# Visualization¶

optuna.visualization.plot_contour(study, params=None)[source]

Plot the parameter relationship as contour plot in a study.

Note that, If a parameter contains missing values, a trial with missing values is not plotted.

Example

The following code snippet shows how to plot the parameter relationship as contour plot.

import optuna

def objective(trial):
...

study = optuna.create_study()
study.optimize(objective, n_trials=100)

optuna.visualization.plot_contour(study, params=['param_a', 'param_b'])

Parameters: study – A Study object whose trials are plotted for their objective values. params – Parameter list to visualize. The default is all parameters.
optuna.visualization.plot_intermediate_values(study)[source]

Plot intermediate values of all trials in a study.

Example

The following code snippet shows how to plot intermediate values.

import optuna

def objective(trial):
# Intermediate values are supposed to be reported inside the objective function.
...

study = optuna.create_study()
study.optimize(objective, n_trials=100)

optuna.visualization.plot_intermediate_values(study)

Parameters: study – A Study object whose trials are plotted for their intermediate values.
optuna.visualization.plot_optimization_history(study)[source]

Plot optimization history of all trials in a study.

Example

The following code snippet shows how to plot optimization history.

import optuna

def objective(trial):
...

study = optuna.create_study()
study.optimize(objective ,n_trials=100)

optuna.visualization.plot_optimization_history(study)

Parameters: study – A Study object whose trials are plotted for their objective values.
optuna.visualization.plot_parallel_coordinate(study, params=None)[source]

Plot the high-dimentional parameter relationships in a study.

Note that, If a parameter contains missing values, a trial with missing values is not plotted.

Example

The following code snippet shows how to plot the high-dimentional parameter relationships.

import optuna

def objective(trial):
...

study = optuna.create_study()
study.optimize(objective, n_trials=100)

optuna.visualization.plot_parallel_coordinate(study, params=['param_a', 'param_b'])

Parameters: study – A Study object whose trials are plotted for their objective values. params – Parameter list to visualize. The default is all parameters.
optuna.visualization.plot_slice(study, params=None)[source]

Plot the parameter relationship as slice plot in a study.

Note that, If a parameter contains missing values, a trial with missing values is not plotted.

Example

The following code snippet shows how to plot the parameter relationship as slice plot.

import optuna

def objective(trial):
...

study = optuna.create_study()
study.optimize(objective, n_trials=100)

optuna.visualization.plot_slice(study, params=['param_a', 'param_b'])

Parameters: study – A Study object whose trials are plotted for their objective values. params – Parameter list to visualize. The default is all parameters.
optuna.visualization.is_available()[source]

Returns whether visualization is available or not.

Note

visualization module depends on plotly version 4.0.0 or higher. If a supported version of plotly isn’t installed in your environment, this function will return False. In such case, please execute \$ pip install -U plotly>=4.0.0 to install plotly.

Returns: True if visualization is available, False otherwise.