- optuna.importance.get_param_importances(study, *, evaluator=None, params=None, target=None, normalize=True)
Evaluate parameter importances based on completed trials in the given study.
The parameter importances are returned as a dictionary where the keys consist of parameter names and their values importances. The importances are represented by non-negative floating point numbers, where higher values mean that the parameters are more important. The returned dictionary is ordered by its values in a descending order. By default, the sum of the importance values are normalized to 1.0.
None, all parameter that are present in all of the completed trials are assessed. This implies that conditional parameters will be excluded from the evaluation. To assess the importances of conditional parameters, a
listof parameter names can be specified via
params. If specified, only completed trials that contain all of the parameters will be considered. If no such trials are found, an error will be raised.
If the given study does not contain completed trials, an error will be raised.
paramsis specified as an empty list, an empty dictionary is returned.
plot_param_importances()to plot importances.
study (Study) – An optimized study.
evaluator (BaseImportanceEvaluator | None) – An importance evaluator object that specifies which algorithm to base the importance assessment on. Defaults to
A function to specify the value to evaluate importances. If it is
studyis being used for single-objective optimization, the objective values are used.
targetmust be specified if
studyis being used for multi-objective optimization.
Specify this argument if
studyis being used for multi-objective optimization. For example, to get the hyperparameter importance of the first objective, use
target=lambda t: t.valuesfor the target parameter.
normalize (bool) –
A boolean option to specify whether the sum of the importance values should be normalized to 1.0. Defaults to
Added in v3.0.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.0.0.
dictwhere the keys are parameter names and the values are assessed importances.
- Return type: