def main():
str_acq = 'pi'
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.pi(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 test_pi():
with pytest.raises(AssertionError) as error:
package_target.pi('abc', np.ones(10), np.zeros((5, 1)))
with pytest.raises(AssertionError) as error:
package_target.pi(np.ones(10), 'abc', np.zeros((5, 1)))
with pytest.raises(AssertionError) as error:
package_target.pi(np.ones(10), np.ones(10), 'abc')
with pytest.raises(AssertionError) as error:
package_target.pi(np.ones(10), np.ones(10), np.zeros((5, 1)), 1)
with pytest.raises(AssertionError) as error:
package_target.pi(np.ones(5), np.ones(10), np.zeros((5, 1)))
with pytest.raises(AssertionError) as error:
package_target.pi(np.ones(10), np.ones(10), np.zeros(5))
with pytest.raises(AssertionError) as error:
package_target.pi(np.ones(10), np.ones((10, 1)), np.zeros((5, 1)))
with pytest.raises(AssertionError) as error:
package_target.pi(np.ones((10, 1)), np.ones(10), np.zeros((5, 1)))
val_acq = package_target.pi(np.arange(0, 10), np.ones(10), np.zeros((5, 1)))
truth_val_acq = np.array([5.00000000e-01, 1.58657674e-01, 2.27512118e-02, 1.35003099e-03, 3.16765954e-05, 2.86725916e-07, 9.86952260e-10, 1.28045212e-12, 6.22500364e-16, 1.12951395e-19])
print(val_acq)
assert (np.abs(val_acq - truth_val_acq) < TEST_EPSILON).all()