def test_save_gp_2d(self):
k = GPy.kern.Matern52(input_dim=2)
m = GPModel(kernel=k)
myBopt = BayesianOptimization(f=self.f_2d, domain=self.domain_2d, model=m)
myBopt.run_optimization(max_iter=1, verbosity=False)
myBopt.save_models(self.outfile_path)
self.check_output_model_file(['Iteration'])
def test_save_gp_2d(self):
k = GPy.kern.Matern52(input_dim=2)
m = GPModel(kernel=k)
myBopt = BayesianOptimization(f=self.f_2d,
domain=self.domain_2d,
model=m)
myBopt.run_optimization(max_iter=1, verbosity=False)
myBopt.save_models(self.outfile_path)
self.check_output_model_file(['Iteration'])
def test_save_gp_2d_ard(self):
"""
This was previously an edge-case, when some parameters were vectors, the naming of the columns was incorrect
"""
k = GPy.kern.Matern52(input_dim=2, ARD=True)
m = GPModel(kernel=k)
myBopt = BayesianOptimization(f=self.f_2d, domain=self.domain_2d, model=m)
myBopt.run_optimization(max_iter=1, verbosity=False)
myBopt.save_models(self.outfile_path)
self.check_output_model_file(['Iteration'])
def test_save_gp_2d_ard(self):
"""
This was previously an edge-case, when some parameters were vectors, the naming of the columns was incorrect
"""
k = GPy.kern.Matern52(input_dim=2, ARD=True)
m = GPModel(kernel=k)
myBopt = BayesianOptimization(f=self.f_2d,
domain=self.domain_2d,
model=m)
myBopt.run_optimization(max_iter=1, verbosity=False)
myBopt.save_models(self.outfile_path)
self.check_output_model_file(['Iteration'])
Y_init = np.reshape(np.array(array_y), (-1, 1))
for ind, cost in enumerate(array_cost):
list_sampled_x.append(array_sampled_x[ind, :])
list_y.append(array_y[ind])
list_cost.append(array_cost[ind])
max_iter = max_iter_ - len(list_y)
obj_count = len(list_y) - 1
myBO = BayesianOptimization(f=obj_val,
domain=domain,
constraints=constraints,
X=X_init,
Y=Y_init,
initial_design_numdata=initial_design_numdata,
initial_design_type='latin',
acquisition_type=acq,
maximize=is_max)
myBO.run_optimization(max_iter=max_iter)
myBO.save_report(report_file=txt_path + '_report.txt')
myBO.save_evaluations(evaluations_file=txt_path + '_evaluations.txt')
myBO.save_models(txt_path + '_models.txt')
result = {
'sampled_x': np.array(list_sampled_x),
'observed_y': np.array(list_y),
'cost': np.array(list_cost),
'random_seeds': random_seeds
}
with open(txt_path + '_result.txt', "w") as file:
file.write(str(result))
with open(txt_path + '_result.pickle', "wb") as file:
pickle.dump(result, file)
def test_save_gp_default(self):
myBopt = BayesianOptimization(f=self.f_2d, domain=self.domain_2d)
myBopt.run_optimization(max_iter=1, verbosity=False)
myBopt.save_models(self.outfile_path)
self.check_output_model_file(['Iteration'])
def test_save_gp_no_filename(self):
myBopt = BayesianOptimization(f=self.f_2d, domain=self.domain_2d)
myBopt.run_optimization(max_iter=1, verbosity=False)
# Need to at least pass in filename or buffer
self.assertRaises(TypeError, lambda: myBopt.save_models())
def test_save_gp_default_no_iters(self):
myBopt = BayesianOptimization(f=self.f_2d, domain=self.domain_2d)
# Exception should be raised as no iterations have been carried out yet
self.assertRaises(ValueError,
lambda: myBopt.save_models(self.outfile_path))
def test_save_gp_default(self):
myBopt = BayesianOptimization(f=self.f_2d, domain=self.domain_2d)
myBopt.run_optimization(max_iter=1, verbosity=False)
myBopt.save_models(self.outfile_path)
self.check_output_model_file(['Iteration'])
def test_save_gp_no_filename(self):
myBopt = BayesianOptimization(f=self.f_2d, domain=self.domain_2d)
myBopt.run_optimization(max_iter=1, verbosity=False)
# Need to at least pass in filename or buffer
self.assertRaises(TypeError, lambda: myBopt.save_models())
def test_save_gp_default_no_iters(self):
myBopt = BayesianOptimization(f=self.f_2d, domain=self.domain_2d)
# Exception should be raised as no iterations have been carried out yet
self.assertRaises(ValueError, lambda: myBopt.save_models(self.outfile_path))
