Abstract This package is the TPE’s Acquisition Visualizer. It provides callback and plot functions.
APIs Class: TPEAcquisitionVisualizer TPEAcquisitionVisualizer() __call__(self, study: optuna.study.Study, trial: optuna.trial.FrozenTrial) -> None Callback function to collect tpe sampler’s acquisition information.
Args:
study (optuna.study.Study): The study object. trial (optuna.trial.FrozenTrial): The trial object for which the callback is called. Returns: None
plot(self, study: optuna.study.Study, trial_number: int, param_name: str,) -> plt.Figure Plots the TPE acquisition for a given trial and parameter.
Abstract This package provides a sampler using Google Vizier. It is a simple wrapper around Vizier’s Python client, enabling its optimization capabilities within the Optuna framework.
Class or Function Names VizierSampler Example import optuna from optuna.visualization import plot_hypervolume_history import optunahub wfg = optunahub.load_module("benchmarks/wfg") wfg4 = wfg.Problem(function_id=4, n_objectives=2, dimension=5, k=1) vizier = optunahub.load_module("samplers/vizier") study = optuna.create_study( study_name="Vizier", sampler=vizier.VizierSampler(search_space=wfg4.search_space), directions=wfg4.directions, ) study.optimize(wfg4, n_trials=100) plot_hypervolume_history(study, reference_point=[0.5, 0.5]) Benchmarks Following images are the benchmark of Vizier and other samplers.