plot_optimization_history

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

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

See also

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

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 | Sequence[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 (Callable[[FrozenTrial], float] | None) –

  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 matplotlib.axes.Axes object.

Return type:

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.

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.optimization_history.py:26: ExperimentalWarning:

plot_optimization_history is experimental (supported from v2.2.0). The interface can change in the future.

import optuna
import matplotlib.pyplot as plt


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)
plt.tight_layout()