optuna.importance.MeanDecreaseImpurityImportanceEvaluator¶
- class optuna.importance.MeanDecreaseImpurityImportanceEvaluator(*, n_trees: int = 64, max_depth: int = 64, seed: Optional[int] = None)[source]¶
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.
Note
This evaluator requires the sklean Python package and is based on sklearn.ensemble.RandomForestClassifier.feature_importances_.
- Parameters
n_trees – Number of trees in the random forest.
max_depth – The maximum depth of each tree in the random forest.
seed – Seed for the random forest.
Methods
__init__
(*[, n_trees, max_depth, seed])evaluate
(study[, params])Evaluate parameter importances based on completed trials in the given study.
- evaluate(study: optuna.study.Study, params: Optional[List[str]] = None) Dict[str, float] [source]¶
Evaluate parameter importances based on completed trials in the given study.
Note
This method is not meant to be called by library users.
See also
Please refer to
get_param_importances()
for how a concrete evaluator should implement this method.- Parameters
study – An optimized study.
params – A list of names of parameters to assess. If
None
, all parameters that are present in all of the completed trials are assessed.
- Returns
An
collections.OrderedDict
where the keys are parameter names and the values are assessed importances.