def main():
str_acq = 'aei'
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.aei(mu_test.flatten(), sigma_test.flatten(),
Y_train, hyps['noise'])
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 choose_fun_acquisition(
str_acq: str,
noise: constants.TYPING_UNION_FLOAT_NONE = None
) -> constants.TYPING_CALLABLE:
"""
It chooses and returns an acquisition function.
:param str_acq: the name of acquisition function.
:type str_acq: str.
:param hyps: dictionary of hyperparameters for acquisition function.
:type hyps: dict.
:returns: acquisition function.
:rtype: callable
:raises: AssertionError
"""
assert isinstance(str_acq, str)
assert isinstance(noise, (float, constants.TYPE_NONE))
assert str_acq in constants.ALLOWED_BO_ACQ
if str_acq == 'pi':
fun_acquisition = acquisition.pi
elif str_acq == 'ei':
fun_acquisition = acquisition.ei
elif str_acq == 'ucb':
fun_acquisition = acquisition.ucb
elif str_acq == 'aei':
assert noise is not None
fun_acquisition = lambda pred_mean, pred_std, Y_train: acquisition.aei(
pred_mean, pred_std, Y_train, noise)
elif str_acq == 'pure_exploit':
fun_acquisition = lambda pred_mean, pred_std, Y_train: acquisition.pure_exploit(
pred_mean)
elif str_acq == 'pure_explore':
fun_acquisition = lambda pred_mean, pred_std, Y_train: acquisition.pure_explore(
pred_std)
else:
raise NotImplementedError('_choose_fun_acquisition: allowed str_acq,\
but it is not implemented.')
return fun_acquisition
def _choose_fun_acquisition(str_acq, hyps):
"""
It chooses and returns an acquisition function.
:param str_acq: the name of acquisition function.
:type str_acq: str.
:param hyps: dictionary of hyperparameters for acquisition function.
:type hyps: dict.
:returns: acquisition function.
:rtype: function
:raises: AssertionError
"""
assert isinstance(str_acq, str)
assert isinstance(hyps, dict)
assert str_acq in constants.ALLOWED_BO_ACQ
if str_acq == 'pi':
fun_acquisition = acquisition.pi
elif str_acq == 'ei':
fun_acquisition = acquisition.ei
elif str_acq == 'ucb':
fun_acquisition = acquisition.ucb
elif str_acq == 'aei':
fun_acquisition = lambda pred_mean, pred_std, Y_train: acquisition.aei(
pred_mean, pred_std, Y_train, hyps['noise'])
elif str_acq == 'pure_exploit':
fun_acquisition = acquisition.pure_exploit
elif str_acq == 'pure_explore':
fun_acquisition = acquisition.pure_explore
else:
raise NotImplementedError(
'_choose_fun_acquisition: allowed str_acq, but it is not implemented.'
)
return fun_acquisition
def _choose_fun_acquisition(str_acq, hyps):
assert isinstance(str_acq, str)
assert isinstance(hyps, dict)
assert str_acq in constants.ALLOWED_BO_ACQ
if str_acq == 'pi':
fun_acquisition = acquisition.pi
elif str_acq == 'ei':
fun_acquisition = acquisition.ei
elif str_acq == 'ucb':
fun_acquisition = acquisition.ucb
elif str_acq == 'aei':
fun_acquisition = lambda pred_mean, pred_std, Y_train: acquisition.aei(
pred_mean, pred_std, Y_train, hyps['noise'])
elif str_acq == 'pure_exploit':
fun_acquisition = acquisition.pure_exploit
elif str_acq == 'pure_explore':
fun_acquisition = acquisition.pure_explore
else:
raise NotImplementedError(
'_choose_fun_acquisition: allowed str_acq, but it is not implemented.'
)
return fun_acquisition
def test_aei():
with pytest.raises(AssertionError) as error:
package_target.aei('abc', np.ones(10), np.zeros((5, 1)), 1.0)
with pytest.raises(AssertionError) as error:
package_target.aei(np.ones(10), 'abc', np.zeros((5, 1)), 1.0)
with pytest.raises(AssertionError) as error:
package_target.aei(np.ones(10), np.ones(10), 'abc', 1.0)
with pytest.raises(AssertionError) as error:
package_target.aei(np.ones(10), np.ones(10), np.zeros((5, 1)), 'abc')
with pytest.raises(AssertionError) as error:
package_target.aei(np.ones(10), np.ones(10), np.zeros((5, 1)), 1.0, jitter=1)
with pytest.raises(AssertionError) as error:
package_target.aei(np.ones(5), np.ones(10), np.zeros((5, 1)), 1.0)
with pytest.raises(AssertionError) as error:
package_target.aei(np.ones(10), np.ones(10), np.zeros(5), 1.0)
with pytest.raises(AssertionError) as error:
package_target.aei(np.ones(10), np.ones((10, 1)), np.zeros((5, 1)), 1.0)
with pytest.raises(AssertionError) as error:
package_target.aei(np.ones((10, 1)), np.ones(10), np.zeros((5, 1)), 1.0)
val_acq = package_target.aei(np.arange(0, 10), np.ones(10), np.zeros((5, 1)), 1.0)
truth_val_acq = np.array([1.16847489e-01, 2.44025364e-02, 2.48686922e-03, 1.11930407e-04, 2.09279771e-06, 1.56585558e-08, 4.57958958e-11, 5.15587486e-14, 2.21142019e-17, 3.58729395e-21])
assert (np.abs(val_acq - truth_val_acq) < TEST_EPSILON).all()