optuna.visualization.matplotlib.plot_param_importances

optuna.visualization.matplotlib.plot_param_importances(study: optuna.study.Study, evaluator: Optional[optuna.importance._base.BaseImportanceEvaluator] = None, params: Optional[List[str]] = None) → matplotlib.axes._axes.Axes

Plot hyperparameter importances with Matplotlib.

See also

Please refer to optuna.visualization.plot_param_importances() for an example.

Example

The following code snippet shows how to plot hyperparameter importances.

import optuna


def objective(trial):
    x = trial.suggest_int("x", 0, 2)
    y = trial.suggest_float("y", -1.0, 1.0)
    z = trial.suggest_float("z", 0.0, 1.5)
    return x ** 2 + y ** 3 - z ** 4


sampler = optuna.samplers.RandomSampler(seed=10)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=100)

optuna.visualization.matplotlib.plot_param_importances(study)

Parameters

• study – An optimized study.

• evaluator – An importance evaluator object that specifies which algorithm to base the importance assessment on. Defaults to FanovaImportanceEvaluator.

• params – A list of names of parameters to assess. If None, all parameters that are present in all of the completed trials are assessed.

Returns

A matplotlib.axes.Axes object.

Note

Added in v2.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.2.0.