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.mpi_acquisition = AcquisitionMPI(self.mock_model, self.space, self.mock_optimizer)
def test_optimize_without_analytical_gradient_prediction(self):
expected_optimum_position = [[0,0]]
self.mock_optimizer.optimize.return_value = expected_optimum_position
self.mock_model.analytical_gradient_prediction = False
self.mpi_acquisition = AcquisitionMPI(self.mock_model, self.space, self.mock_optimizer)
optimum_position = self.mpi_acquisition.optimize()
self.assertEqual(optimum_position, expected_optimum_position)
def test_ChecKGrads_MPI(self):
acquisition_mpi = acquisition_for_test(
AcquisitionMPI(self.model, self.feasible_region))
grad_mpi = GradientChecker(acquisition_mpi.acquisition_function,
acquisition_mpi.d_acquisition_function,
self.X_test)
self.assertTrue(grad_mpi.checkgrad(tolerance=self.tolerance))
class TestMPIAcquisition(unittest.TestCase):
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.mpi_acquisition = AcquisitionMPI(self.mock_model, self.space, self.mock_optimizer)
def test_acquisition_function(self):
self.mock_model.predict.return_value = (1,3)
self.mock_model.get_fmin.return_value = (0.1)
weighted_acquisition = self.mpi_acquisition.acquisition_function(np.array([2,2]))
expected_acquisition = np.array([[-0.38081792], [-0.38081792]])
self.assertTrue(np.isclose(weighted_acquisition, expected_acquisition).all())
def test_acquisition_function_withGradients(self):
self.mock_model.predict_withGradients.return_value = (1,3,0.1,0.2)
self.mock_model.get_fmin.return_value = 0.1
weighted_acquisition, weighted_gradient = self.mpi_acquisition.acquisition_function_withGradients(np.array([2,2]))
self.assertTrue(np.isclose(weighted_acquisition, np.array([[-0.38081792],[-0.38081792]])).all())
self.assertTrue(np.isclose(weighted_gradient, np.array([[0.00499539, 0.00499539],[0.00499539,0.00499539]])).all())
def test_optimize_with_analytical_gradient_prediction(self):
expected_optimum_position = [[0,0]]
self.mock_optimizer.optimize.return_value = expected_optimum_position
self.mock_model.analytical_gradient_prediction = True
self.mpi_acquisition = AcquisitionMPI(self.mock_model, self.space, self.mock_optimizer)
optimum_position = self.mpi_acquisition.optimize()
self.assertEqual(optimum_position, expected_optimum_position)
def test_optimize_without_analytical_gradient_prediction(self):
expected_optimum_position = [[0,0]]
self.mock_optimizer.optimize.return_value = expected_optimum_position
self.mock_model.analytical_gradient_prediction = False
self.mpi_acquisition = AcquisitionMPI(self.mock_model, self.space, self.mock_optimizer)
optimum_position = self.mpi_acquisition.optimize()
self.assertEqual(optimum_position, expected_optimum_position)
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 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.')