class ExpectedImprovementPerCost(Acquisition):
def __init__(self, model: Union[IModel, IDifferentiable], cost_model: Union[IModel, IDifferentiable],
jitter: np.float64 = np.float64(0))-> None:
"""
This acquisition computes for a given input the improvement over the current best observed value in
expectation. For more information see:
Efficient Global Optimization of Expensive Black-Box Functions
Jones, Donald R. and Schonlau, Matthias and Welch, William J.
Journal of Global Optimization
:param model: model that is used to compute the improvement.
:param jitter: parameter to encourage extra exploration.
"""
self.model = model
self.cost_model = cost_model
self.jitter = jitter
self.ei = ExpectedImprovement(model, jitter)
def evaluate(self, x: np.ndarray) -> np.ndarray:
"""
Computes the Expected Improvement.
:param x: points where the acquisition is evaluated.
"""
improvement = self.ei.evaluate(x)
mean, _ = self.cost_model.predict(x)
return improvement / mean
def evaluate_with_gradients(self, x: np.ndarray) -> Tuple:
"""
Computes the Expected Improvement and its derivative.
:param x: locations where the evaluation with gradients is done.
"""
improvement, dimprovement_dx = self.ei.evaluate_with_gradients(x)
mean, _ = self.cost_model.predict(x)
dmean_dx, _ = self.model.get_prediction_gradients(x)
return improvement / mean, (dimprovement_dx * mean - dmean_dx * improvement) / (mean ** 2)
def has_gradients(self) -> bool:
"""Returns that this acquisition has gradients"""
return True
Y_train,
n_fidelities=2,
kernels=kernels,
verbose=True,
optimization_restarts=1)
for m in nonlin_mf_model.models:
m.Gaussian_noise.variance.fix(0)
nonlin_mf_model.optimize()
from emukit.bayesian_optimization.acquisitions import ExpectedImprovement
ei = ExpectedImprovement(nonlin_mf_model)
ei_locations = np.atleast_2d(np.array([[0.4232, 0.6761]]))
print(ei.evaluate(ei_locations))
# ## Compute mean and variance predictions
#
# hf_mean_nonlin_mf_model, hf_var_nonlin_mf_model = nonlin_mf_model.predict(X_plot_high)
# hf_std_nonlin_mf_model = np.sqrt(hf_var_nonlin_mf_model)
#
# lf_mean_nonlin_mf_model, lf_var_nonlin_mf_model = nonlin_mf_model.predict(X_plot_low)
# lf_std_nonlin_mf_model = np.sqrt(lf_var_nonlin_mf_model)
#
#
# ## Plot posterior mean and variance of nonlinear multi-fidelity model
#
# plt.figure(figsize=(12,8))
# plt.fill_between(x_plot.flatten(), (lf_mean_nonlin_mf_model - 1.96*lf_std_nonlin_mf_model).flatten(),
# (lf_mean_nonlin_mf_model + 1.96*lf_std_nonlin_mf_model).flatten(), color='g', alpha=0.3)