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
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.