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 run(dataset_name):
file_id = '%s-resnet-%s-%d.pkl' % (dataset_name, mode, trial_num)
saved_file = os.path.join(data_dir, file_id)
# (x_train, y_train), (x_test, y_test), cls_num = load_dataset(dataset_name)
# print(x_train[0])
# print(x_test[0])
# print(x_train.shape)
# print(x_test.shape)
# print(y_train.shape)
def objective_function(cfg):
(x_train, y_train), (x_test,
y_test), cls_num = load_dataset(dataset_name)
epochs_num, run_count = get_default_setting(dataset_name)
val_error = train(cls_num,
epochs_num,
cfg,
x_train,
y_train,
x_test,
y_test,
seed=32)
print('the validation accuracy is ', 1 - val_error)
if not os.path.exists(saved_file):
data = list()
else:
with open(saved_file, 'rb') as f:
data = pickle.load(f)
data.append([cfg, val_error])
with open(saved_file, 'wb') as f:
pickle.dump(data, f)
return val_error
cs = create_configspace()
bo = BO(objective_function,
cs,
max_runs=trial_num,
time_limit_per_trial=10000,
sample_strategy=mode,
rng=np.random.RandomState(1))
bo.run()
min_impurity_decrease=self.min_impurity_decrease,
random_state=self.random_state,
n_jobs=self.n_jobs,
class_weight=self.class_weight,
warm_start=True)
self.estimator.fit(X, y, sample_weight=sample_weight)
return self
def predict(self, X):
if self.estimator is None:
raise NotImplementedError()
return self.estimator.predict(X)
dataset_list = dataset_str.split(',')
check_datasets(dataset_list)
cs = get_cs()
_run_count = min(int(len(set(cs.sample_configuration(30000))) * 0.75), run_count)
print(_run_count)
for dataset in dataset_list:
node = load_data(dataset, '../soln-ml/', True, task_type=0)
_x, _y = node.data[0], node.data[1]
eval = partial(eval_func, x=_x, y=_y)
bo = BO(eval, cs, max_runs=_run_count, time_limit_per_trial=600, sample_strategy=mode, rng=np.random.RandomState(1))
bo.run()
with open('logs/%s-random_forest-%s-%d.pkl' % (dataset, mode, run_count), 'wb')as f:
pickle.dump(bo.get_history().data, f)
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)