deepcave.evaluators.mo_fanova

# fANOVA

This module provides a tool for assessing the importance of an algorithms Hyperparameters.

Utilities provide calculation of the data wrt the budget and train the forest on the encoded data.

## Classes

  • fANOVA: Calculate and provide midpoints and sizes.

Classes

MOfANOVA(run)

Multi-Objective fANOVA.
class deepcave.evaluators.mo_fanova.MOfANOVA(run)[source]

Bases: fANOVA

Multi-Objective fANOVA.

Calculate and provide midpoints and sizes from the forest’s split values in order to get the marginals. Override: to train the random forest with an arbitrary weighting of the objectives (multi-objective case).

calculate(objectives=None, budget=None, n_trees=100, seed=0)[source]

Get the data with respect to budget and train the forest on the encoded data.

Calculates weighted fanova for multiple objectives.

Note

Right now, only n_trees is used. It can be further specified if needed.

Parameters:

  • objectives (Optional[Union[Objective, List[Objective]]], optional) – Considered objectives. By default None. If None, all objectives are considered.

  • budget (Optional[Union[int, float]], optional) – Considered budget. By default None. If None, the highest budget is chosen.

  • n_trees (int, optional) – How many trees should be used. By default 100.

  • seed (int) – Random seed. By default 0.

Return type:

None

get_importances_(hp_names=None)[source]

Return the importance scores from the passed Hyperparameter names.

Parameters:

  • hp_names (Optional[List[str]]) – Selected Hyperparameter names to get the importance scores from. If None, all Hyperparameters of the configuration space are used.

  • sort (bool, optional) – Whether the Hyperparameters should be sorted by importance. By default True.

Returns:

Dictionary with Hyperparameter names and the corresponding importance scores and variances.

Return type:

Dict

Raises:

RuntimeError – If the important scores are not calculated.

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