plot_param_importances

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

Plot hyperparameter importances.

See also

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

Parameters:

• study (Study) – An optimized study.

• evaluator (BaseImportanceEvaluator | None) –

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

  Note

  FanovaImportanceEvaluator takes over 1 minute when given a study that contains 1000+ trials. We published optuna-fast-fanova library, that is a Cython accelerated fANOVA implementation. By using it, you can get hyperparameter importances within a few seconds.

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

• target (Callable[[FrozenTrial], float] | None) –

  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. For multi-objective optimization, all objectives will be plotted if target is None.

  Note

  This argument can be used to specify which objective to plot if study is being used for multi-objective optimization. For example, to get only the hyperparameter importance of the first objective, use target=lambda t: t.values[0] for the target parameter.

• target_name (str) – Target’s name to display on the legend. Names set via set_metric_names() will be used if target is None, overriding this argument.

Returns:

A plotly.graph_objects.Figure object.

Return type:

Figure

The following code snippet shows how to plot hyperparameter importances.

import optuna
from plotly.io import show


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)
show(fig)