optuna.visualization.matplotlib.plot_pareto_front

optuna.visualization.matplotlib.plot_pareto_front(study, *, target_names=None, include_dominated_trials=True, axis_order=None)

Plot the Pareto front of a study.

See also

Please refer to optuna.visualization.plot_pareto_front() for an example.

Example

The following code snippet shows how to plot the Pareto front of a study.

import optuna


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)

optuna.visualization.matplotlib.plot_pareto_front(study)

Parameters

• study (optuna.study.study.Study) – A Study object whose trials are plotted for their objective values.

• target_names (Optional[List[str]]) – Objective name list used as the axis titles. If None is specified, “Objective {objective_index}” is used instead.

• include_dominated_trials (bool) – A flag to include all dominated trial’s objective values.

• axis_order (Optional[List[int]]) – A list of indices indicating the axis order. If None is specified, default order is used.

Returns

A matplotlib.axes.Axes object.

Raises

ValueError – If the number of objectives of study isn’t 2 or 3.

Return type

matplotlib.axes._axes.Axes

Note

Added in v2.8.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.8.0.