syne_tune.optimizer.schedulers.transfer_learning.zero_shot module

class syne_tune.optimizer.schedulers.transfer_learning.zero_shot.ZeroShotTransfer(config_space, metric, transfer_learning_evaluations, do_minimize=True, sort_transfer_learning_evaluations=True, use_surrogates=False, random_seed=None)

Bases: TransferLearningMixin, SingleObjectiveScheduler

A zero-shot transfer hyperparameter optimization method which jointly selects configurations that minimize the average rank obtained on historic metadata (transfer_learning_evaluations). This is a searcher which can be used with FIFOScheduler. Reference:

Sequential Model-Free Hyperparameter Tuning.

Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme.

IEEE International Conference on Data Mining (ICDM) 2015.

Additional arguments on top of parent class StochasticSearcher:

Parameters:

• transfer_learning_evaluations (Dict[str, TransferLearningTaskEvaluations]) – Dictionary from task name to offline evaluations.

• mode – Whether to minimize (“min”, default) or maximize (“max”)

• sort_transfer_learning_evaluations (bool) – Use False if the hyperparameters for each task in transfer_learning_evaluations are already in the same order. If set to True, hyperparameters are sorted. Defaults to True

• use_surrogates (bool) – If the same configuration is not evaluated on all tasks, set this to True. This will generate a set of configurations and will impute their performance using surrogate models. Defaults to False

get_config(**kwargs)

Return type:

Optional[dict]