with network workers")
if not workers:
raise RuntimeError("You have to specify a list of remote"
" worker addresses")
# Connect to workers with ZeroMQ.
self.cc = NetworkClientComm(addresses=parse_worker_addresses(workers))
def __network_mode(self, workers):
printf("Starting optimization with network workers")
if not workers:
raise RuntimeError("You have to specify a list of remote"
" worker addresses")
# Connect to workers with ZeroMQ.
self.cc = NetworkClientComm(addresses=parse_worker_addresses(workers))
def run(self):
u"Fire the Genetic Algorithm Engine."
printf("Starting GA: %d generations of %d individuals" % (
self.generations, self.size))
self.timer.start()
self.ga.evolve()
self.__post_run()
# Return the best parameters found.
return list(self.ga.bestIndividual().getInternalList())
def run(self):
printf("Starting LM: %d iterations" % self.max_iter)
objective = LevmarObjective(self.comm)
self.timer.start()
output = levmar(objective, p0=self.p0, y=self.y, bounds=self.bounds,
maxit=self.max_iter, cdif=self.central, eps1=1e-15, eps2=1e-15,
eps3=1e-20, full_output=True, breakf=self.__breakf)
self.__post_run(output)
return list(output.p)
def __breakf(self, i, maxit, p, error):
pstr = "Iteration %d of %d, ||error|| %g" % (i, maxit, error)
self.timer.lap()
if i > 0:
iter_left = maxit - i
mean_time = self.timer.time_so_far / i
pstr += "\t(est. time to end=%d s.)" % (iter_left * mean_time)
printf(pstr)
if check_stop_flag(self.stop_flag):
return 1
def stats_step_callback(ga):
u"Function run every generation printing erorr and time stats"
pstr = "Step %d out of %d: " % (ga.currentGeneration, ga.nGenerations)
pstr += "err [%%] = %g" % (ga.bestIndividual().score * 100.)
timer = ga.getParam("timer")
timer.lap()
if ga.currentGeneration > 0:
gens_left = ga.nGenerations - ga.currentGeneration
mean_time = timer.time_so_far / ga.currentGeneration
pstr += "\t(est. time to end=%d s.)" % (gens_left * mean_time)
printf(pstr)
def stats_step_callback(ga):
u"Function run every generation printing erorr and time stats"
pstr = "Step %d out of %d: " % (ga.currentGeneration, ga.nGenerations)
pstr += "err [%%] = %g" % (ga.bestIndividual().score * 100.)
timer = ga.getParam("timer")
timer.lap()
if ga.currentGeneration > 0:
gens_left = ga.nGenerations - ga.currentGeneration
mean_time = timer.time_so_far / ga.currentGeneration
pstr += "\t(est. time to end=%d s.)" % (gens_left * mean_time)
printf(pstr)
def __local_mode(self, workers, xargs, evaluator_path):
if evaluator_path is None:
raise ValueError("You have to specify a path to the evaluator"
" while running in local mode.")
printf("Starting optimization with local workers (%d)" % workers)
# Prepare the ZeroMQ communication layer.
self.cc = LocalClientComm()
# Arguments to be passed to evaluator processes.
evaluator_args = xargs + ["-local-mode", "-local-pull-address",
self.cc.push_addr, "-local-publish-address", self.cc.sub_addr]
# Launch worker processes.
spawn_workers(workers, evaluator_path, evaluator_args)
def __local_mode(self, workers, xargs, evaluator_path):
if evaluator_path is None:
raise ValueError("You have to specify a path to the evaluator"
" while running in local mode.")
printf("Starting optimization with local workers (%d)" % workers)
# Prepare the ZeroMQ communication layer.
self.cc = LocalClientComm()
# Arguments to be passed to evaluator processes.
evaluator_args = xargs + [
"-local-mode", "-local-pull-address", self.cc.push_addr,
"-local-publish-address", self.cc.sub_addr
]
# Launch worker processes.
spawn_workers(workers, evaluator_path, evaluator_args)
def __post_run(self, lm_output):
run_time = self.timer.stop()
info = lm_output.info
printf("Iteration %d of %d, ||error|| %g" % (info[2], self.max_iter,
info[1][0]))
print "LM finished in %g s, with reason: %s" % (run_time, info[3])
def display_info(self, parameters):
u"Asks evaluator for parameters description and displays it."
self.cc.get_stats(parameters, "2%d" % id(parameters))
response = self.cc.resp_stats("2%d" % id(parameters), wait=True)
printf("Statistics for given parameters:")
printf(response["stats"])
def display_info(self, parameters):
u"Asks evaluator for parameters description and displays it."
self.cc.get_stats(parameters, "2%d" % id(parameters))
response = self.cc.resp_stats("2%d" % id(parameters), wait=True)
printf("Statistics for given parameters:")
printf(response["stats"])
