View source on GitHub |
Analytical Gaussian Process upper confidence bound acquisition function.
Inherits From: AcquisitionFunction
tfp.experimental.bayesopt.acquisition.GaussianProcessUpperConfidenceBound(
predictive_distribution, observations, seed=None, exploration=0.01
)
Computes the analytic sequential upper confidence bound for a Gaussian process model.
Requires that predictive_distribution
has a .mean
, stddev
method.
Examples
Build and evaluate a GP Upper Confidence Bound acquisition function.
import numpy as np
import tensorflow_probability as tfp
tfd = tfp.distributions
tfpk = tfp.math.psd_kernels
tfp_acq = tfp.experimental.bayesopt.acquisition
# Sample 12 5-dimensional index points and associated observations.
index_points = np.random.uniform(size=[12, 5])
observations = np.random.uniform(size=[12])
# Build a GP regression model conditioned on observed data.
dist = tfd.GaussianProcessRegressionModel(
kernel=tfpk.ExponentiatedQuadratic(),
observation_index_points=index_points,
observations=observations)
# Build a GP upper confidence bound acquisition function.
gp_ucb = tfp_acq.GausianProcessUpperConfidenceBound(
predictive_distribution=dist,
observations=observations,
exploration=0.05,
num_samples=int(2e4))
# Evaluate the acquisition function at a set of 6 predictive index points.
pred_index_points = np.random.uniform(size=[6, 5])
acq_fn_vals = gp_ucb(pred_index_points) # Has shape [6].
Methods
__call__
__call__(
**kwargs
)
Computes analytic GP upper confidence bound.
Args | |
---|---|
**kwargs
|
Keyword args passed on to the mean and stddev methods of
predictive_distribution .
|
Returns | |
---|---|
Upper confidence bound at index points implied by
predictive_distribution (or overridden in **kwargs ).
|