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bayesian_optimization.py
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bayesian_optimization.py
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"""This Bayesian optimization demo using gradient-based optimization
to find the next query point."""
import matplotlib.pyplot as plt
from gaussian_process import make_gp_funs, rbf_covariance
from scipy.optimize import minimize
import autograd.numpy as np
import autograd.numpy.random as npr
from autograd import value_and_grad
from autograd.scipy.stats import norm
def probability_of_improvement(mean, std, max_so_far):
return norm.cdf(max_so_far, mean, std)
def expected_new_max(mean, std, max_so_far):
return (
max_so_far
- (mean - max_so_far) * norm.cdf(mean, max_so_far, std)
+ std * norm.pdf(mean, max_so_far, std)
)
def init_covariance_params(num_params):
return np.zeros(num_params)
def defaultmax(x, default=-np.inf):
if x.size == 0:
return default
return np.max(x)
def bayesian_optimize(func, domain_min, domain_max, num_iters=20, callback=None):
D = len(domain_min)
num_params, predict, log_marginal_likelihood = make_gp_funs(rbf_covariance, num_cov_params=D + 1)
model_params = init_covariance_params(num_params)
def optimize_gp_params(init_params, X, y):
log_hyperprior = lambda params: np.sum(norm.logpdf(params, 0.0, 100.0))
objective = lambda params: -log_marginal_likelihood(params, X, y) - log_hyperprior(params)
return minimize(value_and_grad(objective), init_params, jac=True, method="CG").x
def choose_next_point(domain_min, domain_max, acquisition_function, num_tries=15, rs=npr.RandomState(0)):
"""Uses gradient-based optimization to find next query point."""
init_points = rs.rand(num_tries, D) * (domain_max - domain_min) + domain_min
grad_obj = value_and_grad(lambda x: -acquisition_function(x))
def optimize_point(init_point):
print(".", end="")
result = minimize(
grad_obj,
x0=init_point,
jac=True,
method="L-BFGS-B",
options={"maxiter": 10},
bounds=list(zip(domain_min, domain_max)),
)
return result.x, acquisition_function(result.x)
optimzed_points, optimized_values = list(zip(*list(map(optimize_point, init_points))))
print()
best_ix = np.argmax(optimized_values)
return np.atleast_2d(optimzed_points[best_ix])
# Start by evaluating once in the middle of the domain.
X = np.zeros((0, D))
y = np.zeros(0)
X = np.concatenate((X, np.reshape((domain_max - domain_min) / 2.0, (D, 1))))
y = np.concatenate((y, np.reshape(np.array(func(X)), (1,))))
for i in range(num_iters):
if i > 1:
print("Optimizing model parameters...")
model_params = optimize_gp_params(model_params, X, y)
print("Choosing where to look next", end="")
def predict_func(xstar):
mean, cov = predict(model_params, X, y, xstar)
return mean, np.sqrt(np.diag(cov))
def acquisition_function(xstar):
xstar = np.atleast_2d(xstar) # To work around a bug in scipy.minimize
mean, std = predict_func(xstar)
return expected_new_max(mean, std, defaultmax(y))
next_point = choose_next_point(domain_min, domain_max, acquisition_function)
print("Evaluating expensive function...")
new_value = func(next_point)
X = np.concatenate((X, next_point))
y = np.concatenate((y, np.reshape(np.array(new_value), (1,))))
if callback:
callback(X, y, predict_func, acquisition_function, next_point, new_value)
best_ix = np.argmax(y)
return X[best_ix, :], y[best_ix]
if __name__ == "__main__":
def example_function(x):
return np.sum(x * np.sin(10.0 * x) + x) - 1
domain_min = np.array([0.0])
domain_max = np.array([1.1])
# Set up figure.
fig = plt.figure(figsize=(12, 8), facecolor="white")
ax = fig.add_subplot(111, frameon=False)
plt.show(block=False)
def callback(X, y, predict_func, acquisition_function, next_point, new_value):
plt.cla()
# Show posterior marginals.
plot_xs = np.reshape(np.linspace(domain_min, domain_max, 300), (300, 1))
pred_mean, pred_std = predict_func(plot_xs)
ax.plot(plot_xs, pred_mean, "b")
ax.fill(
np.concatenate([plot_xs, plot_xs[::-1]]),
np.concatenate([pred_mean - 1.96 * pred_std, (pred_mean + 1.96 * pred_std)[::-1]]),
alpha=0.15,
fc="Blue",
ec="None",
)
ax.plot(X, y, "kx")
ax.plot(next_point, new_value, "ro")
alphas = acquisition_function(plot_xs)
ax.plot(plot_xs, alphas, "r")
ax.set_ylim([-1.5, 1.5])
ax.set_xticks([])
ax.set_yticks([])
plt.draw()
plt.pause(1)
best_x, best_y = bayesian_optimize(example_function, domain_min, domain_max, callback=callback)