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&")
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)))
from robo import BayesianOptimization
# def objective_function(x):
# return np.sin(x) + 0.1 * np.cos(10 * x)
def objective_function(x):
return np.sin(3 * x) * 4 * (x - 1) * (x + 2)
X_lower = np.array([0])
X_upper = np.array([6])
dims = 1
kernel = GPy.kern.Matern52(input_dim=dims, lengthscale=0.01)
model = GPyModel(kernel, optimize=True, noise_variance=1e-8, num_restarts=10)
acquisition_func = EI(model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, par=0.1)
maximizer = DIRECT
bo = BayesianOptimization(acquisition_fkt=acquisition_func,
model=model,
maximize_fkt=maximizer,
X_lower=X_lower,
X_upper=X_upper,
dims=dims,
objective_fkt=objective_function)
bo.run(num_iterations=5)
X, Y = bo.get_observations()
X = X[:-1]
Y = Y[:-1]
##
print dims
# 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.Exponential(input_dim=dims)
model = GPyModel(kernel, optimize=True, noise_variance=1e-5, num_restarts=10)
acqf = 'ucb'
# The acquisition function that we optimize in order to pick a new x
if acqf == 'ei':
acquisition_func = EI(
model,
X_upper=X_upper,
X_lower=X_lower,
compute_incumbent=compute_incumbent,
par=0.05) # par is the minimum improvement that a point has to obtain
else:
acquisition_func = UCB(model,
X_upper=X_upper,
X_lower=X_lower,
compute_incumbent=compute_incumbent,
par=5)
xv = np.array([np.random.uniform(X_lower, X_upper, dims)])
xv = np.array([[-10.46778014, -7.01362049]])
print xv
dimfold = 1
slowness = X.shape[0] / dimfold
cvfolds = X.shape[0] / dimfold
def objective_function(x):
return np.sin(3 * x) * 4 * (x - 1) * (x + 2)
X_lower = np.array([0])
X_upper = np.array([6])
dims = 1
kernel = GPy.kern.Matern52(input_dim=dims)
model = GPyModel(kernel, optimize=True, noise_variance=1e-4, num_restarts=10)
proposal_measurement = EI(model,
X_upper=X_upper,
X_lower=X_lower,
compute_incumbent=compute_incumbent,
par=0.1)
acquisition_func = Entropy(model,
X_lower,
X_upper,
sampling_acquisition=proposal_measurement)
maximizer = grid_search
bo = BayesianOptimization(acquisition_fkt=acquisition_func,
model=model,
maximize_fkt=maximizer,
X_lower=X_lower,
X_upper=X_upper,
dims=dims,
objective_fkt=objective_function)
return np.sin(3 * x) * 4 * (x - 1) * (x + 2)
# 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)
# 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)
# 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)
# 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)
def __init__(self):
self.X_lower = np.array([0])
self.X_upper = np.array([7])
self.n_dims = 1
def objective_function(self, x):
return np.sin(3 * x) * 4 * (x - 1) * (x + 2)
task = ExampleTask()
# Defining the method to model the objective function
kernel = GPy.kern.Matern52(input_dim=task.n_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=task.X_upper, X_lower=task.X_lower, compute_incumbent=compute_incumbent, par=0.1) # par is the minimum improvement that a point has to obtain
# Set the method that we will use to optimize the acquisition function
maximizer = GridSearch(acquisition_func, task.X_lower, task.X_upper)
# Draw one random point and evaluate it to initialize BO
X = np.array([np.random.uniform(task.X_lower, task.X_upper, task.n_dims)])
Y = task.objective_function(X)
# This is the main Bayesian optimization loop
for i in range(10):
# Fit the model on the data we observed so far
model.train(X, Y)
