def main(str_optimizer_method_gp, str_mlm_method, str_ms_method, int_bo, int_iter, int_init):
int_dim = 2
bounds = utils_benchmarks.get_bounds(INFO_TARGET, int_dim)
list_Y = []
list_time = []
for ind_bo in range(0, int_bo):
print('BO Iteration', ind_bo)
model_bo = bo.BO(bounds, str_optimizer_method_gp=str_optimizer_method_gp, debug=False)
X_final, Y_final, time_final = utils_bo.optimize_many_with_random_init(model_bo, fun_target, int_init, int_iter, str_initial_method_bo='uniform', str_initial_method_ao='uniform', int_samples_ao=100, str_mlm_method=str_mlm_method, str_modelselection_method=str_ms_method, int_seed=77*(ind_bo+1))
list_Y.append(Y_final)
list_time.append(time_final)
arr_Y = np.array(list_Y)
if int_bo == 1:
arr_Y = np.expand_dims(np.squeeze(arr_Y), axis=0)
else:
arr_Y = np.squeeze(arr_Y)
arr_Y = np.expand_dims(arr_Y, axis=0)
arr_time = np.array(list_time)
arr_time = np.expand_dims(arr_time, axis=0)
print(np.array2string(arr_Y, separator=','))
print(np.array2string(arr_time, separator=','))
utils_plotting.plot_minimum(arr_Y, [STR_FUN_TARGET], int_init, True, path_save=None, str_postfix=None)
utils_plotting.plot_minimum_time(arr_time, arr_Y, [STR_FUN_TARGET], int_init, True, path_save=None, str_postfix=None)
return arr_Y, arr_time
def main():
# (max_depth, n_estimators)
int_init = 3
model_bo = bo.BO(np.array([[0.1, 2]]), debug=True)
list_Y = []
list_time = []
for _ in range(0, 10):
X_final, Y_final, time_final, _, _ = utils_bo.optimize_many_with_random_init(
model_bo,
fun_target,
int_init,
10,
str_initial_method_bo='uniform',
str_initial_method_ao='uniform',
int_samples_ao=100)
list_Y.append(Y_final)
list_time.append(time_final)
arr_Y = np.array(list_Y)
arr_Y = np.expand_dims(np.squeeze(arr_Y), axis=0)
arr_time = np.array(list_time)
arr_time = np.expand_dims(arr_time, axis=0)
utils_plotting.plot_minimum(arr_Y, ['ridge'],
int_init,
True,
path_save=PATH_SAVE,
str_postfix='ridge')
utils_plotting.plot_minimum_time(arr_time,
arr_Y, ['ridge'],
int_init,
True,
path_save=PATH_SAVE,
str_postfix='ridge')
def main():
int_bo = 5
int_iter = 50
int_init = 3
int_dim = 2
bounds = utils_benchmarks.get_bounds(INFO_TARGET, int_dim)
model_bo = bo.BO(bounds, debug=True)
list_Y = []
list_time = []
for ind_bo in range(0, int_bo):
print('BO Iteration', ind_bo)
X_final, Y_final, time_final = utils_bo.optimize_many_with_random_init(model_bo, fun_target, int_init, int_iter, str_initial_method_bo='uniform', str_initial_method_ao='uniform', int_samples_ao=100)
print(X_final)
print(Y_final)
print(time_final)
list_Y.append(Y_final)
list_time.append(time_final)
arr_Y = np.array(list_Y)
arr_Y = np.expand_dims(np.squeeze(arr_Y), axis=0)
arr_time = np.array(list_time)
arr_time = np.expand_dims(arr_time, axis=0)
utils_plotting.plot_minimum(arr_Y, [STR_FUN_TARGET], int_init, True, path_save=PATH_SAVE, str_postfix=STR_FUN_TARGET)
utils_plotting.plot_minimum_time(arr_time, arr_Y, [STR_FUN_TARGET], int_init, True, path_save=PATH_SAVE, str_postfix=STR_FUN_TARGET)
def test_optimize_many_with_random_init():
np.random.seed(42)
arr_range = np.array([
[-5.0, 5.0],
])
dim_X = arr_range.shape[0]
num_X = 3
num_iter = 10
fun_target = lambda x: x**2 - 2.0 * x + 1.0
model_bo = bo.BO(arr_range)
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(1, fun_target, num_X, num_iter)
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo, 1, num_X, num_iter)
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo, fun_target, 1.2,
num_iter)
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo, fun_target, num_X,
1.2)
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo,
fun_target,
num_X,
num_iter,
str_initial_method_bo=1)
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo,
fun_target,
num_X,
num_iter,
str_initial_method_bo='abc')
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo,
fun_target,
num_X,
num_iter,
str_initial_method_bo='grid')
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo,
fun_target,
num_X,
num_iter,
str_initial_method_ao=1)
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo,
fun_target,
num_X,
num_iter,
str_initial_method_ao='abc')
with pytest.raises(AssertionError) as error:
utils_bo.optimize_many_with_random_init(model_bo,
fun_target,
num_X,
num_iter,
int_seed=1.2)
X_final, Y_final, time_final = utils_bo.optimize_many_with_random_init(
model_bo, fun_target, num_X, num_iter, str_initial_method_bo='uniform')
assert len(X_final.shape) == 2
assert len(Y_final.shape) == 2
assert len(time_final.shape) == 1
assert X_final.shape[1] == dim_X
assert X_final.shape[0] == Y_final.shape[0] == time_final.shape[
0] == num_X + num_iter
assert Y_final.shape[1] == 1
