smac.runhistory.encoder¶

Interfaces¶

class smac.runhistory.encoder.AbstractRunHistoryEncoder(scenario, considered_states=None, lower_budget_states=None, scale_percentage=5, seed=None)[source]¶

Bases: object

Abstract class for preparing data in order to train a surrogate model.

Parameters:

scenario (Scenario object.)
considered_states (list[StatusType], defaults to [StatusType.SUCCESS, StatusType.CRASHED, StatusType.MEMORYOUT] # noqa: E501) – Trials with the passed states are considered.
lower_budget_states (list[StatusType], defaults to []) – Additionally consider all trials with these states for budget < current budget.
scale_percentage (int, defaults to 5) – Scaled y-transformation use a percentile to estimate distance to optimum. Only used in some sub-classes.
seed (int | None, defaults to none)

Raises:

TypeError – If no success states are given.

get_configurations(budget_subset=None)[source]¶

Returns vector representation of the configurations.

Warning

Instance features are not appended and cost values are not taken into account.

Parameters:: budget_subset (list[int|float] | None, defaults to none) – List of budgets to consider.
Returns:: configs_array
Return type:: np.ndarray

property meta: dict[str, Any]¶

Returns the meta-data of the created object.

Returns:

dict[str, Any] (meta-data of the created object: name, considered states, lower budget)
states, scale_percentage, seed.

property multi_objective_algorithm: AbstractMultiObjectiveAlgorithm | None¶: The multi objective algorithm used to transform the data.

property runhistory: RunHistory¶: The RunHistory used to transform the data.

transform(budget_subset=None)[source]¶

Returns a vector representation of the RunHistory.

Parameters:

budget_subset (list | None, defaults to none) – List of budgets to consider.

Return type:

tuple[ndarray, ndarray]

Returns:

X (np.ndarray) – Configuration vector and instance features.
Y (np.ndarray) – Cost values.

abstract transform_response_values(values)[source]¶

Transform function response values.

Parameters:: values (np.ndarray) – Response values to be transformed.
Returns:: transformed_values
Return type:: np.ndarray

class smac.runhistory.encoder.RunHistoryEIPSEncoder(scenario, considered_states=None, lower_budget_states=None, scale_percentage=5, seed=None)[source]¶

Bases: AbstractRunHistoryEncoder

Encoder specifically for the EIPS (expected improvement per second) acquisition function.