def main():
str_acq = 'ei'
num_iter = 10
X_train = np.array([
[-5],
[-1],
[1],
[2],
])
num_init = X_train.shape[0]
model_bo = bo.BO(np.array([[-6., 6.]]), str_acq=str_acq, normalize_Y=False)
X_test = np.linspace(-6, 6, 400)
X_test = np.reshape(X_test, (400, 1))
for ind_ in range(1, num_iter + 1):
Y_train = fun_target(X_train)
next_x, dict_info = model_bo.optimize(X_train, fun_target(X_train), str_sampling_method='uniform')
cov_X_X = dict_info['cov_X_X']
inv_cov_X_X = dict_info['inv_cov_X_X']
hyps = dict_info['hyps']
mu_test, sigma_test = model_bo.compute_posteriors(X_train, Y_train, X_test, cov_X_X, inv_cov_X_X, hyps)
acq_test = model_bo.compute_acquisitions(X_test, X_train, Y_train, cov_X_X, inv_cov_X_X, hyps)
mu_test = np.expand_dims(mu_test, axis=1)
sigma_test = np.expand_dims(sigma_test, axis=1)
acq_test = np.expand_dims(acq_test, axis=1)
X_train = np.vstack((X_train, next_x))
Y_train = fun_target(X_train)
utils_plotting.plot_bo_step(X_train, Y_train, X_test, fun_target(X_test), mu_test, sigma_test, path_save=PATH_SAVE, str_postfix='bo_{}_'.format(str_acq) + str(ind_), num_init=num_init)
utils_plotting.plot_bo_step_with_acq(X_train, Y_train, X_test, fun_target(X_test), mu_test, sigma_test, acq_test, path_save=PATH_SAVE, str_postfix='bo_{}_'.format(str_acq) + str(ind_), num_init=num_init)
def main():
str_acq = 'ei'
num_iter = 10
X_train = np.array([
[-5],
[-1],
[1],
[2],
])
num_init = X_train.shape[0]
model_bo = bo.BO(np.array([[-6., 6.]]), str_acq=str_acq)
X_test = np.linspace(-6, 6, 400)
X_test = np.reshape(X_test, (400, 1))
for ind_ in range(1, num_iter + 1):
Y_train = fun_target(X_train)
next_x, dict_info = model_bo.optimize(X_train,
fun_target(X_train),
str_initial_method_ao='uniform')
cov_X_X = dict_info['cov_X_X']
inv_cov_X_X = dict_info['inv_cov_X_X']
hyps = dict_info['hyps']
mu_test, sigma_test, Sigma_test = gp.predict_test_(
X_train, Y_train, X_test, cov_X_X, inv_cov_X_X, hyps)
acq_test = acquisition.ei(mu_test.flatten(), sigma_test.flatten(),
Y_train)
acq_test = np.expand_dims(acq_test, axis=1)
X_train = np.vstack((X_train, next_x))
Y_train = fun_target(X_train)
utils_plotting.plot_bo_step(X_train,
Y_train,
X_test,
fun_target(X_test),
mu_test,
sigma_test,
path_save=PATH_SAVE,
str_postfix='bo_{}_'.format(str_acq) +
str(ind_),
int_init=num_init)
utils_plotting.plot_bo_step_acq(X_train,
Y_train,
X_test,
fun_target(X_test),
mu_test,
sigma_test,
acq_test,
path_save=PATH_SAVE,
str_postfix='bo_{}_'.format(str_acq) +
str(ind_),
int_init=num_init)
def main():
num_iter = 10
X_train = np.array([
[-5],
[-1],
[1],
[2],
])
num_init = X_train.shape[0]
model_bo = bo.BO(np.array([[-6., 6.]]))
X_test = np.linspace(-6, 6, 400)
X_test = np.reshape(X_test, (400, 1))
for ind_ in range(1, num_iter + 1):
Y_train = fun_target(X_train)
next_x, _, _, cov_X_X, inv_cov_X_X, hyps = model_bo.optimize(
X_train, fun_target(X_train), str_initial_method='uniform')
mu_test, sigma_test = gp.predict_test_(X_train, Y_train, X_test,
cov_X_X, inv_cov_X_X, hyps)
acq_test = acquisition.ei(mu_test.flatten(), sigma_test.flatten(),
Y_train)
acq_test = np.expand_dims(acq_test, axis=1)
X_train = np.vstack((X_train, next_x))
Y_train = fun_target(X_train)
utils_plotting.plot_bo_step(X_train,
Y_train,
X_test,
fun_target(X_test),
mu_test,
sigma_test,
path_save=PATH_SAVE,
str_postfix='basics_bo_' + str(ind_),
int_init=num_init)
utils_plotting.plot_bo_step_acq(X_train,
Y_train,
X_test,
fun_target(X_test),
mu_test,
sigma_test,
acq_test,
path_save=PATH_SAVE,
str_postfix='basics_bo_' + str(ind_),
int_init=num_init,
is_acq_axis_small=True)
def test_plot_bo_step():
num_dim_X = 1
num_dim_Y = 1
num_train = 5
num_test = 10
X_train = np.ones((num_train, num_dim_X))
Y_train = np.ones((num_train, num_dim_Y))
X_test = np.ones((num_test, num_dim_X))
Y_test = np.ones((num_test, num_dim_Y))
mean_test = np.ones((num_test, num_dim_Y))
std_test = np.ones((num_test, num_dim_Y))
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, Y_test,
mean_test, 1)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, Y_test, 1,
std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, 1, mean_test,
std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, 1, Y_test, mean_test,
std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, 1, X_test, Y_test, mean_test,
std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(1, Y_train, X_test, Y_test, mean_test,
std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(np.arange(0, 10), Y_train, X_test, Y_test,
mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, np.arange(0, 10), X_test, Y_test,
mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, np.arange(0, 10), Y_test,
mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, np.arange(0, 10),
mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, Y_test,
np.arange(0, 10), std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, Y_test,
mean_test, np.arange(0, 10))
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(np.ones((num_train, 2)), Y_train, X_test,
Y_test, mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, np.ones((num_test, 2)),
Y_test, mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, np.ones((num_train, 2)), X_test,
Y_test, mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(np.ones((30, num_dim_X)), Y_train, X_test,
Y_test, mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, Y_test,
np.ones((num_test, 2)), std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, Y_test,
mean_test, np.ones((num_test, 2)))
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, np.ones((30, num_dim_X)),
Y_test, mean_test, std_test)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train, Y_train, X_test, Y_test,
mean_test, np.ones((30, num_dim_Y)))
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
num_init=20)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
path_save=1)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
str_postfix=1)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
str_x_axis=1)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
str_y_axis=1)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
num_init='abc')
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
use_tex=1)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
draw_zero_axis=1)
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
pause_figure='abc')
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
time_pause='abc')
with pytest.raises(AssertionError) as error:
package_target.plot_bo_step(X_train,
Y_train,
X_test,
Y_test,
mean_test,
std_test,
range_shade='abc')
