optuna.visualization.plot_param_importances¶
- optuna.visualization.plot_param_importances(study: optuna.study.Study, evaluator: optuna.importance._base.BaseImportanceEvaluator = None, params: Optional[List[str]] = None) go.Figure [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 study = optuna.create_study(sampler=optuna.samplers.RandomSampler()) 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 – 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
plotly.graph_objs.Figure
object.