optuna.importance.PedAnovaImportanceEvaluator

class optuna.importance.PedAnovaImportanceEvaluator(*, baseline_quantile=0.1, evaluate_on_local=True)[source]

PED-ANOVA importance evaluator.

Implements the PED-ANOVA hyperparameter importance evaluation algorithm.

PED-ANOVA fits Parzen estimators of COMPLETE trials better than a user-specified baseline. Users can specify the baseline by a quantile. The importance can be interpreted as how important each hyperparameter is to get the performance better than baseline.

For further information about PED-ANOVA algorithm, please refer to the following paper:

Note

The performance of PED-ANOVA depends on how many trials to consider above baseline. To stabilize the analysis, it is preferable to include at least 5 trials above baseline.

Note

Please refer to the original work.

Parameters:

  • baseline_quantile (float) – Compute the importance of achieving top-baseline_quantile quantile objective value. For example, baseline_quantile=0.1 means that the importances give the information of which parameters were important to achieve the top-10% performance during optimization.

  • evaluate_on_local (bool) – Whether we measure the importance in the local or global space. If True, the importances imply how importance each parameter is during optimization. Meanwhile, evaluate_on_local=False gives the importances in the specified search_space. evaluate_on_local=True is especially useful when users modify search space during optimization.

Example

An example of using PED-ANOVA is as follows:

import optuna
from optuna.importance import PedAnovaImportanceEvaluator


def objective(trial):
    x1 = trial.suggest_float("x1", -10, 10)
    x2 = trial.suggest_float("x2", -10, 10)
    return x1 + x2 / 1000


study = optuna.create_study()
study.optimize(objective, n_trials=100)
evaluator = PedAnovaImportanceEvaluator()
importance = optuna.importance.get_param_importances(study, evaluator=evaluator)

Note

Added in v3.6.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.6.0.

Methods

evaluate(study[, params, target])

Evaluate parameter importances based on completed trials in the given study.
evaluate(study, params=None, *, target=None)[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 (Study) – An optimized study.

  • params (list[str] | None) – A list of names of parameters to assess. If None, all parameters that are present in all of the completed trials are assessed.

  • target (Callable[[FrozenTrial], float] | None) –

    A function to specify the value to evaluate importances. If it is None and study is being used for single-objective optimization, the objective values are used. Can also be used for other trial attributes, such as the duration, like target=lambda t: t.duration.total_seconds().

    Note

    Specify this argument if study is being used for multi-objective optimization. For example, to get the hyperparameter importance of the first objective, use target=lambda t: t.values[0] for the target parameter.

Returns:

A dict where the keys are parameter names and the values are assessed importances.

Return type:

dict[str, float]