Visualization¶

Note

visualization module uses 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(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):
    x = trial.suggest_uniform('x', -100, 100)
    y = trial.suggest_categorical('y', [-1, 0, 1])
    return x ** 2 + y

study = optuna.create_study()
study.optimize(objective, n_trials=10)

optuna.visualization.plot_contour(study, params=['x', 'y'])

Parameters:	study – A `Study` object whose trials are plotted for their objective values. params – Parameter list to visualize. The default is all parameters.
Returns:	A `plotly.graph_objs.Figure` object.

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):
    x = trial.suggest_uniform('x', -100, 100)
    y = trial.suggest_categorical('y', [-1, 0, 1])
    return x ** 2 + y

study = optuna.create_study()
study.optimize(objective, n_trials=10)

optuna.visualization.plot_intermediate_values(study)

Parameters:	study – A `Study` object whose trials are plotted for their intermediate values.
Returns:	A `plotly.graph_objs.Figure` object.

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):
    x = trial.suggest_uniform('x', -100, 100)
    y = trial.suggest_categorical('y', [-1, 0, 1])
    return x ** 2 + y

study = optuna.create_study()
study.optimize(objective, n_trials=10)

optuna.visualization.plot_optimization_history(study)

Parameters:	study – A `Study` object whose trials are plotted for their objective values.
Returns:	A `plotly.graph_objs.Figure` object.

optuna.visualization.plot_parallel_coordinate(study: optuna.study.Study, params: Optional[List[str]] = None) → go.Figure[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):
    x = trial.suggest_uniform('x', -100, 100)
    y = trial.suggest_categorical('y', [-1, 0, 1])
    return x ** 2 + y

study = optuna.create_study()
study.optimize(objective, n_trials=10)

optuna.visualization.plot_parallel_coordinate(study, params=['x', 'y'])

Parameters:	study – A `Study` object whose trials are plotted for their objective values. params – Parameter list to visualize. The default is all parameters.
Returns:	A `plotly.graph_objs.Figure` object.

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):
    x = trial.suggest_uniform('x', -100, 100)
    y = trial.suggest_categorical('y', [-1, 0, 1])
    return x ** 2 + y

study = optuna.create_study()
study.optimize(objective, n_trials=10)

optuna.visualization.plot_slice(study, params=['x', 'y'])

Parameters:	study – A `Study` object whose trials are plotted for their objective values. params – Parameter list to visualize. The default is all parameters.
Returns:	A `plotly.graph_objs.Figure` object.

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.