# optuna.visualization.matplotlib.plot_param_importances¶

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

Plot hyperparameter importances with Matplotlib.

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 (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 matplotlib.axes.Axes object.

Raises

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

Return type

matplotlib.axes._axes.Axes

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.