optuna.visualization.plot_optimization_history

optuna.visualization.plot_optimization_history(study, *, target=None, target_name='Objective Value', error_bar=False)[source]

Plot optimization history of all trials in a study.

Example

The following code snippet shows how to plot optimization history.

import optuna


def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_categorical("y", [-1, 0, 1])
    return x ** 2 + y


sampler = optuna.samplers.TPESampler(seed=10)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=10)

fig = optuna.visualization.plot_optimization_history(study)
fig.show()
Parameters
  • study (Union[optuna.study.study.Study, Sequence[optuna.study.study.Study]]) – A Study object whose trials are plotted for their target values. You can pass multiple studies if you want to compare those optimization histories.

  • target (Optional[Callable[[optuna.trial._frozen.FrozenTrial], float]]) –

    A function to specify the value to display. If it is None and study is being used for single-objective optimization, the objective values are plotted.

    Note

    Specify this argument if study is being used for multi-objective optimization.

  • target_name (str) – Target’s name to display on the axis label and the legend.

  • error_bar (bool) – A flag to show the error bar.

Returns

A plotly.graph_objs.Figure object.

Raises

ValueError – If target is None and study is being used for multi-objective optimization.

Return type

plotly.graph_objs._figure.Figure