tfp.experimental.bayesopt.acquisition.StudentTProcessExpectedImprovement

tfp.experimental.bayesopt.acquisition.StudentTProcessExpectedImprovement(
    predictive_distribution, observations, seed=None, exploration=0.01
)

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 5-dimensional index points and associated observations.
index_points = np.random.uniform(size=[10, 5])
observations = np.random.uniform(size=[10])

# Build a Student T Process regression model over the function values at
# `predictive_index_points` conditioned on observations.
predictive_index_points = np.random.uniform(size=[8, 5])
dist = tfd.StudentTProcessRegressionModel(
    kernel=tfpk.MaternFiveHalves(),
    df=5.,
    observation_index_points=index_points,
    observations=observations,
    predictive_index_points=predictive_index_points)

# Define a Student T Process Expected Improvement acquisition function.
stp_ei = tfp_acq.StudentTProcessExpectedImprovement(
    predictive_distribution=dist,
    observations=observations,
    exploration=0.02)

# Evaluate the acquisition function at `predictive_index_points`.
acq_fn_vals = stp_ei()  # Has shape [8].

# Evaluate the acquisition function at a new set of predictive index points.
new_pred_index_points = np.random.uniform(size=[6, 5])
acq_fn_vals = stp_ei(pred_index_points)  # Has shape [6].

__call__(
    **kwargs
)