plot_param_importances

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

使用 Matplotlib 绘制超参数重要性图表。

参见

请参考 optuna.visualization.plot_param_importances() 的示例。

参数:

• study (Study) – 一个优化的研究。

• evaluator (BaseImportanceEvaluator | None) – 一个重要性评估器对象，指定基于哪种算法进行重要性评估。默认为 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 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.

  备注

  此参数可用于指定在 study 用于多目标优化时绘制哪个目标。例如，要仅获取第一个目标的超参数重要性，请为目标参数使用 target=lambda t: t.values[0]。

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

返回:

一个 matplotlib.axes.Axes 对象。

返回类型:

Axes

备注

作为实验性功能在 v2.2.0 中添加。接口可能会在更新版本中未经事先通知而更改。请参阅 https://github.com/optuna/optuna/releases/tag/v2.2.0。

以下代码片段展示了如何绘制超参数重要性。

/Users/cw/baidu/code/fin_tool/github/optuna/docs/visualization_matplotlib_examples/optuna.visualization.matplotlib.param_importances.py:26: ExperimentalWarning:

plot_param_importances is experimental (supported from v2.2.0). The interface can change in the future.


<Axes: title={'left': 'Hyperparameter Importances'}, xlabel='Hyperparameter Importance', ylabel='Hyperparameter'>

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)