Class or Function Names GPEISampler (alias for optuna.samplers.GPSampler) GPPISampler GPUCBSampler GPTSSampler Installation pip install scipy torch Overview Optuna’s built-in GPSampler only supports Expected Improvement (EI) as an acquisition function. This package extends GPSampler with three additional acquisition functions commonly used in Bayesian optimization:
Sampler Acquisition Function Description GPEISampler Expected Improvement (EI) Alias for optuna.samplers.GPSampler. Balances improvement magnitude and probability. GPPISampler Probability of Improvement (PI) Selects the point most likely to improve over the current best.
Class or Function Names MESSampler Installation pip install scipy torch Overview Optuna’s acquisition functions all score a candidate by improvement over the incumbent (LogEI, LogPI) or by a posterior quantile (UCB, LCB). Max-value entropy search scores it instead by information: how much observing f(x) is expected to reduce the entropy of the distribution of the maximum value y* = max_x f(x).
Writing mu(x), sigma(x) for the posterior mean and standard deviation, phi and Psi for the standard normal PDF and CDF, and gamma = (y* - mu(x)) / sigma(x), the acquisition is