def test_duplicate_with_ignored_and_pending(self):
space = [
{'name': 'var_1', 'type': 'continuous', 'domain':(-3,1), 'dimensionality': 1},
{'name': 'var_2', 'type': 'discrete', 'domain': (0,1,2,3)},
{'name': 'var_3', 'type': 'categorical', 'domain': (0, 1)}
]
design_space = Design_space(space)
np.random.seed(666)
number_points = 5
zipped_X = initial_design("random",design_space,number_points)
pending_zipped_X = initial_design("random", design_space, number_points)
ignored_zipped_X = initial_design("random", design_space, number_points)
d = DuplicateManager(design_space, zipped_X, pending_zipped_X, ignored_zipped_X)
duplicate_in_pending_state = np.atleast_2d(pending_zipped_X[0,:].copy())
assert d.is_zipped_x_duplicate(duplicate_in_pending_state)
assert d.is_unzipped_x_duplicate(design_space.unzip_inputs(duplicate_in_pending_state))
duplicate_in_ignored_state = np.atleast_2d(ignored_zipped_X[0,:].copy())
assert d.is_zipped_x_duplicate(duplicate_in_ignored_state)
assert d.is_unzipped_x_duplicate(design_space.unzip_inputs(duplicate_in_ignored_state))
def test_grid_design(self):
init_points_count = 3
samples = initial_design('grid', self.design_space, init_points_count)
self.assertEqual(len(samples), init_points_count)
self.assert_samples_against_space(samples)
init_points_count = 1000
samples = initial_design('grid', self.design_space, init_points_count)
self.assertEqual(len(samples), init_points_count)
self.assert_samples_against_space(samples)
def test_grid_design(self):
init_points_count = 3
samples = initial_design('grid', self.design_space, init_points_count)
self.assertEqual(len(samples), init_points_count)
self.assert_samples_against_space(samples)
init_points_count = 1000
samples = initial_design('grid', self.design_space, init_points_count)
self.assertEqual(len(samples), init_points_count)
self.assert_samples_against_space(samples)
def test_grid_design_with_multiple_continuous_variables(self):
self.space.extend([
{'name': 'var_5', 'type': 'continuous', 'domain':(0,5), 'dimensionality': 2},
{'name': 'var_6', 'type': 'continuous', 'domain':(-5,5), 'dimensionality': 1}
])
self.design_space = Design_space(self.space)
init_points_count = 10
samples = initial_design('grid', self.design_space, init_points_count)
self.assertEqual(len(samples), 1)
init_points_count = 100
samples = initial_design('grid', self.design_space, init_points_count)
self.assertEqual(len(samples), 3**4)
def test_grid_design_with_multiple_continuous_variables(self):
self.space.extend([
{'name': 'var_5', 'type': 'continuous', 'domain':(0,5), 'dimensionality': 2},
{'name': 'var_6', 'type': 'continuous', 'domain':(-5,5), 'dimensionality': 1}
])
self.design_space = Design_space(self.space)
init_points_count = 10
samples = initial_design('grid', self.design_space, init_points_count)
self.assertEqual(len(samples), 1)
init_points_count = 100
samples = initial_design('grid', self.design_space, init_points_count)
self.assertEqual(len(samples), 3**4)
def start(self):
if self.Y is None:
if "Y_init" in self.module and "X_init" in self.module:
self.X = self.module["X_init"]
self.Y = self.module["Y_init"]
else:
if "X_init" in self.module:
self.X = self.module["X_init"]
elif self.X is None:
self.X = initial_design(self.initial_design_type,
self.space,
self.initial_design_numdata)
self.Y = self.run_initial(
self.X) # FIXME: wait for all thest to finish
assert len(self.X) == len(self.Y)
print("Initial: {} points. Max exp num: {}.".format(
len(self.X), self.max_exp))
opt = GPyOpt.methods.BayesianOptimization(
f=None,
domain=self.domain,
constraints=self.constraints,
cost_withGradients=None,
model_type='GP',
X=self.X,
Y=self.Y,
acquisition_type='EI',
acquisition_optimizer_type='lbfgs',
evaluator_type='local_penalization',
batch_size=self.num_processes)
self.init_opt(opt)
first_batch_X = opt.suggest_next_locations()
self.run_batch(first_batch_X)
self.wait_and_run()
def test_duplicate(self):
space = [
{'name': 'var_1', 'type': 'continuous', 'domain':(-3,1), 'dimensionality': 1},
{'name': 'var_2', 'type': 'discrete', 'domain': (0,1,2,3)},
{'name': 'var_3', 'type': 'categorical', 'domain': (0, 1)}
]
design_space = Design_space(space)
np.random.seed(666)
number_points = 5
zipped_X = initial_design("random",design_space,number_points)
d = DuplicateManager(design_space, zipped_X)
duplicate = np.atleast_2d(zipped_X[0,:].copy())
assert d.is_zipped_x_duplicate(duplicate)
assert d.is_unzipped_x_duplicate(design_space.unzip_inputs(duplicate))
non_duplicate = np.array([[-2.5, 2., 0.]])
for x in zipped_X:
assert not np.all(non_duplicate==x)
assert not d.is_zipped_x_duplicate(non_duplicate)
assert not d.is_unzipped_x_duplicate(design_space.unzip_inputs(non_duplicate))
def suggest_sample(self, number_of_samples=1):
"""
Returns a suggested next point to evaluate.
"""
suggested_sample = initial_design('random',
self.decision_context_space, 1)
return suggested_sample
def _compute_final_evaluations(self,
pending_zipped_X=None,
ignored_zipped_X=None,
re_use=False):
"""
Computes the location of the new evaluation (optimizes the acquisition in the standard case).
:param pending_zipped_X: matrix of input configurations that are in a pending state (i.e., do not have an evaluation yet).
:param ignored_zipped_X: matrix of input configurations that the user black-lists, i.e., those configurations will not be suggested again.
:return:
"""
## --- Update the context if any
self.acquisition.optimizer.context_manager = ContextManager(
self.space,
self.context,
)
print("compute next evaluation")
if self.sample_from_acq:
print("suggest next location given THOMPSON SAMPLING")
candidate_points = initial_design('latin', self.space, 2000)
aux_var = self.acquisition._compute_acq(candidate_points)
else:
if self.constraint is not None:
aux_var = self.last_step_evaluator.compute_batch(
duplicate_manager=None, re_use=re_use, constrained=True)
else:
aux_var = self.last_step_evaluator.compute_batch(
duplicate_manager=None, re_use=re_use, constrained=False)
return self.space.zip_inputs(aux_var[0])
def test_random_design_with_bandit_only(self):
space = [self.bandit_variable]
self.design_space = Design_space(space)
initial_points_count = 3
samples = initial_design('random', self.design_space, initial_points_count)
self.assertEqual(len(samples), initial_points_count)
def test_random_design_with_bandit_only(self):
space = [self.bandit_variable]
self.design_space = Design_space(space)
initial_points_count = 3
samples = initial_design('random', self.design_space, initial_points_count)
self.assertEqual(len(samples), initial_points_count)
def verbosity_plot_2D(self):
####plots
print("generating plots")
design_plot = initial_design('random', self.space, 1000)
# precision = []
# for i in range(20):
# kg_f = -self.acquisition._compute_acq(design_plot)
# precision.append(np.array(kg_f).reshape(-1))
# print("mean precision", np.mean(precision, axis=0), "std precision", np.std(precision, axis=0), "max precision", np.max(precision, axis=0), "min precision",np.min(precision, axis=0))
ac_f = self.expected_improvement(design_plot)
Y, _ = self.objective.evaluate(design_plot)
C, _ = self.constraint.evaluate(design_plot)
pf = self.probability_feasibility_multi_gp(design_plot, self.model_c).reshape(-1, 1)
mu_f = self.model.predict(design_plot)[0]
bool_C = np.product(np.concatenate(C, axis=1) < 0, axis=1)
func_val = Y * bool_C.reshape(-1, 1)
# kg_f = -self.acquisition._compute_acq(design_plot)
fig, axs = plt.subplots(2, 2)
axs[0, 0].set_title('True Function')
axs[0, 0].scatter(design_plot[:, 0], design_plot[:, 1], c=np.array(func_val).reshape(-1))
axs[0, 0].scatter(self.X[:, 0], self.X[:, 1], color="red", label="sampled")
#suggested_sample_value = self.objective.evaluate(self.suggested_sample)
axs[0, 0].scatter(self.suggested_sample[:,0], self.suggested_sample[:,1], marker="x", color="red",
label="suggested")
axs[0, 0].legend()
axs[0, 1].set_title('approximation Acqu Function')
axs[0, 1].scatter(design_plot[:,0],design_plot[:,1], c=np.array(ac_f).reshape(-1))
axs[0, 1].legend()
# axs[1, 0].set_title("KG")
# axs[1, 0].scatter(design_plot[:,0],design_plot[:,1],c= np.array(kg_f).reshape(-1))
# axs[1, 0].legend()
axs[1, 1].set_title("mu pf")
axs[1, 1].scatter(design_plot[:,0],design_plot[:,1],c= np.array(mu_f).reshape(-1) * np.array(pf).reshape(-1))
axs[1, 1].legend()
# axs[2, 1].set_title('approximation kg Function')
# axs[2, 1].scatter(design_plot, np.array(kg_f).reshape(-1))
# axs[2, 1].legend()
# import os
# folder = "IMAGES"
# subfolder = "new_branin"
# cwd = os.getcwd()
# print("cwd", cwd)
# time_taken = time.time()
# path = cwd + "/" + folder + "/" + subfolder + '/im_' +str(time_taken) +str(self.X.shape[0]) + '.pdf'
# if os.path.isdir(cwd + "/" + folder + "/" + subfolder) == False:
# os.makedirs(cwd + "/" + folder + "/" + subfolder)
# plt.savefig(path)
plt.show()
def test_random_design_with_constraints(self):
constraints = [{'name': 'const_1', 'constraint': 'x[:,0]**2 - 1'}]
self.design_space = Design_space(self.space, constraints=constraints)
initial_points_count = 10
samples = initial_design('random', self.design_space, initial_points_count)
self.assert_samples_against_space(samples)
self.assertTrue((samples[:,0]**2 - 1 < 0).all())
def test_random_design_with_constraints(self):
constraints = [{'name': 'const_1', 'constraint': 'x[:,0]**2 - 1'}]
self.design_space = Design_space(self.space, constraints=constraints)
initial_points_count = 10
samples = initial_design('random', self.design_space, initial_points_count)
self.assert_samples_against_space(samples)
self.assertTrue((samples[:,0]**2 - 1 < 0).all())
def next_evaluation_params(self):
"""
Returns the next evaluation points requested by this optimiser
"""
if self.evaluated_init:
x_new = self.optimiser._compute_next_evaluations()
else:
x_new = initial_design(self.optimiser.initial_design_type,
self.optimiser.space, self._nb_init)
return x_new
def create_initial_jobs(self):
print("\nCreating initial jobs.\n")
self.safe_query(
"UPDATE job_runners SET current_task=\"Creating Initial Jobs\" WHERE id={0}"
.format(self.runner_id))
space = gpo.Design_space(self.domain)
X_init = initial_design('sobol', space,
self.job_properties['initial_iterations'])
for X in X_init:
self.safe_query(self.insert_query_from_X(X, True))
def true_best_value(self):
from scipy.optimize import minimize
X = initial_design('random', self.space, 1000)
fval = self.func_val(X)
anchor_point = np.array(X[np.argmin(fval)]).reshape(-1)
anchor_point = anchor_point.reshape(1, -1)
print("anchor_point",anchor_point)
best_design = minimize(self.func_val, anchor_point, method='Nelder-Mead', tol=1e-8).x
self.true_best_stats["true_best"].append(self.func_val(best_design))
self.true_best_stats["mean_gp"].append(self.model.posterior_mean(best_design))
self.true_best_stats["std gp"].append(self.model.posterior_variance(best_design, noise=False))
self.true_best_stats["pf"].append(self.probability_feasibility_multi_gp(best_design,self.model_c).reshape(-1,1))
mean = self.model_c.posterior_mean(best_design)
var = self.model_c.posterior_variance(best_design, noise=False)
residual_noise = self.model_c.posterior_variance(self.X[1], noise=False)
self.true_best_stats["mu_pf"].append(mean)
self.true_best_stats["var_pf"].append(var)
self.true_best_stats["residual_noise"].append(residual_noise)
if False:
fig, axs = plt.subplots(3, 2)
N = len(np.array(self.true_best_stats["std gp"]).reshape(-1))
GAP = np.array(np.abs(np.abs(self.true_best_stats["true_best"]).reshape(-1) - np.abs(self.true_best_stats["mean_gp"]).reshape(-1))).reshape(-1)
print("GAP len", len(GAP))
print("N",N)
axs[0, 0].set_title('GAP')
axs[0, 0].plot(range(N),GAP)
axs[0, 0].set_yscale("log")
axs[0, 1].set_title('VAR')
axs[0, 1].plot(range(N),np.array(self.true_best_stats["std gp"]).reshape(-1))
axs[0, 1].set_yscale("log")
axs[1, 0].set_title("PF")
axs[1, 0].plot(range(N),np.array(self.true_best_stats["pf"]).reshape(-1))
axs[1, 1].set_title("mu_PF")
axs[1, 1].plot(range(N),np.abs(np.array(self.true_best_stats["mu_pf"]).reshape(-1)))
axs[1, 1].set_yscale("log")
axs[2, 1].set_title("std_PF")
axs[2, 1].plot(range(N),np.sqrt(np.array(self.true_best_stats["var_pf"]).reshape(-1)))
axs[2, 1].set_yscale("log")
axs[2, 0].set_title("Irreducible noise")
axs[2, 0].plot(range(N), np.sqrt(np.array(self.true_best_stats["residual_noise"]).reshape(-1)))
axs[2, 0].set_yscale("log")
plt.show()
def _set_initial_values(self):
if self.X is None:
self.X = initial_design(self.initial_design_type, self.subspace, self.initial_design_numdata)
self.Y, _ = self.objective.evaluate(self.map_to_original_space(x=self.X))
elif self.X is not None and self.Y is None:
self.Y, _ = self.objective.evaluate(self.map_to_original_space(x=self.X))
# save initial values
self.initial_X = deepcopy(self.X)
if self.maximize:
self.initial_Y = -deepcopy(self.Y)
else:
self.initial_Y = deepcopy(self.Y)
def example_initial_design():
func = GPyOpt.objective_examples.experimentsNd.alpine1(input_dim=2)
mixed_domain = [{
'name': 'var1_2',
'type': 'continuous',
'domain': (-10, 10),
'dimensionality': 1
}, {
'name': 'var5',
'type': 'continuous',
'domain': (-1, 5)
}]
space = GPyOpt.Design_space(mixed_domain)
data_init = 500
### --- Grid design
X = initial_design('grid', space, data_init)
plt.plot(X[:, 0], X[:, 1], 'b.')
plt.title('Grid design')
### --- Random initial design
X = initial_design('random', space, data_init)
plt.plot(X[:, 0], X[:, 1], 'b.')
plt.title('Random design')
### --- Latin design
X = initial_design('latin', space, data_init)
plt.plot(X[:, 0], X[:, 1], 'b.')
plt.title('Latin design')
### --- Sobol design
X = initial_design('sobol', space, data_init)
plt.plot(X[:, 0], X[:, 1], 'b.')
plt.title('Sobol design')
pass
def step(self):
"""
Runs Bayesian Optimization every loop
"""
if self.Y.shape[0] < self.initial_design_numdata:
self.suggested_sample = initial_design('random', self.space, 1)
else:
self.suggested_sample = self._compute_next_evaluations()
self.X = np.vstack((self.X, self.suggested_sample))
# --- Update current evaluation time and function evaluations
self.num_acquisitions += 1
if self.verbosity:
print("num acquisition: {}".format(self.num_acquisitions))
return np.array(self.suggested_sample[0, :])
def test_nonrandom_designs_with_constrains(self):
constraints = [{'name': 'const_1', 'constraint': 'x[:,0]**2 - 1'}]
self.design_space = Design_space(self.space, constraints=constraints)
initial_points_count = 10
with self.assertRaises(InvalidConfigError):
initial_design('grid', self.design_space, initial_points_count)
with self.assertRaises(InvalidConfigError):
initial_design('latin', self.design_space, initial_points_count)
with self.assertRaises(InvalidConfigError):
initial_design('sobol', self.design_space, initial_points_count)
def test_nonrandom_designs_with_constrains(self):
constraints = [{'name': 'const_1', 'constrain': 'x[:,0]**2 - 1'}]
self.design_space = Design_space(self.space, constraints=constraints)
initial_points_count = 10
with self.assertRaises(InvalidConfigError):
initial_design('grid', self.design_space, initial_points_count)
with self.assertRaises(InvalidConfigError):
initial_design('latin', self.design_space, initial_points_count)
with self.assertRaises(InvalidConfigError):
initial_design('sobol', self.design_space, initial_points_count)
def init(self, max_iter=0, context=None, verbosity=False):
"""
Runs Bayesian Optimization
:param verbosity: flag to print the optimization results after each iteration (default, False).
:param context: fixes specified variables to a particular context (values) for the optimization run (default, None).
"""
# --- Save the options to print and save the results
self.max_iter = max_iter
self.verbosity = verbosity
self.context = context
# --- Initialize iterations and running time
self.time_zero = time.time()
self.cum_time = 0
self.num_acquisitions = 0
self.suggested_sample = initial_design('random', self.space, 1)
self.X = self.suggested_sample
self.Y_new = self.Y
return np.array(self.suggested_sample[0, :])
def _init_design_chooser(self):
"""
Initializes the choice of X and Y based on the selected initial design and number of points selected.
"""
# If objective function was not provided, we require some initial sample data
if self.f is None and (self.X is None or self.Y is None):
raise InvalidConfigError(
"Initial data for both X and Y is required when objective function is not provided")
# Case 1:
if self.X is None:
self.X = initial_design(
self.initial_design_type,
self.space,
self.initial_design_numdata)
self.Y, _ = self.objective.evaluate(self.X)
# Case 2
elif self.X is not None and self.Y is None:
self.Y, _ = self.objective.evaluate(self.X)
def test_random_design(self):
init_points_count = 10
samples = initial_design('random', self.design_space,
init_points_count)
self.assertEqual(len(samples), init_points_count)
self.assert_samples_against_space(samples)
def test_random_design(self):
init_points_count = 10
samples = initial_design('random', self.design_space, init_points_count)
self.assertEqual(len(samples), init_points_count)
self.assert_samples_against_space(samples)
def optimize(self,
f=None,
df=None,
f_df=None,
duplicate_manager=None,
f_aux=None):
"""
Optimizes the input function.
:param f: function to optimize.
:param df: gradient of the function to optimize.
:param f_df: returns both the function to optimize and its gradient.
"""
self.f = f
self.df = df
self.f_df = f_df
## --- Update the optimizer, in case context has beee passed.
self.optimizer = choose_optimizer(
self.optimizer_name, self.context_manager.noncontext_bounds)
## --- Selecting the anchor points and removing duplicates
X_init = initial_design(random_design_type, self.space,
self.n_starting)
fX_init = f(X_init)
scores = fX_init.flatten()
anchor_points = X_init[
np.argsort(scores)[:min(len(scores), self.n_anchor)], :]
anchor_points_values = np.sort(
scores)[0:min(len(scores), self.n_anchor)]
## -- Select the anchor points (with context)
x_min_anchor = np.atleast_2d(anchor_points[0])
fx_min_anchor = anchor_points_values[0]
x_min = np.atleast_2d(anchor_points[0])
fx_min = anchor_points_values[0]
print('anchor points')
print(anchor_points)
print(anchor_points_values)
parallel = True
if parallel:
n_cores = 4
pool = Pool(n_cores)
i = 0
while i < self.n_anchor:
points_to_optimize = anchor_points[i:i + 4, :]
optimized_points = pool.map(
self._parallel_optimization_wrapper, points_to_optimize)
x_aux, fx_aux = min(optimized_points, key=lambda t: t[1])
if fx_aux < fx_min + 1e-2:
x_min = x_aux
fx_min = fx_aux
if i > 0:
break
else:
fx_aux = f(np.atleast_2d(x_aux))
if fx_aux < fx_min + 1e-2:
x_min = x_aux
fx_min = fx_aux
if i > 0:
break
i += 4
else:
optimized_points = [
apply_optimizer(self.optimizer,
a,
f=f,
df=None,
f_df=f_df,
duplicate_manager=duplicate_manager,
context_manager=self.context_manager,
space=self.space) for a in anchor_points
]
x_min, fx_min = min(optimized_points, key=lambda t: t[1])
print('min and min value before bo')
print(x_min)
print(fx_min)
if fx_min_anchor < fx_min + 1e-3:
try:
aux_objective = GPyOpt2.core.task.SingleObjective(f)
aux_model = GPyOpt2.models.GPModel_MCMC()
aux_acq_opt = GPyOpt.optimization.AcquisitionOptimizer(
optimizer='lbfgs', space=self.space)
aux_acquisition = GPyOpt2.acquisitions.AcquisitionEI_MCMC(
aux_model, self.space, optimizer=aux_acq_opt)
aux_evaluator = GPyOpt2.core.evaluators.Sequential(
aux_acquisition)
bo = GPyOpt2.core.BO(aux_model, self.space, aux_objective,
aux_acquisition, aux_evaluator, X_init,
fX_init)
bo.run_optimization(max_iter=75)
x_min, fx_min = bo.get_results()
x_min = np.atleast_2d(x_min)
print('min and min value after bo')
print(x_min)
print(fx_min)
except:
pass
return x_min, fx_min
def get_samples(self, n_samples, log_p_function, burn_in_steps=50):
samples = initial_design('latin', self.space, n_samples)
samples_log = np.array([[i] for i in range(n_samples)])
return samples, samples_log
def verbosity_plot_2D_constrained(self):
####plots
print("generating plots")
design_plot = initial_design('random', self.space, 1000)
# precision = []
# for i in range(20):
# kg_f = -self.acquisition._compute_acq(design_plot)
# precision.append(np.array(kg_f).reshape(-1))
# print("mean precision", np.mean(precision, axis=0), "std precision", np.std(precision, axis=0), "max precision", np.max(precision, axis=0), "min precision",np.min(precision, axis=0))
# self.acquisition._gradient_sanity_check_2D(f=self.acquisition._compute_acq, grad_f = self.acquisition.acquisition_Gradients, x_value = self.suggested_sample, delta=1e-4)
Y, _ = self.objective.evaluate(design_plot)
Y = np.concatenate(Y, axis=1)
C, _ = self.constraint.evaluate(design_plot)
pf = self.probability_feasibility_multi_gp(design_plot,
self.model_c).reshape(
-1, 1)
mu_f = self.model.posterior_mean(design_plot)
bool_C = np.product(np.concatenate(C, axis=1) < 0, axis=1)
bool_C = np.array(bool_C, dtype=bool)
func_val = Y[bool_C]
mu_predicted_best = self.model.posterior_mean(self.suggested_sample)
# mu_predicted_final_best = self.model.posterior_mean(self.suggested_final_evaluation)
feasable_mu_index = np.array(pf > 0.51, dtype=bool).reshape(-1)
# HVI = self.acquisition._compute_acq(design_plot[feasable_mu_index ])
# kg_f = -self.acquisition._compute_acq(design_plot)
# HVI_optimiser = self.acquisition._compute_acq(self.suggested_sample)
# print("optimiser best", HVI_optimiser, "discretisation best", np.max(np.array(HVI).reshape(-1)))
# print("x", design_plot[np.argmax(np.array(HVI).reshape(-1))])
# x_suggested_discretisation = design_plot[np.argmax(np.array(HVI).reshape(-1))]
# print("ac with grad info optimised", self.acquisition._compute_acq_withGradients(self.suggested_sample), "ac info optimised",self.acquisition._compute_acq(self.suggested_sample))
# print("best discretisation ac", self.acquisition._compute_acq(x_suggested_discretisation ))
# print("best discretisation ac with gradients", self.acquisition._compute_acq_withGradients(x_suggested_discretisation))
# print("mu predicted best opt", mu_predicted_best[0], mu_predicted_best[1])
# print("mu predicted best discretisation", mu_f[0][np.argmax(np.array(HVI).reshape(-1))], mu_f[1][np.argmax(np.array(HVI).reshape(-1))])
fig, axs = plt.subplots(2, 2)
axs[0, 0].set_title('True PF Function')
axs[0, 0].scatter(func_val[:, 0], func_val[:, 1])
axs[0, 1].set_title("HVI")
axs[0, 1].scatter(mu_f[0], mu_f[1],
color="green") # , c=np.array(HVI).reshape(-1))
axs[0, 1].scatter(mu_f[0][feasable_mu_index],
mu_f[1][feasable_mu_index],
color="blue") #, c=np.array(HVI).reshape(-1))
axs[0, 1].scatter(mu_predicted_best[0],
mu_predicted_best[1],
color="red",
label="optimiser best")
# axs[0, 1].scatter(mu_predicted_final_best[0], mu_predicted_final_best[1], color="red", label="optimiser final best")
#axs[0, 1].scatter(mu_f[0][np.argmax(np.array(HVI).reshape(-1))], mu_f[1][np.argmax(np.array(HVI).reshape(-1))], color="red",label="discretisation best")
axs[0, 1].legend()
axs[1, 0].set_title('Opportunity Cost')
axs[1, 0].plot(range(len(self.Opportunity_Cost["Hypervolume"])),
self.Opportunity_Cost["Hypervolume"])
axs[1, 0].set_yscale("log")
axs[1, 0].legend()
Y_reccomended, _ = self.objective.evaluate(self.suggested_sample)
# Y_reccomended = np.concatenate(Y_reccomended, axis=1)
axs[1, 1].set_title('True PF Function with sampled points')
axs[1, 1].scatter(func_val[:, 0], func_val[:, 1])
axs[1, 1].scatter(Y_reccomended[0],
Y_reccomended[1],
color="red",
label="sampled")
axs[1, 1].scatter(self.Y[0], self.Y[1], color="green")
# import os
# folder = "IMAGES"
# subfolder = "new_branin"
# cwd = os.getcwd()
# print("cwd", cwd)
# time_taken = time.time()
# path = cwd + "/" + folder + "/" + subfolder + '/im_' +str(time_taken) +str(self.X.shape[0]) + '.pdf'
# if os.path.isdir(cwd + "/" + folder + "/" + subfolder) == False:
# os.makedirs(cwd + "/" + folder + "/" + subfolder)
# plt.savefig(path)
plt.show()
def verbosity_plot_2D_unconstrained(self):
####plots
print("generating plots")
design_plot = initial_design('random', self.space, 10000)
# precision = []
# for i in range(20):
# kg_f = -self.acquisition._compute_acq(design_plot)
# precision.append(np.array(kg_f).reshape(-1))
# print("mean precision", np.mean(precision, axis=0), "std precision", np.std(precision, axis=0), "max precision", np.max(precision, axis=0), "min precision",np.min(precision, axis=0))
func_val, _ = self.objective.evaluate(design_plot)
func_val = np.concatenate(func_val, axis=1)
mu_f = self.model.posterior_mean(design_plot)
var_f = self.model.posterior_variance(design_plot, noise=False)
mu_predicted_best = self.model.posterior_mean(self.suggested_sample)
HVI = self.acquisition._compute_acq(design_plot)
fig, axs = plt.subplots(2, 2)
axs[0, 0].set_title('True PF Function')
axs[0, 0].scatter(func_val[:, 0], func_val[:, 1])
print("self.suggested_sample", self.suggested_sample)
axs[0, 1].set_title("GP(X)")
axs[0,
1].scatter(
design_plot[:, 0],
design_plot[:, 1],
c=np.array(mu_f).reshape(-1)) #,c= np.array(HVI).reshape(-1))
axs[0, 1].scatter(self.suggested_sample[:, 0],
self.suggested_sample[:, 1],
color="magenta")
axs[0, 1].legend()
print("self.suggested_sample", self.suggested_sample)
axs[1, 0].set_title("Var[GP(X)]")
axs[1, 0].scatter(
design_plot[:, 0],
design_plot[:, 1],
c=np.array(var_f).reshape(-1)) #,c= np.array(HVI).reshape(-1))
axs[1, 0].scatter(self.suggested_sample[:, 0],
self.suggested_sample[:, 1],
color="magenta")
axs[1, 0].legend()
axs[1, 1].set_title("acq(X)")
axs[1,
1].scatter(
design_plot[:, 0],
design_plot[:, 1],
c=np.array(HVI).reshape(-1)) #,c= np.array(HVI).reshape(-1))
axs[1, 1].scatter(self.suggested_sample[:, 0],
self.suggested_sample[:, 1],
color="magenta")
axs[1, 1].legend()
# axs[1, 1].set_title("mu pf")
# axs[1, 1].scatter(design_plot[:,0],design_plot[:,1],c= np.array(mu_f).reshape(-1) * np.array(pf).reshape(-1))
# axs[1, 1].legend()
#
# axs[1, 0].set_title('Opportunity Cost')
# axs[1, 0].plot(range(len(self.Opportunity_Cost)), self.Opportunity_Cost)
# axs[1, 0].set_yscale("log")
# axs[1, 0].legend()
# axs[1, 1].set_title('True PF Function with sampled points')
# axs[1, 1].scatter(func_val[:, 0], func_val[:, 1])
# axs[1, 1].scatter(self.Y[0],self.Y[1], color="red", label="sampled")
# import os
# folder = "IMAGES"
# subfolder = "new_branin"
# cwd = os.getcwd()
# print("cwd", cwd)
# time_taken = time.time()
# path = cwd + "/" + folder + "/" + subfolder + '/im_' +str(time_taken) +str(self.X.shape[0]) + '.pdf'
# if os.path.isdir(cwd + "/" + folder + "/" + subfolder) == False:
# os.makedirs(cwd + "/" + folder + "/" + subfolder)
# plt.savefig(path)
plt.show()
def get(self,
num_anchor=5,
duplicate_manager=None,
unique=False,
context_manager=None):
## --- We use the context handler to remove duplicates only over the non-context variables
if context_manager and not self.space._has_bandit():
# print("In AnchorPointsGenerator: ")
# print(" space.config_space_expanded : ", self.space.config_space_expanded)
# print(" context_manager.nocontext_index_obj: ", context_manager.noncontext_bounds)
space_configuration_without_context = [
self.space.config_space_expanded[idx]
for idx in context_manager.nocontext_index_obj
]
space = Design_space(space_configuration_without_context,
context_manager.space.constraints)
add_context = lambda x: context_manager._expand_vector(x)
else:
space = self.space
add_context = lambda x: x
## --- Generate initial design
X = initial_design(self.design_type, space, self.num_samples)
if unique:
sorted_design = sorted(list({tuple(x) for x in X}))
X = space.unzip_inputs(np.vstack(sorted_design))
else:
X = space.unzip_inputs(X)
## --- Add context variables
X = add_context(X)
if duplicate_manager:
is_duplicate = duplicate_manager.is_unzipped_x_duplicate
else:
# In absence of duplicate manager, we never detect duplicates
is_duplicate = lambda _: False
non_duplicate_anchor_point_indexes = [
index for index, x in enumerate(X) if not is_duplicate(x)
]
if not non_duplicate_anchor_point_indexes:
raise FullyExploredOptimizationDomainError(
"No anchor points could be generated ({} used samples, {} requested anchor points)."
.format(self.num_samples, num_anchor))
if len(non_duplicate_anchor_point_indexes) < num_anchor:
# Since logging has not been setup yet, I do not know how to express warnings...I am using standard print for now.
print("Warning: expecting {} anchor points, only {} available.".
format(num_anchor, len(non_duplicate_anchor_point_indexes)))
X = X[non_duplicate_anchor_point_indexes, :]
scores = self.get_anchor_point_scores(X)
anchor_points = X[np.argsort(scores)[:min(len(scores), num_anchor)], :]
return anchor_points
def _marginal_acq_with_gradient(self, X, utility_params_samples):
"""
"""
marginal_acqX = np.zeros((X.shape[0], len(utility_params_samples)))
marginal_dacq_dX = np.zeros(
(X.shape[0], X.shape[1], len(utility_params_samples)))
n_h = 1 # Number of GP hyperparameters samples.
# gp_hyperparameters_samples = self.model.get_hyperparameters_samples(n_h)
n_z = len(self.Z_samples_obj) #2 # Number of samples of Z.
Z_samples = self.Z_samples_obj #np.random.normal(size=n_z)
Z_samples_c = self.Z_samples_const
for h in range(n_h):
# self.model.set_hyperparameters(h)
varX = self.model.posterior_variance(X, noise=True)
dvar_dX = self.model.posterior_variance_gradient(X)
varX_c = self.model_c.posterior_variance(X, noise=True)
dvar_c_dX = self.model_c.posterior_variance_gradient(X)
for i in range(0, len(X)):
x = np.atleast_2d(X[i])
self.model.partial_precomputation_for_covariance(x)
self.model.partial_precomputation_for_covariance_gradient(x)
self.model_c.partial_precomputation_for_covariance(x)
self.model_c.partial_precomputation_for_covariance_gradient(x)
for l in range(0, len(utility_params_samples)):
# Precompute aux1 and aux2 for computational efficiency.
aux = np.multiply(np.square(utility_params_samples[l]),
np.reciprocal(varX[:, i]))
aux2 = np.multiply(np.square(utility_params_samples[l]),
np.square(np.reciprocal(varX[:, i])))
aux_c = np.reciprocal(varX_c[:, i])
aux2_c = np.square(np.reciprocal(varX_c[:, i]))
for z in range(len(Z_samples)):
# inner function of maKG acquisition function.
def inner_func(X_inner):
X_inner = np.atleast_2d(X_inner)
muX_inner = self.model.posterior_mean(X_inner)
cov = self.model.posterior_covariance_between_points_partially_precomputed(
X_inner, x)[:, :, 0]
a = np.matmul(utility_params_samples[l], muX_inner)
# a = support[t]*muX_inner
b = np.sqrt(np.matmul(aux, np.square(cov)))
func_val = np.reshape(a + b * Z_samples[z],
(len(X_inner), 1))
grad_c = gradients(
x_new=x,
model=self.model_c,
Z=Z_samples_c[z],
aux=aux_c,
X_inner=X_inner
) # , test_samples = initial_design('random', self.space, 1000))
Fz = grad_c.compute_probability_feasibility_multi_gp(
x=X_inner, l=0)
func_val_constrained = func_val * Fz
return -func_val_constrained
# inner function of maKG acquisition function with its gradient.
def inner_func_with_gradient(X_inner):
X_inner = np.atleast_2d(X_inner)
muX_inner = self.model.posterior_mean(X_inner)
dmu_dX_inner = self.model.posterior_mean_gradient(
X_inner)
cov = self.model.posterior_covariance_between_points_partially_precomputed(
X_inner, x)[:, :, 0]
dcov_dX_inner = self.model.posterior_covariance_gradient_partially_precomputed(
X_inner, x)
a = np.matmul(utility_params_samples[l], muX_inner)
# a = support[t]*muX_inner
da_dX_inner = np.tensordot(
utility_params_samples[l],
dmu_dX_inner,
axes=1)
b = np.sqrt(np.matmul(aux, np.square(cov)))
for k in range(X_inner.shape[1]):
dcov_dX_inner[:, :, k] = np.multiply(
cov, dcov_dX_inner[:, :, k])
db_dX_inner = np.tensordot(aux,
dcov_dX_inner,
axes=1)
db_dX_inner = np.multiply(np.reciprocal(b),
db_dX_inner.T).T
func_val = np.reshape(a + b * Z_samples[z],
(len(X_inner), 1))
func_gradient = np.reshape(
da_dX_inner + db_dX_inner * Z_samples[z],
X_inner.shape)
grad_c = gradients(x_new=x,
model=self.model_c,
Z=Z_samples_c[z],
aux=aux_c,
X_inner=X_inner,
precompute_grad=True)
Fz, grad_Fz = grad_c.compute_probability_feasibility_multi_gp(
x=X_inner, l=0, gradient_flag=True)
func_val_constrained = func_val * Fz
func_gradient_constrained = np.array(
func_val).reshape(-1) * grad_Fz.reshape(
-1) + Fz.reshape(
-1) * func_gradient.reshape(-1)
return -func_val_constrained, -func_gradient_constrained
x_opt, opt_val = self.optimizer.optimize_inner_func(
f=inner_func, f_df=inner_func_with_gradient)
marginal_acqX[i, l] -= opt_val
x_opt = np.atleast_2d(x_opt)
#mu x opt calculations
muX_inner = self.model.posterior_mean(x_opt)
cov = self.model.posterior_covariance_between_points_partially_precomputed(
x_opt, x)[:, :, 0]
a = np.matmul(utility_params_samples[l], muX_inner)
b = np.sqrt(np.matmul(aux, np.square(cov)))
mu_xopt = np.reshape(a + b * Z_samples[z],
(len(x_opt), 1))
#grad x opt calculations
cov_opt = self.model.posterior_covariance_between_points_partially_precomputed(
x_opt, x)[:, 0, 0]
dcov_opt_dx = self.model.posterior_covariance_gradient(
x, x_opt)[:, 0, :]
b = np.sqrt(np.dot(aux, np.square(cov_opt)))
grad_mu_xopt = 0.5 * Z_samples[z] * np.reciprocal(
b) * np.matmul(
aux2, (2 * np.multiply(varX[:, i] * cov_opt,
dcov_opt_dx.T) -
np.multiply(np.square(cov_opt),
dvar_dX[:, i, :].T)).T)
grad_c = gradients(x_new=x,
model=self.model_c,
Z=Z_samples_c[z],
xopt=x_opt,
aux=aux_c,
aux2=aux2_c,
varX=varX_c[:, i],
dvar_dX=dvar_c_dX[:, i, :],
test_samples=initial_design(
'random', self.space, 1000))
Fz_xopt, grad_Fz_xopt = grad_c.compute_probability_feasibility_multi_gp_xopt(
xopt=x_opt, gradient_flag=True)
grad_f_val_xopt = np.array(mu_xopt).reshape(
-1) * np.array(grad_Fz_xopt).reshape(
-1) + np.array(Fz_xopt).reshape(-1) * np.array(
grad_mu_xopt).reshape(-1)
marginal_dacq_dX[i, :,
l] = grad_f_val_xopt # grad_f_val
marginal_acqX = marginal_acqX / (n_h * n_z)
marginal_dacq_dX = marginal_dacq_dX / (n_h * n_z)
return marginal_acqX, marginal_dacq_dX
def run_optimization(self,
max_iter=1,
max_time=np.inf,
eps=1e-8,
context=None,
verbosity=False,
evaluations_file=None):
"""
Runs Bayesian Optimization for a number 'max_iter' of iterations (after the initial exploration data)
:param max_iter: exploration horizon, or number of acquisitions. If nothing is provided optimizes the current acquisition.
:param max_time: maximum exploration horizon in seconds.
:param eps: minimum distance between two consecutive x's to keep running the model.
:param context: fixes specified variables to a particular context (values) for the optimization run (default, None).
:param verbosity: flag to print the optimization results after each iteration (default, False).
:param evaluations_file: filename of the file where the evaluated points and corresponding evaluations are saved (default, None).
"""
self.verbosity = verbosity
if self.objective is None:
raise InvalidConfigError(
"Cannot run the optimization loop without the objective function"
)
# --- Save the options to print and save the results
self.verbosity = verbosity
self.evaluations_file = evaluations_file
self.context = context
# --- Setting up stop conditions
self.eps = eps
if (max_iter is None) and (max_time is None):
self.max_iter = 0
self.max_time = np.inf
elif (max_iter is None) and (max_time is not None):
self.max_iter = np.inf
self.max_time = max_time
elif (max_iter is not None) and (max_time is None):
self.max_iter = max_iter
self.max_time = np.inf
else:
self.max_iter = max_iter
self.max_time = max_time
# print("------------------------TRAINING HYPERS----------------------")
# self._get_hyperparameters()
# --- Initial function evaluation and model fitting
if self.X is not None and self.Y is None:
self.Y, cost_values = self.objective.evaluate(self.X)
if self.constraint is not None:
self.C, cost_values = self.constraint.evaluate(self.X)
if self.cost.cost_type == 'evaluation_time':
self.cost.update_cost_model(self.X, cost_values)
#self.model.updateModel(self.X,self.Y)
# --- Initialize iterations and running time
self.time_zero = time.time()
self.cum_time = 0
self.num_acquisitions = 0
self.suggested_sample = self.X
self.Y_new = self.Y
self.Opportunity_Cost = []
value_so_far = []
# --- Initialize time cost of the evaluations
print("-----------------------MAIN LOOP STARTS----------------------")
Opportunity_Cost = []
self.true_best_stats = {
"true_best": [],
"mean_gp": [],
"std gp": [],
"pf": [],
"mu_pf": [],
"var_pf": [],
"residual_noise": []
}
while (self.max_iter > self.num_acquisitions):
self._update_model()
if self.constraint is None:
self.Opportunity_Cost_caller_unconstrained()
else:
self.Opportunity_Cost_caller_constrained()
print("maKG optimizer")
start = time.time()
self.suggested_sample = self._compute_next_evaluations()
finish = time.time()
print("time optimisation point X", finish - start)
if verbosity:
####plots
design_plot = initial_design('random', self.space, 1000)
ac_f = self.expected_improvement(design_plot)
Y, _ = self.objective.evaluate(design_plot)
C, _ = self.constraint.evaluate(design_plot)
pf = self.probability_feasibility_multi_gp(
design_plot, self.model_c).reshape(-1, 1)
mu_f = self.model.predict(design_plot)[0]
bool_C = np.product(np.concatenate(C, axis=1) < 0, axis=1)
func_val = Y * bool_C.reshape(-1, 1)
print("self.suggested_sample", self.suggested_sample)
fig, axs = plt.subplots(2, 2)
axs[0, 0].set_title('True Function')
axs[0, 0].scatter(design_plot[:, 0],
design_plot[:, 1],
c=np.array(func_val).reshape(-1))
axs[0, 0].scatter(self.X[:, 0],
self.X[:, 1],
color="red",
label="sampled")
axs[0, 0].scatter(self.suggested_sample[:, 0],
self.suggested_sample[:, 1],
marker="x",
color="red",
label="suggested")
axs[0, 1].set_title('approximation Acqu Function')
axs[0, 1].scatter(design_plot[:, 0],
design_plot[:, 1],
c=np.array(ac_f).reshape(-1))
axs[1, 0].set_title("convergence")
axs[1, 0].plot(range(len(self.Opportunity_Cost)),
np.array(self.Opportunity_Cost).reshape(-1))
axs[1, 0].set_yscale("log")
axs[1, 1].set_title("mu")
axs[1, 1].scatter(design_plot[:, 0],
design_plot[:, 1],
c=np.array(mu_f).reshape(-1) *
np.array(pf).reshape(-1))
plt.show()
self.X = np.vstack((self.X, self.suggested_sample))
# --- Evaluate *f* in X, augment Y and update cost function (if needed)
self.evaluate_objective()
print("X", self.X, "Y", self.Y, "C", self.C, "OC",
self.Opportunity_Cost)
# --- Update current evaluation time and function evaluations
self.cum_time = time.time() - self.time_zero
self.num_acquisitions += 1
return self.X, self.Y, self.C, self.Opportunity_Cost
def _dropout_random(self, embedded_idx):
return initial_design(
'random', get_subspace(space=self.space,
subspace_idx=embedded_idx), 1)[0]
'name': 'tau_0',
'type': 'continuous',
'domain': scaled_parameter_bounds['tau_0']
},
{
'name': 'nu',
'type': 'continuous',
'domain': scaled_parameter_bounds['nu']
},
]
domain = [
item for item in full_domain
if item['name'] not in fixed_parameters.keys()
]
space = gpo.Design_space(domain)
X_init = initial_design('sobol', space, n_initial_points)
def value_from_X(X, param_name):
names = [item['name'] for item in domain]
if param_name in names:
return X[names.index(param_name)]
else:
return fixed_parameters[param_name]
queries = []
for X in X_init:
query = "INSERT INTO jobs (job_name, is_initial, fish_group, machine_assigned, start_time, completed_time, objective_function,\
delta_0, alpha_tau, alpha_d, beta, A_0, t_s_0, discriminability, flicker_frequency, tau_0, nu) VALUES \
(\"{job_name}\", TRUE, \"{fish_group}\", NULL, NULL, NULL, NULL, \
def run_optimization(self, max_iter = 1, max_time = np.inf, eps = 1e-8, context = None, verbosity=False, evaluations_file = None):
"""
Runs Bayesian Optimization for a number 'max_iter' of iterations (after the initial exploration data)
:param max_iter: exploration horizon, or number of acquisitions. If nothing is provided optimizes the current acquisition.
:param max_time: maximum exploration horizon in seconds.
:param eps: minimum distance between two consecutive x's to keep running the model.
:param context: fixes specified variables to a particular context (values) for the optimization run (default, None).
:param verbosity: flag to print the optimization results after each iteration (default, False).
:param evaluations_file: filename of the file where the evaluated points and corresponding evaluations are saved (default, None).
"""
if self.objective is None:
raise InvalidConfigError("Cannot run the optimization loop without the objective function")
# --- Save the options to print and save the results
self.verbosity = verbosity
self.evaluations_file = evaluations_file
self.context = context
# --- Setting up stop conditions
self.eps = eps
if (max_iter is None) and (max_time is None):
self.max_iter = 0
self.max_time = np.inf
elif (max_iter is None) and (max_time is not None):
self.max_iter = np.inf
self.max_time = max_time
elif (max_iter is not None) and (max_time is None):
self.max_iter = max_iter
self.max_time = np.inf
else:
self.max_iter = max_iter
self.max_time = max_time
# --- Initial function evaluation and model fitting
if self.X is not None and self.Y is None:
self.Y, cost_values = self.objective.evaluate(self.X)
self.C, cost_values = self.constraint.evaluate(self.X)
if self.cost.cost_type == 'evaluation_time':
self.cost.update_cost_model(self.X, cost_values)
#self.model.updateModel(self.X,self.Y)
# --- Initialize iterations and running time
self.time_zero = time.time()
self.cum_time = 0
self.num_acquisitions = 0
self.suggested_sample = self.X
self.Y_new = self.Y
self.Opportunity_Cost = []
value_so_far = []
# --- Initialize time cost of the evaluations
print("MAIN LOOP STARTS")
Opportunity_Cost = []
while (self.max_iter > self.num_acquisitions ):
# self._update_model()
print("maKG optimizer")
start = time.time()
self.suggested_sample = initial_design('random', self.space, 1)
finish = time.time()
print("time optimisation point X", finish - start)
if verbosity:
self.verbosity_plot_2D()
print("self.Opportunity_Cost",self.Opportunity_Cost)
self.X = np.vstack((self.X,self.suggested_sample))
# --- Evaluate *f* in X, augment Y and update cost function (if needed)
self.evaluate_objective()
# --- Update current evaluation time and function evaluations
self.cum_time = time.time() - self.time_zero
self.num_acquisitions += 1
print("optimize_final_evaluation")
self.optimize_final_evaluation()
print("self.X, self.Y, self.C , self.Opportunity_Cost",self.X, self.Y, self.C , self.Opportunity_Cost)
return self.X, self.Y, self.C , self.Opportunity_Cost
def suggest_sample(self, number_of_samples=1):
"""
Returns a suggested next point to evaluate.
"""
utility_parameter_sample = self.utility.sample_parameter(
number_of_samples=1)
model_sample = self.model.get_copy_of_model_sample()
X_evaluated = np.copy(model_sample.X)
Y_evaluated = np.copy(model_sample.Y)
self.X_aux = np.copy(X_evaluated)
self.Y_aux = np.copy(Y_evaluated)
def objective_func_sample(d):
X_new = np.vstack(
[np.append(d, theta) for theta in self.scenario_support])
Y_new = model_sample.posterior_samples_f(X_new,
size=1,
full_cov=True)
self.X_aux = np.vstack((self.X_aux, X_new))
self.Y_aux = np.vstack((self.Y_aux, Y_new))
model_sample.set_XY(self.X_aux, self.Y_aux)
val = 0.
for w in range(self.scenario_support_cardinality):
val += self.scenario_prob_dist[w] * self.utility.eval_func(
Y_new[w, 0], utility_parameter_sample)
return -val
d0 = initial_design('random', self.decision_space, 1)
try:
#argmax = self.decision_space_optimizer.optimize(d0, objective_func_sample, maxfevals=200)[0]
argmax = apply_optimizer(
self.decision_space_optimizer,
d0,
f=objective_func_sample,
context_manager=self.decision_space_context_manager,
space=self.decision_space,
maxfevals=200)[0]
except:
argmax = d0
aux_grid = np.vstack(
[np.append(argmax, theta) for theta in self.scenario_support])
self.model.set_hyperparameters(0)
var = self.model.posterior_variance(aux_grid)
for h in range(1, self.number_of_gp_hyps_samples):
self.model.set_hyperparameters(h)
var += self.model.posterior_variance(aux_grid)
var = var[:, 0]
index = np.argmax(var)
suggested_sample = np.append(argmax, self.scenario_support[index])
use_suggested_sample = True
i = 0
min_distance = np.infty
while use_suggested_sample and i < X_evaluated.shape[0]:
distance_to_evaluated_point = euclidean(X_evaluated[i, :],
suggested_sample)
if distance_to_evaluated_point < min_distance:
min_distance = distance_to_evaluated_point
if distance_to_evaluated_point < 1e-1 / np.sqrt(
X_evaluated.shape[1]):
use_suggested_sample = False
i += 1
print('Minimum distance to previously evaluated point is: {}'.format(
min_distance))
if not use_suggested_sample:
print(
'Suggested point is to close to previously evaluated point; swithching to max expected value sampling policy.'
)
def expectation_objective_func(d):
d = np.atleast_2d(d)
func_val = 0.
cross_product_grid = np.vstack(
[np.append(d, theta) for theta in self.scenario_support])
for h in range(self.number_of_gp_hyps_samples):
self.model.set_hyperparameters(h)
mean, var = self.model.predict_noiseless(
cross_product_grid)
for w in range(self.scenario_support_cardinality):
expectation_utility = self.expectation_utility.eval_func(
mean[w, 0], var[w, 0], utility_parameter_sample)
func_val += self.scenario_prob_dist[
w] * expectation_utility
func_val /= self.number_of_gp_hyps_samples
func_val = func_val[:, 0]
return -func_val
#argmax = self.decision_space_optimizer.optimize(d0, expectation_objective_func)[0]
argmax = apply_optimizer(
self.decision_space_optimizer,
d0,
f=expectation_objective_func,
context_manager=self.decision_space_context_manager,
space=self.decision_space)[0]
aux_grid = np.vstack(
[np.append(argmax, theta) for theta in self.scenario_support])
self.model.set_hyperparameters(0)
var = self.model.posterior_variance(aux_grid)
for h in range(1, self.number_of_gp_hyps_samples):
self.model.set_hyperparameters(h)
var += self.model.posterior_variance(aux_grid)
var = var[:, 0]
index = np.argmax(var)
suggested_sample = np.append(argmax, self.scenario_support[index])
use_suggested_sample = True
i = 0
min_distance = np.infty
while use_suggested_sample and i < X_evaluated.shape[0]:
distance_to_evaluated_point = euclidean(
X_evaluated[i, :], suggested_sample)
if distance_to_evaluated_point < min_distance:
min_distance = distance_to_evaluated_point
if distance_to_evaluated_point < 1e-2 / np.sqrt(
X_evaluated.shape[1]):
use_suggested_sample = False
i += 1
print(
'Minimum distance to previously evaluated point is: {}'.format(
min_distance))
if not use_suggested_sample:
print(
'Suggested point is to close to previously evaluated point; swithching to max variance sampling policy.'
)
def posterior_variance(x):
self.model.set_hyperparameters(0)
var = self.model.posterior_variance(x)
for h in range(1, self.number_of_gp_hyps_samples):
self.model.set_hyperparameters(h)
var += self.model.posterior_variance(x)
var = var[:, 0]
return -var
x0 = initial_design('random', self.decision_context_space, 1)
#suggested_sample = self.decision_context_space_optimizer.optimize(x0, posterior_variance)[0]
suggested_sample = apply_optimizer(
self.decision_context_space_optimizer,
x0,
f=posterior_variance,
context_manager=self.decision_context_space_context_manager,
space=self.decision_context_space)[0]
suggested_sample = np.atleast_2d(suggested_sample)
return suggested_sample
