syne_tune.optimizer.schedulers.searchers.utils.warmstarting module

syne_tune.optimizer.schedulers.searchers.utils.warmstarting.create_hp_ranges_for_warmstarting(**kwargs)

See GPFIFOSearcher for details on “transfer_learning_task_attr”, “transfer_learning_active_task”, “transfer_learning_active_config_space” as optional fields in kwargs. If given, they determine active_config_space and prefix_keys of hp_ranges created here, and they also place constraints on config_space.

This function is not only called in gp_searcher_factory to create hp_ranges for a new GPFIFOSearcher object. It is also needed to create the TuningJobState object containing the data to be used in warmstarting.

Return type:

HyperparameterRanges

syne_tune.optimizer.schedulers.searchers.utils.warmstarting.create_filter_observed_data_for_warmstarting(**kwargs)

See GPFIFOSearcher for details on transfer_learning_task_attr’, ‘transfer_learning_active_task’ as optional fields in kwargs.

Return type:

Optional[Callable[[Dict[str, Union[str, float, int]]], bool]]

syne_tune.optimizer.schedulers.searchers.utils.warmstarting.create_base_gp_kernel_for_warmstarting(hp_ranges, **kwargs)

In the transfer learning case, the base kernel is a product of two Matern52 kernels, the first non-ARD over the categorical parameter determining the task, the second ARD over the remaining parameters.

Return type:

KernelFunction