optuna.visualization

The visualization module provides utility functions for plotting the optimization process using plotly and matplotlib. Plotting functions generally take a Study object and optional parameters are passed as a list to the params argument.

Note

In the optuna.visualization module, the following functions use plotly to create figures, but JupyterLab cannot render them by default. Please follow this installation guide to show figures in JupyterLab.

Note

The plot_param_importances() requires the Python package of scikit-learn.

`optuna.visualization.plot_contour`	Plot the parameter relationship as contour plot in a study.
`optuna.visualization.plot_edf`	Plot the objective value EDF (empirical distribution function) of a study.
`optuna.visualization.plot_hypervolume_history`	Plot hypervolume history of all trials in a study.
`optuna.visualization.plot_intermediate_values`	Plot intermediate values of all trials in a study.
`optuna.visualization.plot_optimization_history`	Plot optimization history of all trials in a study.
`optuna.visualization.plot_parallel_coordinate`	Plot the high-dimensional parameter relationships in a study.
`optuna.visualization.plot_param_importances`	Plot hyperparameter importances.
`optuna.visualization.plot_pareto_front`	Plot the Pareto front of a study.
`optuna.visualization.plot_rank`	Plot parameter relations as scatter plots with colors indicating ranks of target value.
`optuna.visualization.plot_slice`	Plot the parameter relationship as slice plot in a study.
`optuna.visualization.plot_terminator_improvement`	Plot the potentials for future objective improvement.
`optuna.visualization.plot_timeline`	Plot the timeline of a study.
`optuna.visualization.is_available`	Returns whether visualization with plotly is available or not.

Note

The following optuna.visualization.matplotlib module uses Matplotlib as a backend.

optuna.visualization.matplotlib