syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_skipopt module

class syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_skipopt.SkipOptimizationPredicate

Bases: object

Interface for skip_optimization predicate in ModelStateTransformer.

reset()

If there is an internal state, reset it to its initial value

class syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_skipopt.NeverSkipPredicate

Bases: SkipOptimizationPredicate

Hyperparameter optimization is never skipped.

class syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_skipopt.AlwaysSkipPredicate

Bases: SkipOptimizationPredicate

Hyperparameter optimization is always skipped.

class syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_skipopt.SkipPeriodicallyPredicate(init_length, period, metric_name='target')

Bases: SkipOptimizationPredicate

Let N be the number of labeled points for metric metric_name. Optimizations are not skipped if N < init_length. Afterwards, we increase a counter whenever N is larger than in the previous call. With respect to this counter, optimizations are done every period times, in between they are skipped.

Parameters:

• init_length (int) – See above

• period (int) – See above

• metric_name (str) – Name of internal metric. Defaults to INTERNAL_METRIC_NAME.

reset()

If there is an internal state, reset it to its initial value

class syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_skipopt.SkipNoMaxResourcePredicate(init_length, max_resource, metric_name='target')

Bases: SkipOptimizationPredicate

This predicate works for multi-fidelity HPO, see for example GPMultiFidelitySearcher.

We track the number of labeled datapoints at resource level max_resource. HP optimization is skipped if the total number N of labeled cases is N >= init_length, and if the number of max_resource cases has not increased since the last recent optimization.

This means that as long as the dataset only grows w.r.t. cases at lower resources than max_resource, this does not trigger HP optimization.

Parameters:

• init_length (int) – See above

• max_resource (int) – See above

• metric_name (str) – Name of internal metric. Defaults to INTERNAL_METRIC_NAME.

reset()

If there is an internal state, reset it to its initial value