plot_optimizer(opt, x, fx)
#############################################################################
# Changing kappa and xi on the go
# -------------------------------
# If we want to change kappa or ki at any point during our optimization
# process we just replace opt.acq_func_kwargs. Remember to call
# `opt.update_next()` after the change, in order for next point to be
# recalculated.
acq_func_kwargs = {"kappa": 0}
#############################################################################
opt = Optimizer([(-2.0, 2.0)],
"GP",
n_initial_points=1,
acq_func="LCB",
acq_optimizer="sampling",
acq_func_kwargs=acq_func_kwargs)
#############################################################################
opt.acq_func_kwargs
#############################################################################
opt.run(objective, n_iter=20)
plot_optimizer(opt, x, fx)
#############################################################################
acq_func_kwargs = {"kappa": 100000}
#############################################################################
opt.acq_func_kwargs = acq_func_kwargs
opt.update_next()
#############################################################################
opt.run(objective, n_iter=20)
plot_optimizer(opt, x, fx)
if __name__ == '__main__':
optimizer = Optimizer(dimensions=dim_vals,
n_jobs=12,
random_state=1,
base_estimator='RF',
n_initial_points=n_initial,
initial_point_generator='random')
with mp.Pool(n_procs) as p:
#with DebugPool() as p:
jobs = []
try:
for i in range(n_eval):
args = optimizer.ask()
optimizer.update_next()
print('#######')
print(f'Evaluation #{i}')
print(args)
print('#######')
jobs.append(Job(p.apply_async(objective, args), args))
for job in jobs:
if job.result.ready():
optimizer.tell(job.args, job.result.get())
jobs.remove(job)
while sum(map(not_ready, jobs)) >= n_procs:
time.sleep(0.5)
for job in jobs:
optimizer.tell(job.args, job.result.get())
except KeyboardInterrupt:
pass
