def bbopt_actor(env):
_coconut_match_to_0 = env
_coconut_match_check_0 = False
if _coconut.isinstance(_coconut_match_to_0, _coconut.abc.Mapping):
_coconut_match_temp_0 = _coconut_match_to_0.get(bb_name, _coconut_sentinel)
if _coconut_match_temp_0 is not _coconut_sentinel:
bb = _coconut_match_temp_0
_coconut_match_check_0 = True
if _coconut_match_check_0 and not (bb is not None):
_coconut_match_check_0 = False
if _coconut_match_check_0:
if isinstance(util_func, Str):
util = env[util_func]
else:
util = util_func(env)
bb.maximize(util)
else:
bb = BlackBoxOptimizer(file=file, tag=env["game"].name + "_" + name)
env[bb_name] = bb
bb.run(alg=alg if not env["game"].final_step else None)
if print_chosen_alg:
chosen_alg = bb.get_current_run()["values"].get(meta_opt_alg_var)
if chosen_alg is not None:
print("\nusing BBopt alg =", chosen_alg)
return tunable_actor(bb, env)
def __init__(self):
super().__init__(create_parser(usage))
self.bb = BlackBoxOptimizer(file=self.args.trials_name)
if not self.test:
data = TrainData.from_both(self.args.tags_file,
self.args.tags_folder, self.args.folder)
_, self.test = data.load(False, True)
from keras.callbacks import ModelCheckpoint
for i in list(self.callbacks):
if isinstance(i, ModelCheckpoint):
self.callbacks.remove(i)
"""
Example of using BBopt with conditional parameters and randomness
while leveraging the bayes-skopt backend.
To run this example, just run:
> bbopt ./bask_example.py
"""
# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__)
if __name__ == "__main__":
bb.run(alg="bask_gaussian_process")
# We set the x parameter conditional on the use_high parameter and add randomness.
import random
use_high = bb.randbool("use high", guess=False)
assert isinstance(use_high, bool), type(use_high)
if use_high:
x = bb.randrange("x high", 10, 20) * random.random()
else:
x = bb.randrange("x low", 10) * random.random()
# We set x as the thing we want to optimize.
bb.maximize(x)
# Finally, we'll print out the value we used for debugging purposes.
if __name__ == "__main__":
print(repr(x))
"""
Simple example using json instead of pickle for enhanced cross-platform compatibility.
To run this example, just run:
> bbopt ./skopt_example.py
"""
# BBopt setup:
from bbopt import BlackBoxOptimizer
bb = BlackBoxOptimizer(file=__file__, protocol="json")
if __name__ == "__main__":
bb.run()
# Set up log uniform and log normal parameters.
x0 = bb.loguniform("x0", 1, 10, guess=5)
x1 = bb.lognormvariate("x1", 0, 1, guess=1)
# Set the goal to be the sum.
y = x0 + x1
bb.minimize(y)
# Finally, we'll print out the value we used for debugging purposes.
if __name__ == "__main__":
print(repr(y))