optuna.importance.FanovaImportanceEvaluator¶
- class optuna.importance.FanovaImportanceEvaluator(*, n_trees: int = 64, max_depth: int = 64, seed: Optional[int] = None)[source]¶
fANOVA importance evaluator.
Implements the fANOVA hyperparameter importance evaluation algorithm in An Efficient Approach for Assessing Hyperparameter Importance.
Given a study, fANOVA fits a random forest regression model that predicts the objective value given a parameter configuration. The more accurate this model is, the more reliable the importances assessed by this class are.
Note
Requires the sklearn Python package.
Note
Pairwise and higher order importances are not supported through this class. They can be computed using
_Fanova
directly but is not recommended as interfaces may change without prior notice.Note
The performance of fANOVA depends on the prediction performance of the underlying random forest model. In order to obtain high prediction performance, it is necessary to cover a wide range of the hyperparameter search space. It is recommended to use an exploration-oriented sampler such as
RandomSampler
.Note
For how to cite the original work, please refer to https://automl.github.io/fanova/cite.html.
- Parameters
n_trees – The number of trees in the forest.
max_depth – The maximum depth of the trees in the forest.
seed – Controls the randomness of the forest. For deterministic behavior, specify a value other than
None
.
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.