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) fig = optuna.visualization.plot_param_importances(study) fig.show()
See also
This function visualizes the results of
optuna.importance.get_param_importances()
.- Parameters
study (Study) – An optimized study.
evaluator (Optional[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[[FrozenTrial], float]]) –
A function to specify the value to display. If it is
None
andstudy
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. For example, to get the hyperparameter importance of the first objective, usetarget=lambda t: t.values[0]
for the target parameter.target_name (str) – Target’s name to display on the axis label.
- Returns
A
plotly.graph_objs.Figure
object.- Return type
Figure