optuna.importance.MeanDecreaseImpurityImportanceEvaluator
- class optuna.importance.MeanDecreaseImpurityImportanceEvaluator(*, n_trees=64, max_depth=64, seed=None)[源代码]
Mean Decrease Impurity (MDI) parameter importance evaluator.
This evaluator fits a random forest that predicts objective values given hyperparameter configurations. Feature importances are then computed using MDI.
备注
This evaluator requires the sklean Python package and is based on sklearn.ensemble.RandomForestClassifier.feature_importances_.
- 参数
- 返回类型
None
Methods
evaluate
(study[, params, target])Evaluate parameter importances based on completed trials in the given study.
- evaluate(study, params=None, *, target=None)[源代码]
Evaluate parameter importances based on completed trials in the given study.
备注
This method is not meant to be called by library users.
参见
Please refer to
get_param_importances()
for how a concrete evaluator should implement this method.- 参数
study (optuna.study.Study) – An optimized study.
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 evaluate importances. If it is
None
andstudy
is being used for single-objective optimization, the objective values are used.备注
Specify this argument if
study
is being used for multi-objective optimization.
- 返回
An
collections.OrderedDict
where the keys are parameter names and the values are assessed importances.- 引发
ValueError – If
target
isNone
andstudy
is being used for multi-objective optimization.- 返回类型