syne_tune.optimizer.schedulers.transfer_learning.transfer_learning_task_evaluation module

class syne_tune.optimizer.schedulers.transfer_learning.transfer_learning_task_evaluation.TransferLearningTaskEvaluations(configuration_space, hyperparameters, objectives_names, objectives_evaluations)

Bases: object

Class that contains offline evaluations for a task that can be used for transfer learning. Args:

configuration_space: Dict the configuration space that was used when sampling evaluations. hyperparameters: pd.DataFrame the hyperparameters values that were acquired, all keys of configuration-space

should appear as columns.

objectives_names: List[str] the name of the objectives that were acquired objectives_evaluations: np.array values of recorded objectives, must have shape

(num_evals, num_seeds, num_fidelities, num_objectives)

configuration_space: Dict

hyperparameters: DataFrame

objectives_names: List[str]

objectives_evaluations: array

objective_values(objective_name)

Return type:

array

objective_index(objective_name)

Return type:

int

top_k_hyperparameter_configurations(k, objective, do_minimize=False)

Returns the best k hyperparameter configurations. :type k: int :param k: The number of top hyperparameters to return. :type do_minimize: bool :param do_minimize: indicating if we minimize or maximize the optimization problem. :type objective: str :param objective: The objective to consider for ranking hyperparameters. :rtype: List[Dict[str, Any]] :returns: List of hyperparameters in order.