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.
See also
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 (Study) – An optimized study.
evaluator (BaseImportanceEvaluator | None) – An importance evaluator object that specifies which algorithm to base the importance assessment on. Defaults to
FanovaImportanceEvaluator
.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
andstudy
is being used for single-objective optimization, the objective values are plotted. For multi-objective optimization, all objectives will be plotted iftarget
isNone
.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, usetarget=lambda t: t.values[0]
for the target parameter.target_name (str) – Target’s name to display on the axis label. Names set via
set_metric_names()
will be used iftarget
isNone
, overriding this argument.
- Returns:
A
matplotlib.axes.Axes
object.- Return type:
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.