optuna.visualizationΒΆ

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

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_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-dimentional 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_slice

Plot the parameter relationship as slice plot in 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.