syne_tune.optimizer.schedulers.searchers.searcher module

class syne_tune.optimizer.schedulers.searchers.searcher.BaseSearcher(config_space, points_to_evaluate=None, random_seed=None)

Bases: object

Base class for searchers that sample hyperparameter configurations from the given configuration space.

Parameters:

• config_space (Dict[str, Any]) – The configuration space to sample from.

• points_to_evaluate (Optional[List[Dict[str, Any]]]) – A list of configurations to evaluate initially (in the given order).

• random_seed (Optional[int]) – Seed used to initialize the random number generators.

suggest(**kwargs)

Suggest a new configuration.

Note: Query _next_points_to_evaluate() for initial configs to return first.

Parameters:

kwargs – Extra information may be passed from scheduler to searcher

Return type:

Optional[Dict[str, Any]]

Returns:

New configuration. The searcher may return None if a new configuration cannot be suggested. In this case, the tuning will stop. This happens if searchers never suggest the same config more than once, and all configs in the (finite) search space are exhausted.

on_trial_result(trial_id, config, metrics)

Inform searcher about result

The scheduler passes every result. If update == True, the searcher should update its surrogate model (if any), otherwise result is an intermediate result not modelled.

The default implementation calls _update() if update == True. It can be overwritten by searchers which also react to intermediate results.

Parameters:

• trial_id (int) – See on_trial_result()

• config (Dict[str, Any]) – See on_trial_result()

• metrics (List[float]) – See on_trial_result()

on_trial_error(trial_id)

Called by scheduler if an evaluation job for a trial failed.

The searcher should react appropriately (e.g., remove pending evaluations for this trial, not suggest the configuration again).

Parameters:

trial_id (int) – ID of trial whose evaluated failed

on_trial_complete(trial_id, config, metrics)

Inform searcher about result

The scheduler passes every result. If update == True, the searcher should update its surrogate model (if any), otherwise result is an intermediate result not modelled.

The default implementation calls _update() if update == True. It can be overwritten by searchers which also react to intermediate results.

Parameters:

• trial_id (int) – See on_trial_result()

• config (Dict[str, Any]) – See on_trial_result()

• metrics (List[float]) – See on_trial_result()