"type": "float",
"bound": [0.7, 1],
"default": 1,
"q": 0.1
},
"colsample_bytree": {
"type": "float",
"bound": [0.7, 1],
"default": 1,
"q": 0.1
},
},
# "num_objs": 1,
# "num_constraints": 0,
# "advisor_type": "default",
"max_runs": 100,
# "surrogate_type": "prf",
"time_limit_per_trial": 180,
# "logging_dir": "logs",
"task_id": "so_hpo"
}
bo = create_smbo(objective_function, **config_dict)
history = bo.run()
print(history)
history.plot_convergence()
plt.show()
# history.visualize_jupyter()
def main():
bo = create_smbo(branin, **config_dict)
config_dict = {
"optimizer": "SMBO",
"parameters": {
"x1": {
"type": "float",
"bound": [-5, 10],
"default": 0
},
"x2": {
"type": "float",
"bound": [0, 15]
},
},
"advisor_type": 'default',
"max_runs": 50,
"surrogate_type": 'gp',
"time_limit_per_trial": 5,
"logging_dir": 'logs',
"task_id": 'hp1'
}
bo = create_smbo(branin, **config_dict)
history = bo.run()
inc_value = bo.get_incumbent()
print('BO', '=' * 30)
print(inc_value)
print(history)
history.plot_convergence(true_minimum=0.397887)
plt.show()