plot_hypervolume_history

optuna.visualization.matplotlib.plot_hypervolume_history(study, reference_point)

Plot hypervolume history of all trials in a study with Matplotlib.

Note

You need to adjust the size of the plot by yourself using plt.tight_layout() or plt.savefig(IMAGE_NAME, bbox_inches='tight').

Parameters:

• study (Study) – A Study object whose trials are plotted for their hypervolumes. The number of objectives must be 2 or more.

• reference_point (Sequence[float]) – A reference point to use for hypervolume computation. The dimension of the reference point must be the same as the number of objectives.

Returns:

A matplotlib.axes.Axes object.

Return type:

Axes

Note

Added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0.

The following code snippet shows how to plot optimization history.

/home/docs/checkouts/readthedocs.org/user_builds/optuna/checkouts/stable/docs/visualization_matplotlib_examples/optuna.visualization.matplotlib.hypervolume_history.py:29: ExperimentalWarning:

plot_hypervolume_history is experimental (supported from v3.3.0). The interface can change in the future.

import optuna
import matplotlib.pyplot as plt


def objective(trial):
    x = trial.suggest_float("x", 0, 5)
    y = trial.suggest_float("y", 0, 3)

    v0 = 4 * x ** 2 + 4 * y ** 2
    v1 = (x - 5) ** 2 + (y - 5) ** 2
    return v0, v1


study = optuna.create_study(directions=["minimize", "minimize"])
study.optimize(objective, n_trials=50)

reference_point=[100, 50]
optuna.visualization.matplotlib.plot_hypervolume_history(study, reference_point)
plt.tight_layout()