optuna.visualization

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_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.

• optuna.visualization.matplotlib