def tune_meta_learner():
cs = build_configspace()
def_value = obj_function(cs.get_default_configuration())
print("Default Value: %.2f" % (def_value))
bo = BayesianOptimization(obj_function, cs, max_runs=50, time_limit_per_trial=1200)
bo.run()
inc_value = bo.get_incumbent()
config = inc_value[0][0]
print('Best hyperparameter config found', config)
return config
def tune_meta_learner():
cs = build_configspace()
def_value = objective_function(cs.get_default_configuration())
print("Default Value: %.2f" % (def_value))
bo = BayesianOptimization(objective_function, cs, max_runs=50, time_limit_per_trial=150)
bo.run()
inc_value = bo.get_incumbent()
config = inc_value[0][0]
with open(meta_dir + 'meta_learner_%s_%s_%s_config.pkl' % (meta_algo, metric, hash_id), 'wb') as f:
pk.dump(config, f)
print('Best hyperparameter config found', config)
return config
def test_branin():
space_dict = {
"parameters": {
"x1": {
"type": "float",
"bound": [-5, 10],
"default": 0
},
"x2": {
"type": "float",
"bound": [0, 15]
},
}
}
cs = get_config_space_from_dict(space_dict)
print(cs)
bo = BayesianOptimization(branin,
cs,
max_runs=30,
time_limit_per_trial=3,
logging_dir='logs')
bo.run()
inc_value = bo.get_incumbent()
print('BO', '=' * 30)
print(inc_value)
# Evaluate the random search.
bo = BayesianOptimization(branin,
cs,
max_runs=30,
time_limit_per_trial=3,
sample_strategy='random',
logging_dir='logs')
bo.run()
inc_value = bo.get_incumbent()
print('RANDOM', '=' * 30)
print(inc_value)
# Evaluate batch BO.
bo = BatchBayesianOptimization(branin,
cs,
max_runs=10,
batch_size=3,
time_limit_per_trial=3,
sample_strategy='median_imputation',
logging_dir='logs')
bo.run()
inc_value = bo.get_incumbent()
print('MEDIAN IMPUTATION BATCH BO', '=' * 30)
print(inc_value)
# Evaluate batch BO.
bo = BatchBayesianOptimization(branin,
cs,
max_runs=10,
batch_size=3,
time_limit_per_trial=3,
sample_strategy='local_penalization',
logging_dir='logs')
bo.run()
inc_value = bo.get_incumbent()
print('LOCAL PENALIZATION BATCH BO', '=' * 30)
print(inc_value)
"bound": [0, 15]
},
}
}
from litebo.utils.config_space.space_utils import get_config_space_from_dict
cs = get_config_space_from_dict(space_dict)
print(cs)
bo = BayesianOptimization(branin,
cs,
max_runs=90,
time_limit_per_trial=3,
logging_dir='logs')
bo.run()
inc_value = bo.get_incumbent()
print('BO', '=' * 30)
print(inc_value)
# Evaluate the random search.
bo = BayesianOptimization(branin,
cs,
max_runs=90,
time_limit_per_trial=3,
sample_strategy='random',
logging_dir='logs')
bo.run()
inc_value = bo.get_incumbent()
print('RANDOM', '=' * 30)
print(inc_value)