class LogEITestCase1(unittest.TestCase):
def setUp(self):
self.x = np.array([[0.62971589], [0.63273273], [0.17867868],
[0.17447447], [1.88558559]])
self.y = np.array([[-3.69925653], [-3.66221988], [-3.65560591],
[-3.58907791], [-8.06925984]])
self.kernel = GPy.kern.RBF(input_dim=1,
variance=30.1646253727,
lengthscale=0.435343653946)
self.noise = 1e-20
self.model = GPyModel(self.kernel,
noise_variance=self.noise,
optimize=False)
self.model.train(self.x, self.y)
def test(self):
X_upper = np.array([2.1])
X_lower = np.array([-2.1])
x_test = np.array([[1.7], [2.0]])
log_ei_estimator = LogEI(self.model,
X_lower,
X_upper,
compute_incumbent=compute_incumbent)
assert log_ei_estimator(x_test[0, np.newaxis])[0] > -np.Infinity
assert log_ei_estimator(x_test[1, np.newaxis])[0] > -np.Infinity
assert (log_ei_estimator(self.x[-1, np.newaxis])[0]) == -np.Infinity
class PITestCase1(unittest.TestCase):
def setUp(self):
self.x = np.array([[0.62971589], [0.63273273], [0.17867868],
[0.17447447], [1.88558559]])
self.y = np.array([[-3.69925653], [-3.66221988], [-3.65560591],
[-3.58907791], [-8.06925984]])
self.kernel = GPy.kern.RBF(input_dim=1,
variance=30.1646253727,
lengthscale=0.435343653946)
self.noise = 1e-20
self.model = GPyModel(self.kernel,
noise_variance=self.noise,
optimize=False)
self.model.train(self.x, self.y)
def test(self):
X_upper = np.array([2.1])
X_lower = np.array([-2.1])
x_test = np.array([[1.7], [2.0]])
pi_estimator = PI(self.model, X_lower, X_upper)
assert pi_estimator(x_test[0, np.newaxis],
incumbent=np.array([1.88558559]))[0] > 0.0
assert pi_estimator(x_test[1, np.newaxis],
incumbent=np.array([1.88558559]))[0] > 0.0
self.assertAlmostEqual(pi_estimator(self.x[-1, np.newaxis],
incumbent=np.array([1.88558559
]))[0],
0.0,
delta=10E-5)
class EITestCase1(unittest.TestCase):
def setUp(self):
self.x = np.array([[0.62971589], [0.63273273], [0.17867868], [0.17447447], [1.88558559]])
self.y = np.array([[-3.69925653], [-3.66221988], [-3.65560591], [-3.58907791], [-8.06925984]])
self.kernel = GPy.kern.RBF(input_dim=1, variance=30.1646253727, lengthscale=0.435343653946)
self.noise = 1e-20
self.model = GPyModel(self.kernel, noise_variance=self.noise, optimize=False)
self.model.train(self.x, self.y)
def test(self):
X_upper = np.array([2.1])
X_lower = np.array([-2.1])
best = np.argmin(self.y)
incumbent = self.x[best]
ei_par = EI(self.model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, par=0.0)
out0 = ei_par(incumbent[:, np.newaxis], derivative=True)
value0 = out0[0]
derivative0 = out0[1]
assert(value0[0] <= 1e-5)
x_value = incumbent + np.random.random_integers(1, 10) / 1000.
out1 = ei_par(x_value[:, np.newaxis], derivative=True)
value1 = out1[0]
derivative1 = out1[1]
assert(np.all(value0 < value1))
assert(np.all(np.abs(derivative0) < np.abs(derivative1)))
def run():
# Defining the bounds and dimensions of the input space
X_lower = np.array([0])
X_upper = np.array([6])
dims = 1
# Set the method that we will use to optimize the acquisition function
maximizer = stochastic_local_search
# Defining the method to model the objective function
kernel = GPy.kern.Matern52(input_dim=dims)
model = GPyModel(kernel, optimize=True, noise_variance=1e-4, num_restarts=10)
# The acquisition function that we optimize in order to pick a new x
acquisition_func = EI(
model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, par=0.1
) # par is the minimum improvement that a point has to obtain
# Draw one random point and evaluate it to initialize BO
X = np.array([np.random.uniform(X_lower, X_upper, dims)])
Y = objective_function(X)
# Fit the model on the data we observed so far
model.train(X, Y)
# Update the acquisition function model with the retrained model
acquisition_func.update(model)
# Optimize the acquisition function to obtain a new point
new_x = maximizer(acquisition_func, X_lower, X_upper)
# Evaluate the point and add the new observation to our set of previous seen points
new_y = objective_function(np.array(new_x))
X = np.append(X, new_x, axis=0)
Y = np.append(Y, new_y, axis=0)
# Visualize the objective function, model and the acquisition function
fig = plt.figure()
# Sub plot for the model and the objective function
ax1 = fig.add_subplot(2, 1, 1)
# Sub plot for the acquisition function
ax2 = fig.add_subplot(2, 1, 2)
resolution = 0.1
# Call plot_model function
ax1 = plotting.plot_model(model, X_lower, X_upper, ax1, resolution, "b", "blue", "Prosterior Mean", 3, True)
# Call plot_objective_function
ax1 = plotting.plot_objective_function(
objective_function, X_lower, X_upper, X, Y, ax1, resolution, "black", "ObjectiveFunction", True
)
ax1.set_title("Model + Objective Function")
# Call plot_acquisition_function
ax2 = plotting.plot_acquisition_function(
acquisition_func, X_lower, X_upper, X, ax2, resolution, "AcquisitionFunction", True
)
plt.savefig("test2.png")
os.system("eog test2.png&")
class PITestCase1(unittest.TestCase):
def setUp(self):
self.x = np.array([[0.62971589], [0.63273273], [0.17867868], [0.17447447], [1.88558559]])
self.y = np.array([[-3.69925653], [-3.66221988], [-3.65560591], [-3.58907791], [-8.06925984]])
self.kernel = GPy.kern.RBF(input_dim=1, variance=30.1646253727, lengthscale=0.435343653946)
self.noise = 1e-20
self.model = GPyModel(self.kernel, noise_variance=self.noise, optimize=False)
self.model.train(self.x, self.y)
def test(self):
X_upper = np.array([2.1])
X_lower = np.array([-2.1])
x_test = np.array([[1.7], [2.0]])
pi_estimator = PI(self.model, X_lower, X_upper)
assert pi_estimator(x_test[0, np.newaxis], incumbent=np.array([1.88558559]))[0] > 0.0
assert pi_estimator(x_test[1, np.newaxis], incumbent=np.array([1.88558559]))[0] > 0.0
self.assertAlmostEqual(pi_estimator(self.x[-1, np.newaxis], incumbent=np.array([1.88558559]))[0], 0.0, delta=10E-5)
class LogEITestCase1(unittest.TestCase):
def setUp(self):
self.x = np.array([[0.62971589], [0.63273273], [0.17867868], [0.17447447], [1.88558559]])
self.y = np.array([[-3.69925653], [-3.66221988], [-3.65560591], [-3.58907791], [-8.06925984]])
self.kernel = GPy.kern.RBF(input_dim=1, variance=30.1646253727, lengthscale=0.435343653946)
self.noise = 1e-20
self.model = GPyModel(self.kernel, noise_variance=self.noise, optimize=False)
self.model.train(self.x, self.y)
def test(self):
X_upper = np.array([2.1])
X_lower = np.array([-2.1])
x_test = np.array([[1.7], [2.0]])
log_ei_estimator = LogEI(self.model, X_lower, X_upper, compute_incumbent=compute_incumbent)
assert log_ei_estimator(x_test[0, np.newaxis])[0] > -np.Infinity
assert log_ei_estimator(x_test[1, np.newaxis])[0] > -np.Infinity
assert (log_ei_estimator(self.x[-1, np.newaxis])[0]) == -np.Infinity
class EITestCase1(unittest.TestCase):
def setUp(self):
self.x = np.array([[0.62971589], [0.63273273], [0.17867868],
[0.17447447], [1.88558559]])
self.y = np.array([[-3.69925653], [-3.66221988], [-3.65560591],
[-3.58907791], [-8.06925984]])
self.kernel = GPy.kern.RBF(input_dim=1,
variance=30.1646253727,
lengthscale=0.435343653946)
self.noise = 1e-20
self.model = GPyModel(self.kernel,
noise_variance=self.noise,
optimize=False)
self.model.train(self.x, self.y)
def test(self):
X_upper = np.array([2.1])
X_lower = np.array([-2.1])
best = np.argmin(self.y)
incumbent = self.x[best]
ei_par = EI(self.model,
X_upper=X_upper,
X_lower=X_lower,
compute_incumbent=compute_incumbent,
par=0.0)
out0 = ei_par(incumbent[:, np.newaxis], derivative=True)
value0 = out0[0]
derivative0 = out0[1]
assert (value0[0] <= 1e-5)
x_value = incumbent + np.random.random_integers(1, 10) / 1000.
out1 = ei_par(x_value[:, np.newaxis], derivative=True)
value1 = out1[0]
derivative1 = out1[1]
assert (np.all(value0 < value1))
assert (np.all(np.abs(derivative0) < np.abs(derivative1)))
yv = np.array([[
mixexpert(X,
Y,
xv[0][0],
xv[0][1],
2,
2,
cvfolds=cvfolds,
slowness=slowness,
ploterr=True)
]])
print yv
for i in xrange(500):
# Fit the model on the data we observed so far
model.train(xv, yv)
if acqf == 'ucb':
deltab = 0.5
acquisition_func.par = 1 * np.sqrt(2. * np.log(
(i + 1)**(2) * 2 * np.pi**2 / (3 * deltab)) + 2. * dims * np.log(
(i + 1)**(2) * dims * np.log(4 * dims / deltab)**0.5))
print 'par', acquisition_func.par
# Update the acquisition function model with the retrained model
acquisition_func.update(model)
# Optimize the acquisition function to obtain a new point
new_x = maximizer(acquisition_func, X_lower, X_upper)
print new_x
# Evaluate the point and add the new observation to our set of previous seen points
# Defining the method to model the objective function
kernel = GPy.kern.Matern52(input_dim=dims)
model = GPyModel(kernel, optimize=True, noise_variance=1e-4, num_restarts=10)
# The acquisition function that we optimize in order to pick a new x
acquisition_func = EI(model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, par=0.1) # par is the minimum improvement that a point has to obtain
# Draw one random point and evaluate it to initialize BO
X = np.array([np.random.uniform(X_lower, X_upper, dims)])
Y = objective_function(X)
# This is the main Bayesian optimization loop
for i in xrange(10):
# Fit the model on the data we observed so far
model.train(X, Y)
# Update the acquisition function model with the retrained model
acquisition_func.update(model)
# Optimize the acquisition function to obtain a new point
new_x = maximizer(acquisition_func, X_lower, X_upper)
# Evaluate the point and add the new observation to our set of previous seen points
new_y = objective_function(np.array(new_x))
X = np.append(X, new_x, axis=0)
Y = np.append(Y, new_y, axis=0)
# Visualize the objective function, model and the acquisition function
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
# The acquisition function that we optimize in order to pick a new x
acquisition_func = EI(
model,
X_upper=X_upper,
X_lower=X_lower,
compute_incumbent=compute_incumbent,
par=0.1) # par is the minimum improvement that a point has to obtain
# Draw one random point and evaluate it to initialize BO
X = np.array([np.random.uniform(X_lower, X_upper, dims)])
Y = objective_function(X)
# This is the main Bayesian optimization loop
for i in xrange(10):
# Fit the model on the data we observed so far
model.train(X, Y)
# Update the acquisition function model with the retrained model
acquisition_func.update(model)
# Optimize the acquisition function to obtain a new point
new_x = maximizer(acquisition_func, X_lower, X_upper)
# Evaluate the point and add the new observation to our set of previous seen points
new_y = objective_function(np.array(new_x))
X = np.append(X, new_x, axis=0)
Y = np.append(Y, new_y, axis=0)
# Visualize the objective function, model and the acquisition function
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
