syne_tune.optimizer.schedulers.searchers.conformal.conformal_quantile_regression_searcher module

class syne_tune.optimizer.schedulers.searchers.conformal.conformal_quantile_regression_searcher.ConformalQuantileRegression(config_space, random_seed=None, points_to_evaluate=None, num_init_random_draws=5, update_frequency=1, max_fit_samples=None, surrogate_cls=<class 'syne_tune.optimizer.schedulers.searchers.conformal.surrogate.quantile_regression_surrogate.QuantileRegressionSurrogateModel'>, **surrogate_kwargs)

Bases: SingleObjectiveBaseSearcher

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.

should_update()

Return type:

bool

num_results()

Return type:

int

make_input_target()

fit_model()

on_trial_complete(trial_id, config, metric, resource_level=None)

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()

• metric (float) – See on_trial_result()

sample_random()

Return type:

Dict

configs_to_df(configs)

Return type:

DataFrame