tfp.experimental.bayesopt.acquisition.GaussianProcessUpperConfidenceBound
Stay organized with collections
Save and categorize content based on your preferences.
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].
Args |
predictive_distribution
|
tfd.Distribution -like, the distribution over
observations at a set of index points. Must have mean , stddev
methods.
|
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.
|
exploration
|
Exploitation-exploration trade-off parameter.
|
Attributes |
exploration
|
|
is_parallel
|
Python bool indicating whether the acquisition function is parallel.
Parallel (batched) acquisition functions evaluate batches of points rather
than single points.
|
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 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 ).
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2023-11-21 UTC.
[{
"type": "thumb-down",
"id": "missingTheInformationINeed",
"label":"Missing the information I need"
},{
"type": "thumb-down",
"id": "tooComplicatedTooManySteps",
"label":"Too complicated / too many steps"
},{
"type": "thumb-down",
"id": "outOfDate",
"label":"Out of date"
},{
"type": "thumb-down",
"id": "samplesCodeIssue",
"label":"Samples / code issue"
},{
"type": "thumb-down",
"id": "otherDown",
"label":"Other"
}]
[{
"type": "thumb-up",
"id": "easyToUnderstand",
"label":"Easy to understand"
},{
"type": "thumb-up",
"id": "solvedMyProblem",
"label":"Solved my problem"
},{
"type": "thumb-up",
"id": "otherUp",
"label":"Other"
}]
{"lastModified": "Last updated 2023-11-21 UTC."}
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-11-21 UTC."],[],[]]