def test_grid_search(self):
maximizer = GridSearch(self.objective_function, self.lower, self.upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert len(x.shape) == 1
assert np.all(x >= self.lower)
assert np.all(x <= self.upper)
def test_grid_search(self):
maximizer = GridSearch(self.objective_function, self.lower, self.upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert len(x.shape) == 1
assert np.all(x >= self.lower)
assert np.all(x <= self.upper)
def test_grid_search(self):
maximizer = GridSearch(self.acquisition_func, self.X_lower, self.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.dims
assert np.all(x[:, 0] >= self.X_lower[0])
assert np.all(x[:, 0] <= self.X_upper[0])
assert np.all(x < self.X_upper)
def test_grid_search(self):
maximizer = GridSearch(self.acquisition_func, self.X_lower,
self.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.dims
assert np.all(x[:, 0] >= self.X_lower[0])
assert np.all(x[:, 0] <= self.X_upper[0])
assert np.all(x < self.X_upper)
# 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)
