syne_tune.optimizer.schedulers.transfer_learning package

class syne_tune.optimizer.schedulers.transfer_learning.LegacyTransferLearningTaskEvaluations(configuration_space, hyperparameters, objectives_names, objectives_evaluations)[source]

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)[source]
Return type:

array

objective_index(objective_name)[source]
Return type:

int

top_k_hyperparameter_configurations(k, mode, objective)[source]

Returns the best k hyperparameter configurations. :type k: int :param k: The number of top hyperparameters to return. :type mode: str :param mode: ‘min’ or ‘max’, indicating the type of 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.

class syne_tune.optimizer.schedulers.transfer_learning.LegacyTransferLearningMixin(config_space, transfer_learning_evaluations, metric_names, **kwargs)[source]

Bases: object

metric_names()[source]
Return type:

List[str]

top_k_hyperparameter_configurations_per_task(transfer_learning_evaluations, num_hyperparameters_per_task, mode, metric)[source]

Returns the best hyperparameter configurations for each task. :type transfer_learning_evaluations: Dict[str, LegacyTransferLearningTaskEvaluations] :param transfer_learning_evaluations: Set of candidates to choose from. :type num_hyperparameters_per_task: int :param num_hyperparameters_per_task: The number of top hyperparameters per task to return. :type mode: str :param mode: ‘min’ or ‘max’, indicating the type of optimization problem. :type metric: str :param metric: The metric to consider for ranking hyperparameters. :rtype: Dict[str, List[Dict[str, Any]]] :returns: Dict which maps from task name to list of hyperparameters in order.

Subpackages

Submodules