optuna.visualization.plot_param_importances

optuna.visualization.plot_param_importances(study, evaluator=None, params=None, *, target=None, target_name='Objective Value')[source]

Plot hyperparameter importances.

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.plot_param_importances(study)

See also

This function visualizes the results of optuna.importance.get_param_importances().

Parameters
  • study (optuna.study.Study) – An optimized study.

  • evaluator (Optional[optuna.importance._base.BaseImportanceEvaluator]) – An importance evaluator object that specifies which algorithm to base the importance assessment on. Defaults to FanovaImportanceEvaluator.

  • params (Optional[List[str]]) – A list of names of parameters to assess. If None, all parameters that are present in all of the completed trials are assessed.

  • target (Optional[Callable[[optuna.trial._frozen.FrozenTrial], float]]) –

    A function to specify the value to display. If it is None and study is being used for single-objective optimization, the objective values are plotted.

    Note

    Specify this argument if study is being used for multi-objective optimization.

  • target_name (str) – Target’s name to display on the axis label.

Returns

A plotly.graph_objs.Figure object.

Raises

ValueError – If target is None and study is being used for multi-objective optimization.

Return type

plotly.graph_objs._figure.Figure