optuna.importance
The importance
module provides functionality for evaluating hyperparameter importances based on completed trials in a given study. The utility function get_param_importances()
takes a Study
and optional evaluator as two of its inputs. The evaluator must derive from BaseImportanceEvaluator
, and is initialized as a FanovaImportanceEvaluator
by default when not passed in. Users implementing custom evaluators should refer to either FanovaImportanceEvaluator
, MeanDecreaseImpurityImportanceEvaluator
, or PedAnovaImportanceEvaluator
as a guide, paying close attention to the format of the return value from the Evaluator’s evaluate
function.
Note
FanovaImportanceEvaluator
takes over 1 minute when given a study that contains 1000+ trials.
We published optuna-fast-fanova library,
that is a Cython accelerated fANOVA implementation. By using it, you can get hyperparameter
importances within a few seconds.
If n_trials
is more than 10000, the Cython implementation takes more than a minute, so you can use PedAnovaImportanceEvaluator
instead, enabling the evaluation to finish in a second.
Evaluate parameter importances based on completed trials in the given study. |
|
fANOVA importance evaluator. |
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Mean Decrease Impurity (MDI) parameter importance evaluator. |
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PED-ANOVA importance evaluator. |