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.

plot_contour

plot_edf

plot_hypervolume_history

plot_intermediate_values

plot_optimization_history

plot_parallel_coordinate

plot_param_importances

plot_pareto_front

plot_rank

plot_slice

plot_terminator_improvement

plot_timeline

Gallery generated by Sphinx-Gallery

Note

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

matplotlib