def setUp(self):
np.random.seed(10)
self.tolerance = 0.05 # Tolerance for difference between true and approximated gradients
objective = GPyOpt.objective_examples.experiments1d.forrester()
self.feasible_region = GPyOpt.Design_space(space = [{'name': 'var_1', 'type': 'continuous', 'domain': objective.bounds[0]}])
n_inital_design = 10
X = samples_multidimensional_uniform(objective.bounds,n_inital_design)
Y = objective.f(X)
self.X_test = samples_multidimensional_uniform(objective.bounds,n_inital_design)
self.model = Mock()
self.model.get_fmin.return_value = 0.0
self.model.predict_withGradients.return_value = np.zeros(X.shape), np.zeros(Y.shape), np.zeros(X.shape), np.zeros(X.shape)
def setUp(self): np.random.seed(1) self.tolerance = 0.01 #Tolerance for difference between true and approximated gradients objective = GPyOpt.fmodels.experiments1d.forrester() bounds = objective.bounds input_dim = len(bounds) n_inital_design = 8 X = samples_multidimensional_uniform(bounds,n_inital_design) Y = objective.f(X) self.X_test = samples_multidimensional_uniform(bounds,n_inital_design) self.model = GPy.models.GPRegression(X,Y) self.model.optimize_restarts(10,verbose=False) self.model.Gaussian_noise.constrain_fixed(1e-6, warning=False)
def setUp(self):
np.random.seed(1)
self.tolerance = 0.01 #Tolerance for difference between true and approximated gradients
objective = GPyOpt.objective_examples.experiments1d.forrester()
self.feasible_region = GPyOpt.Design_space(space = [{'name': 'var_1', 'type': 'continuous', 'domain': objective.bounds[0]}])
n_inital_design = 10
X = samples_multidimensional_uniform(objective.bounds,n_inital_design)
Y = objective.f(X)
self.X_test = samples_multidimensional_uniform(objective.bounds,n_inital_design)
self.model = GPyOpt.models.GPModel()
self.model._create_model(X,Y)
self.model.model.optimize_restarts(10,verbose=False)
self.model.model.Gaussian_noise.constrain_fixed(1e-6, warning=False)
def setUp(self):
np.random.seed(1)
self.tolerance = 0.01 # Tolerance for difference between true and approximated gradients
objective = GPyOpt.fmodels.experiments1d.forrester()
bounds = objective.bounds
input_dim = len(bounds)
n_inital_design = 8
X = samples_multidimensional_uniform(bounds, n_inital_design)
Y = objective.f(X)
self.X_test = samples_multidimensional_uniform(bounds, n_inital_design)
self.model = GPy.models.GPRegression(X, Y)
self.model.optimize_restarts(10, verbose=False)
self.model.Gaussian_noise.constrain_fixed(1e-6, warning=False)
def setUp(self):
np.random.seed(1)
self.tolerance = 0.05 #Tolerance for difference between true and approximated gradients
objective = GPyOpt.objective_examples.experiments1d.forrester()
self.feasible_region = GPyOpt.Design_space(
space=[{
'name': 'var_1',
'type': 'continuous',
'domain': objective.bounds[0]
}])
n_inital_design = 10
X = samples_multidimensional_uniform(objective.bounds, n_inital_design)
Y = objective.f(X)
self.X_test = samples_multidimensional_uniform(objective.bounds,
n_inital_design)
self.model = GPyOpt.models.GPModel()
self.model._create_model(X, Y)
self.model.model.optimize_restarts(10, verbose=False)
self.model.model.Gaussian_noise.constrain_fixed(1e-6, warning=False)
def setUp(self):
# Override margin for error
self.precision = 0.1
##
# -- methods configuration
##
model_type = 'GP'
initial_design_numdata = None
initial_design_type = 'random'
acquisition_type = 'EI'
normalize_Y = True
exact_feval = False
acquisition_optimizer_type = 'lbfgs'
model_update_interval = 1
evaluator_type = 'sequential'
batch_size = 1
num_cores = 1
verbosity = False
# stop conditions
max_iter = 5
max_time = 999
eps = 1e-8
self.methods_configs = [
{ 'name': 'output_normalization',
'model_type' : model_type,
'initial_design_numdata' : initial_design_numdata,
'initial_design_type' : initial_design_type,
'acquisition_type' : acquisition_type,
'normalize_Y' : False,
'exact_feval' : exact_feval,
'acquisition_optimizer_type' : acquisition_optimizer_type,
'model_update_interval' : model_update_interval,
'verbosity' : verbosity,
'evaluator_type' : evaluator_type,
'batch_size' : batch_size,
'num_cores' : num_cores,
'max_iter' : max_iter,
'max_time' : max_time,
'eps' : eps
},
{ 'name': 'noiseless_evauations',
'model_type' : model_type,
'initial_design_numdata' : initial_design_numdata,
'initial_design_type' : initial_design_type,
'acquisition_type' : acquisition_type,
'normalize_Y' : normalize_Y,
'exact_feval' : True,
'acquisition_optimizer_type' : acquisition_optimizer_type,
'model_update_interval' : model_update_interval,
'verbosity' : verbosity,
'evaluator_type' : evaluator_type,
'batch_size' : batch_size,
'num_cores' : num_cores,
'max_iter' : max_iter,
'max_time' : max_time,
'eps' : eps
},
{ 'name': 'update_intervals',
'model_type' : model_type,
'initial_design_numdata' : initial_design_numdata,
'initial_design_type' : initial_design_type,
'acquisition_type' : acquisition_type,
'normalize_Y' : normalize_Y,
'exact_feval' : exact_feval,
'acquisition_optimizer_type' : acquisition_optimizer_type,
'model_update_interval' : 2,
'verbosity' : verbosity,
'evaluator_type' : evaluator_type,
'batch_size' : batch_size,
'num_cores' : num_cores,
'max_iter' : max_iter,
'max_time' : max_time,
'eps' : eps
},
# { 'name': 'max_iterations',
# 'model_type' : model_type,
# 'initial_design_numdata' : initial_design_numdata,
# 'initial_design_type' : initial_design_type,
# 'acquisition_type' : acquisition_type,
# 'normalize_Y' : normalize_Y,
# 'exact_feval' : exact_feval,
# 'acquisition_optimizer_type' : acquisition_optimizer_type,
# 'model_update_interval' : model_update_interval,
# 'verbosity' : verbosity,
# 'evaluator_type' : evaluator_type,
# 'batch_size' : batch_size,
# 'num_cores' : num_cores,
# 'max_iter' : 10,
# 'max_time' : max_time,
# 'eps' : eps
# },
]
# -- Problem setup
np.random.seed(1)
n_inital_design = 5
input_dim = 5
f_bounds = (-5,5)
self.f_inits = samples_multidimensional_uniform([f_bounds]*input_dim,n_inital_design)
self.f_inits = self.f_inits.reshape(input_dim, self.f_inits.shape[-1])
self.problem_config = {
'objective': GPyOpt.objective_examples.experimentsNd.gSobol(np.ones(input_dim)).f,
'domain': [{'name': 'var_1', 'type': 'continuous', 'domain': f_bounds, 'dimensionality': input_dim}],
'constraints': None,
'cost_withGradients': None}
def setUp(self):
# -- This file was used to generate the test files
self.outpath = os.path.join(os.path.dirname(__file__),'test_files')
self.is_unittest = True # Test files were generated with this line = False
##
# -- methods configuration
##
model_type = 'GP'
initial_design_numdata = None
initial_design_type = 'random'
acquisition_type = 'EI'
normalize_Y = True
exact_feval = False
acquisition_optimizer_type = 'lbfgs'
model_update_interval = 1
evaluator_type = 'sequential'
batch_size = 1
num_cores = 1
verbosity = False
# stop conditions
max_iter = 5
max_time = 999
eps = 1e-8
self.methods_configs = [
{ 'name': 'EI',
'model_type' : model_type,
'initial_design_numdata' : initial_design_numdata,
'initial_design_type' : initial_design_type,
'acquisition_type' : 'EI',
'normalize_Y' : normalize_Y,
'exact_feval' : exact_feval,
'acquisition_optimizer_type' : acquisition_optimizer_type,
'model_update_interval' : model_update_interval,
'verbosity' : verbosity,
'evaluator_type' : evaluator_type,
'batch_size' : batch_size,
'num_cores' : num_cores,
'max_iter' : max_iter,
'max_time' : max_time,
'eps' : eps
},
{ 'name': 'MPI',
'model_type' : model_type,
'initial_design_numdata' : initial_design_numdata,
'initial_design_type' : initial_design_type,
'acquisition_type' : 'MPI',
'normalize_Y' : normalize_Y,
'exact_feval' : exact_feval,
'acquisition_optimizer_type' : acquisition_optimizer_type,
'model_update_interval' : model_update_interval,
'verbosity' : verbosity,
'evaluator_type' : evaluator_type,
'batch_size' : batch_size,
'num_cores' : num_cores,
'max_iter' : max_iter,
'max_time' : max_time,
'eps' : eps
},
{ 'name': 'LCB',
'model_type' : model_type,
'initial_design_numdata' : initial_design_numdata,
'initial_design_type' : initial_design_type,
'acquisition_type' : 'LCB',
'normalize_Y' : normalize_Y,
'exact_feval' : exact_feval,
'acquisition_optimizer_type' : acquisition_optimizer_type,
'model_update_interval' : model_update_interval,
'verbosity' : verbosity,
'evaluator_type' : evaluator_type,
'batch_size' : batch_size,
'num_cores' : num_cores,
'max_iter' : max_iter,
'max_time' : max_time,
'eps' : eps
}
]
# -- Problem setup
np.random.seed(1)
n_inital_design = 5
input_dim = 5
f_bounds = (-5,5)
self.f_inits = samples_multidimensional_uniform([f_bounds]*input_dim,n_inital_design)
self.f_inits = self.f_inits.reshape(1, input_dim, self.f_inits.shape[-1])
self.problem_config = {
'objective': GPyOpt.objective_examples.experimentsNd.gSobol(np.ones(input_dim)).f,
'domain': [{'name': 'var_1', 'type': 'continuous', 'domain': f_bounds, 'dimensionality': input_dim}],
'constrains': None,
'cost_withGradients': None}
def setUp(self):
# -- This file was used to generate the test files
self.outpath = './test_files'
self.is_unittest = True # test files were generated with this line =True
##
# -- methods configuration
##
max_iter = 25
eps = 1e-8
# acquisition type (testing here)
acquisition_name = 'EI'
acquisition_par = 0.0
# acquisition optimization type
acqu_optimize_method = 'fast_random'
acqu_optimize_restarts = 25
true_gradients = True
# batch type
n_inbatch = 1
batch_method = 'predictive'
n_procs = 1
# type of inital design
numdata_initial_design = 3
type_initial_design = 'random'
# model type
kernel = None
model_optimize_interval = 1
model_optimize_restarts = 2
sparseGP = False
num_inducing = None
# likelihood type
normalize = False
exact_feval = True
verbosity = False
self.methods_configs = [
{ 'name': 'stop_condition',
'max_iter':max_iter,
'acquisition_name':acquisition_name,
'acquisition_par': acquisition_par,
'true_gradients': true_gradients,
'acqu_optimize_method':acqu_optimize_method,
'acqu_optimize_restarts':acqu_optimize_restarts,
'batch_method': batch_method,
'n_inbatch':n_inbatch,
'n_procs':n_inbatch,
'numdata_initial_design': numdata_initial_design,
'type_initial_design': type_initial_design,
'kernel': kernel,
'model_optimize_interval': model_optimize_interval,
'model_optimize_restarts': model_optimize_restarts,
'sparseGP': sparseGP,
'num_inducing': num_inducing,
'normalize': normalize,
'exact_feval': exact_feval,
'eps': 0.001,
'verbosity':verbosity,
},
{ 'name': 'Y_normalization',
'max_iter':max_iter,
'acquisition_name':acquisition_name,
'acquisition_par': acquisition_par,
'true_gradients': true_gradients,
'acqu_optimize_method':acqu_optimize_method,
'acqu_optimize_restarts':acqu_optimize_restarts,
'batch_method': batch_method,
'n_inbatch':n_inbatch,
'n_procs':n_inbatch,
'numdata_initial_design': numdata_initial_design,
'type_initial_design': type_initial_design,
'kernel': kernel,
'model_optimize_interval': model_optimize_interval,
'model_optimize_restarts': model_optimize_restarts,
'sparseGP': sparseGP,
'num_inducing': num_inducing,
'normalize': True,
'exact_feval': exact_feval,
'eps': eps,
'verbosity':verbosity,
},
{ 'name': 'noisy_evaluations',
'max_iter':max_iter,
'acquisition_name':acquisition_name,
'acquisition_par': acquisition_par,
'true_gradients': true_gradients,
'acqu_optimize_method':acqu_optimize_method,
'acqu_optimize_restarts':acqu_optimize_restarts,
'batch_method': batch_method,
'n_inbatch':n_inbatch,
'n_procs':n_inbatch,
'numdata_initial_design': numdata_initial_design,
'type_initial_design': type_initial_design,
'kernel': kernel,
'model_optimize_interval': model_optimize_interval,
'model_optimize_restarts': model_optimize_restarts,
'sparseGP': sparseGP,
'num_inducing': num_inducing,
'normalize': True,
'exact_feval': False,
'eps': eps,
'verbosity':verbosity,
},
{ 'name': 'latin_design',
'max_iter':max_iter,
'acquisition_name':acquisition_name,
'acquisition_par': acquisition_par,
'true_gradients': true_gradients,
'acqu_optimize_method':acqu_optimize_method,
'acqu_optimize_restarts':acqu_optimize_restarts,
'batch_method': batch_method,
'n_inbatch':n_inbatch,
'n_procs':n_inbatch,
'numdata_initial_design': 20,
'type_initial_design': 'latin',
'kernel': kernel,
'model_optimize_interval': model_optimize_interval,
'model_optimize_restarts': model_optimize_restarts,
'sparseGP': sparseGP,
'num_inducing': num_inducing,
'normalize': normalize,
'exact_feval': exact_feval,
'eps': eps,
'verbosity':verbosity,
},
]
# -- Problem setup
np.random.seed(1)
f_bound_dim = (-5.,5.)
f_dim = 5
n_inital_design = 5
self.f_obj = GPyOpt.fmodels.experimentsNd.gSobol(np.ones(f_dim)).f
self.f_bounds = [f_bound_dim]*f_dim
self.f_inits = samples_multidimensional_uniform(self.f_bounds,n_inital_design)
self.f_inits = self.f_inits.reshape(1, f_dim, self.f_inits.shape[-1])
def setUp(self):
##
# -- methods configuration
##
model_type = 'GP'
initial_design_numdata = None
initial_design_type = 'random'
acquisition_type = 'EI'
normalize_Y = True
exact_feval = True
acquisition_optimizer_type = 'lbfgs'
model_update_interval = 1
evaluator_type = 'sequential'
batch_size = 1
num_cores = 1
verbosity = False
# stop conditions
max_iter = 5
max_time = 999
eps = 1e-8
self.methods_configs = [
{ 'name': 'lbfgs',
'model_type' : model_type,
'initial_design_numdata' : initial_design_numdata,
'initial_design_type' : initial_design_type,
'acquisition_type' : acquisition_type,
'normalize_Y' : normalize_Y,
'exact_feval' : exact_feval,
'acquisition_optimizer_type' : 'lbfgs',
'model_update_interval' : model_update_interval,
'verbosity' : verbosity,
'evaluator_type' : evaluator_type,
'batch_size' : batch_size,
'num_cores' : num_cores,
'max_iter' : max_iter,
'max_time' : max_time,
'eps' : eps
},
# { 'name': 'DIRECT',
# 'model_type' : model_type,
# 'initial_design_numdata' : initial_design_numdata,
# 'initial_design_type' : initial_design_type,
# 'acquisition_type' : acquisition_type,
# 'normalize_Y' : normalize_Y,
# 'exact_feval' : exact_feval,
# 'acquisition_optimizer_type' : 'DIRECT',
# 'model_update_interval' : model_update_interval,
# 'verbosity' : verbosity,
# 'evaluator_type' : evaluator_type,
# 'batch_size' : batch_size,
# 'num_cores' : num_cores,
# 'max_iter' : 1,
# 'max_time' : max_time,
# 'eps' : eps
# },
# { 'name': 'CMA',
# 'model_type' : model_type,
# 'initial_design_numdata' : initial_design_numdata,
# 'initial_design_type' : initial_design_type,
# 'acquisition_type' : acquisition_type,
# 'normalize_Y' : normalize_Y,
# 'exact_feval' : exact_feval,
# 'acquisition_optimizer_type' : 'CMA',
# 'model_update_interval' : model_update_interval,
# 'verbosity' : verbosity,
# 'evaluator_type' : evaluator_type,
# 'batch_size' : batch_size,
# 'num_cores' : num_cores,
# 'max_iter' : max_iter,
# 'max_time' : max_time,
# 'eps' : eps
# }
]
# -- Problem setup
n_inital_design = 5
input_dim = 5
f_bounds = (-5,5)
np.random.seed(1)
self.f_inits = samples_multidimensional_uniform([f_bounds]*input_dim,n_inital_design)
self.f_inits = self.f_inits.reshape(input_dim, self.f_inits.shape[-1])
self.problem_config = {
'objective': GPyOpt.objective_examples.experimentsNd.gSobol(np.ones(input_dim)).f,
'domain': [{'name': 'var_1', 'type': 'continuous', 'domain': f_bounds, 'dimensionality': input_dim}],
'constraints': None,
'cost_withGradients': None}
