def test_ChecKGrads_EI(self):
acquisition_ei = acquisition_for_test(
AcquisitionEI(self.model, self.feasible_region))
grad_ei = GradientChecker(acquisition_ei.acquisition_function,
acquisition_ei.d_acquisition_function,
self.X_test)
self.assertTrue(grad_ei.checkgrad(tolerance=self.tolerance))
def setUp(self):
self.mock_model = Mock()
self.mock_optimizer = Mock()
domain = [{'name': 'var_1', 'type': 'continuous', 'domain': (-5,5), 'dimensionality': 2}]
self.space = Design_space(domain, None)
self.ei_acquisition = AcquisitionEI(self.mock_model, self.space, self.mock_optimizer)
def build_acquisition(X_init, space, aquisition_function, model):
aquisition_optimizer = GPyOpt.optimization.AcquisitionOptimizer(space, eps=0)
if(aquisition_function['type'] == 'ei'):
aquisition_function = AcquisitionEI(model=model, space=space, optimizer=aquisition_optimizer,jitter=aquisition_function['epsilon'])
elif(aquisition_function['type']== 'pi'):
aquisition_function = AcquisitionMPI(model=model, space=space, optimizer=aquisition_optimizer,jitter=aquisition_function['epsilon'])
elif(aquisition_function['type'] == 'lcb'):
lcb_const = np.sqrt( aquisition_function['upsilon']* (2* np.log( ((X_init.shape[0])**(X_init.shape[1]/2. + 2))*(np.pi**2)/(3. * aquisition_function['delta']) )))
aquisition_function = AcquisitionLCB(model=model, space=space, optimizer=aquisition_optimizer,exploration_weight=lcb_const)
return aquisition_function
def test_optimize_without_analytical_gradient_prediction(self):
"""Test that acquisition function optimize method returns expected optimum without analytical gradient prediction
"""
expected_optimum_position = [[0, 0]]
self.mock_optimizer.optimize.return_value = expected_optimum_position
self.mock_model.analytical_gradient_prediction = False
self.ei_acquisition = AcquisitionEI(self.mock_model, self.space, self.mock_optimizer)
optimum_position = self.ei_acquisition.optimize()
assert optimum_position == expected_optimum_position
def acquisition_creator(self, acquisition_type, model, space,
acquisition_optimizer, cost_withGradients,
**kwargs):
"""
Acquisition chooser from the available options. Extra parameters can be passed via **kwargs.
"""
acquisition_type = acquisition_type
model = model
space = space
acquisition_optimizer = acquisition_optimizer
cost_withGradients = cost_withGradients
acquisition_jitter = self.kwargs.get('acquisition_jitter', 0.01)
acquisition_weight = self.kwargs.get('acquisition_weight', 2)
# --- Choose the acquisition
if acquisition_type is None or acquisition_type == 'EI':
return AcquisitionEI(model, space, acquisition_optimizer,
cost_withGradients, acquisition_jitter)
elif acquisition_type == 'EI_MCMC':
return AcquisitionEI_MCMC(model, space, acquisition_optimizer,
cost_withGradients, acquisition_jitter)
elif acquisition_type == 'MPI':
return AcquisitionMPI(model, space, acquisition_optimizer,
cost_withGradients, acquisition_jitter)
elif acquisition_type == 'MPI_MCMC':
return AcquisitionMPI_MCMC(model, space, acquisition_optimizer,
cost_withGradients, acquisition_jitter)
elif acquisition_type == 'LCB':
return AcquisitionLCB(model, space, acquisition_optimizer, None,
acquisition_weight)
elif acquisition_type == 'LCB_MCMC':
return AcquisitionLCB_MCMC(model, space, acquisition_optimizer,
None, acquisition_weight)
elif acquisition_type == 'Adaptive_LCB':
return original_acq.AcquisitionLCBwithAdaptiveExplorationWeight(
model, space, acquisition_optimizer, None, **kwargs)
elif acquisition_type == 'Adaptive_LCB_MCMC':
return original_acq.AcquisitionLCBwithAdaptiveExplorationWeight_MCMC(
model, space, acquisition_optimizer, None, **kwargs)
elif acquisition_type == 'Continuous_Time_Varying':
return original_acq.AcquisitionContinuousTimeVarying(
model, space, acquisition_optimizer, None, **kwargs)
elif acquisition_type == 'Continuous_Time_Varying_MCMC':
return original_acq.AcquisitionContinuousTimeVarying_MCMC(
model, space, acquisition_optimizer, None, **kwargs)
else:
raise Exception('Invalid acquisition selected.')
def setUp(self):
self.mock_model = Mock()
self.mock_optimizer = Mock()
domain = [{'name': 'var_1', 'type': 'categorical', 'domain': (0, 1)}, {'name': 'var_2', 'type': 'continuous', 'domain': (-5,5), 'dimensionality': 2}]
# con_1: if var_1 is 0, var_2_1 must be <= -1
# con_2: 3 * (var_2_1 + var_2_2) <= 24
constraints = [{'name': 'con_1', 'constraint': '(x[:,0] == 0) * (x[:,1] + 1)'},
{'name': 'con_2', 'constraint': ' 3 * (x[:,1] + x[:,2]) - 24'}]
self.space = Design_space(domain, constraints)
self.ei_acquisition = AcquisitionEI(self.mock_model, self.space, self.mock_optimizer)
self.ei_acquisition._compute_acq = Mock()
self.ei_acquisition._compute_acq_withGradients = Mock()
def plot_acquisition(obj, d_opt, filename=None):
# GPyOpt part: Interpolation
bounds = obj.space.get_bounds()
x_grid = np.linspace(bounds[0][0], bounds[0][1], 1000)
x_grid = x_grid.reshape(len(x_grid), 1)
AEI = AcquisitionEI(obj.model, obj.space)
acqu = AEI._compute_acq(x_grid)
acqu_normalized = (-acqu - min(-acqu)) / (max(-acqu - min(-acqu)))
m, s = obj.model.predict(x_grid)
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111)
# Plot Model
# Note that we have to flip the GPyOpt utility
ax.plot(x_grid, -m, 'k-', lw=1, alpha=0.6)
ax.plot(x_grid, -(m + np.sqrt(s)), 'k-', alpha=0.2)
ax.plot(x_grid, -(m - np.sqrt(s)), 'k-', alpha=0.2)
ax.fill_between(x_grid.reshape(-1),
-(m + np.sqrt(s)).reshape(-1),
-(m - np.sqrt(s)).reshape(-1),
color='steelblue',
alpha=0.5,
label=r'68\% C.L.')
# Plot Evals
X = obj.X
Y = normalize(obj.Y)
ax.plot(X, -1 * Y, 'r.', markersize=10, label='Bayes. Opt. Evaluations')
# Plot optimum
ax.axvline(d_opt, ls='--', c='g', label='Optimal Design')
ax.set_xlabel(r'Design variable d')
ax.set_ylabel(r'$U(d)$')
ax.legend(loc='bottom right', prop={'size': 14})
ax.grid(True, ls='--')
# ax.set_title('Utility Function')
# ax.tick_params(labelsize=25)
plt.tight_layout()
if filename:
plt.savefig('{}.pdf'.format(filename))
plt.savefig('{}.png'.format(filename))
else:
plt.savefig('./utility.pdf')
plt.savefig('./utility.png')
def setUp(self):
self.mock_model = Mock()
self.mock_optimizer = Mock()
self.expected_optimum_position = [[0, 0]]
self.mock_optimizer.optimize.return_value = self.expected_optimum_position, self.expected_optimum_position
domain = [{
'name': 'var_1',
'type': 'continuous',
'domain': (-5, 5),
'dimensionality': 2
}]
self.space = Design_space(domain, None)
self.mock_optimizer.context_manager = ContextManager(self.space)
self.ei_acquisition = AcquisitionEI(self.mock_model, self.space,
self.mock_optimizer)
self.random_batch = RandomBatch(self.ei_acquisition, 10)