tfp.experimental.bayesopt.acquisition.GaussianProcessMaxValueEntropySearch

Max-value entropy search acquisition function.

Inherits From: AcquisitionFunction

tfp.experimental.bayesopt.acquisition.GaussianProcessMaxValueEntropySearch(
    predictive_distribution, observations, seed=None, num_max_value_samples=100
)

Computes the sequential max-value entropy search acquisition function.

Requires that predictive_distribution has a .mean, stddev method.

Examples

Build and evaluate a Gausian Process Maximum Value Entropy Search 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 10 20-dimensional index points and associated observations.
index_points = np.random.uniform(size=[10, 20])
observations = np.random.uniform(size=[10])

# Build a Gaussian Process regression model.
dist = tfd.GaussianProcessRegressionModel(
    kernel=tfpk.MaternFiveHalves(),
    observation_index_points=index_points,
    observations=observations)

# Define a GP max value entropy search acquisition function.
gp_mes = tfp_acq.GaussianProcessMaxValueEntropySearch(
    predictive_distribution=dist,
    observations=observations,
    num_max_value_samples=200)

# Evaluate the acquisition function at a set of predictive index points.
pred_index_points = np.random.uniform(size=[6, 20])
acq_fn_vals = gp_mes(pred_index_points)

References

[1] Z. Wang, S. Jegelka. Max-value Entropy Search for Efficient Bayesian Optimization. https://arxiv.org/abs/1703.01968

Args
predictive_distribution	tfd.Distribution-like, the distribution over observations at a set of index points.
observations	Float Tensor of observations. Shape has the form [b1, ..., bB, e], where e is the number of index points (such that the event shape of predictive_distribution is [e]) and [b1, ..., bB] is broadcastable with the batch shape of predictive_distribution.
seed	PRNG seed; see tfp.random.sanitize_seed for details.
num_max_value_samples	The number of samples to use for the max-value distribution.

Attributes
is_parallel	Python bool indicating whether the acquisition function is parallel. Parallel (batched) acquisition functions evaluate batches of points rather than single points.
num_max_value_samples
observations	Float Tensor of observations.
predictive_distribution	The distribution over observations at a set of index points.
seed	PRNG seed.

Methods

__call__

View source

__call__(
    **kwargs
)

Computes the max-value entropy search acquisition function.

Args
**kwargs	Keyword args passed on to the mean and stddev methods of predictive_distribution.

Returns
Acquisition function values at index points implied by predictive_distribution (or overridden in **kwargs).