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&")
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]
model = bo.get_model()
f, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1 = plot_model(model, X_lower, X_upper, ax1)
ax1 = plot_objective_function(objective_function, X_lower, X_upper, X, Y, ax1)
ax1.legend()
acquisition_func = EI(model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, par=0.1)
ax2 = plot_acquisition_function(acquisition_func, X_lower, X_upper, ax2)
plt.legend()
plt.savefig("bo.png")
# 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)
# 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.maximize()
# Evaluate the point and add the new observation to our set of previous seen points
new_y = task.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
f, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1 = plot_objective_function(task.objective_function, task.X_lower, task.X_upper, X, Y, ax1)
ax1 = plot_model(model, task.X_lower, task.X_upper, ax1)
ax2 = plot_acquisition_function(acquisition_func, task.X_lower, task.X_upper, ax2)
plt.show(block=True)
