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Gaussian Process probability of improvement acquisition function.
Inherits From: AcquisitionFunction
tfp.experimental.bayesopt.acquisition.GaussianProcessProbabilityOfImprovement(
predictive_distribution, observations, seed=None
)
Computes the analytic sequential probability of improvement for a Gaussian process model relative to observed data.
Requires that predictive_distribution
has mean
and stddev
methods.
Examples
Build and evaluate a GP Probability of Improvement 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 4-dimensional index points and associated observations.
index_points = np.random.uniform(size=[10, 4])
observations = np.random.uniform(size=[10])
# Build a GP regression model.
dist = tfd.GaussianProcessRegressionModel(
kernel=tfpk.ExponentiatedQuadratic(),
observation_index_points=index_points,
observations=observations)
gp_poi = tfp_acq.GaussianProcessProbabilityOfImprovement(
predictive_distribution=dist,
observations=observations)
# Evaluate the acquisition function at a set of predictive index points.
pred_index_points = np.random.uniform(size=[6, 4])
acq_fn_vals = gp_poi(pred_index_points) # Has shape [6].
Methods
__call__
__call__(
**kwargs
)
Computes analytic GP probability of improvement.
Args | |
---|---|
**kwargs
|
Keyword args passed on to the mean and stddev methods of
predictive_distribution .
|
Returns | |
---|---|
Probability of improvement at index points implied by
predictive_distribution (or overridden in **kwargs ).
|