optuna.visualization.matplotlib.plot_optimization_history

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

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

See also

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

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)

optuna.visualization.matplotlib.plot_optimization_history(study)
../../../_images/optuna-visualization-matplotlib-plot_optimization_history-1.png
Parameters
  • study (optuna.study.Study) – A Study object whose trials are plotted for their target values.

  • 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.

Returns

A matplotlib.axes.Axes object.

Raises

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

Return type

matplotlib.axes._axes.Axes

Note

Added in v2.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.2.0.