smac.runhistory.encoder.abstract_encoder¶
Classes¶
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Abstract class for preparing data in order to train a surrogate model. |
Interfaces¶
- class smac.runhistory.encoder.abstract_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.