Abstract This visualization module enables users to plot the target value over time with standard error bands. This module is especially convenient when we use parallel optimization such as Asynchronous optimization simulation.
Class or Function Names plot_target_over_time Installation This module requires the following dependencies:
matplotlib numpy APIs plot_target_over_time(study_list, *, ax=None, states=None, target=None, target_direction=None, cumtime_func=None, log_time_scale=True, n_steps=100, color=None, **plot_kwargs) study_list: A list of optuna.Study objects. Each study is treated as one run, and results are averaged across them.
Class or Function Names plot_sampling_speed Installation This module requires the following dependencies:
matplotlib scipy Example A minimal example would be the following:
from collections import defaultdict import matplotlib.pyplot as plt import optuna import optunahub def objective(trial) -> float: return trial.suggest_float("x", -5, 5)**2 studies = defaultdict(lambda: []) for i in range(3): sampler = optuna.samplers.RandomSampler() study = optuna.create_study(sampler=sampler) study.optimize(objective, timeout=1.0) studies["Random"].append(study) sampler = optuna.samplers.TPESampler() study = optuna.create_study(sampler=sampler) study.optimize(objective, timeout=3.0) studies["TPE"].append(study) plot_sampling_speed = optunahub.