syne_tune.optimizer.schedulers.searchers.random_grid_searcher module

class syne_tune.optimizer.schedulers.searchers.random_grid_searcher.RandomSearcher(config_space, metric, points_to_evaluate=None, debug_log=False, resource_attr=None, allow_duplicates=None, restrict_configurations=None, **kwargs)

Bases: StochasticAndFilterDuplicatesSearcher

Searcher which randomly samples configurations to try next.

Additional arguments on top of parent class StochasticAndFilterDuplicatesSearcher:

Parameters:

• debug_log (Union[bool, DebugLogPrinter]) – If True, debug log printing is activated. Logs which configs are chosen when, and which metric values are obtained. Defaults to False

• resource_attr (Optional[str]) – Optional. Key in result passed to _update() for resource value (for multi-fidelity schedulers)

configure_scheduler(scheduler)

Some searchers need to obtain information from the scheduler they are used with, in order to configure themselves. This method has to be called before the searcher can be used.

Parameters:

scheduler (TrialScheduler) – Scheduler the searcher is used with.

clone_from_state(state)

Together with get_state(), this is needed in order to store and re-create the mutable state of the searcher.

Given state as returned by get_state(), this method combines the non-pickle-able part of the immutable state from self with state and returns the corresponding searcher clone. Afterwards, self is not used anymore.

Parameters:

state (Dict[str, Any]) – See above

Returns:

New searcher object

property debug_log

Some subclasses support writing a debug log, using DebugLogPrinter. See RandomSearcher for an example.

Returns:

debug_log object`` or None (not supported)

class syne_tune.optimizer.schedulers.searchers.random_grid_searcher.GridSearcher(config_space, metric, points_to_evaluate=None, num_samples=None, shuffle_config=True, allow_duplicates=False, **kwargs)

Bases: StochasticSearcher

Searcher that samples configurations from an equally spaced grid over config_space.

It first evaluates configurations defined in points_to_evaluate and then continues with the remaining points from the grid.

Additional arguments on top of parent class StochasticSearcher.

Parameters:

• num_samples (Optional[Dict[str, int]]) – Dictionary, optional. Number of samples per hyperparameter. This is required for hyperparameters of type float, optional for integer hyperparameters, and will be ignored for other types (categorical, scalar). If left unspecified, a default value of DEFAULT_NSAMPLE will be used for float parameters, and the smallest of DEFAULT_NSAMPLE and integer range will be used for integer parameters.

• shuffle_config (bool) – If True (default), the order of configurations suggested after those specified in points_to_evaluate is shuffled. Otherwise, the order will follow the Cartesian product of the configurations.

• allow_duplicates (bool) – If True, get_config() may return the same configuration more than once. Defaults to False

get_config(**kwargs)

Select the next configuration from the grid.

This is done without replacement, so previously returned configs are not suggested again.

Return type:

Optional[dict]

Returns:

A new configuration that is valid, or None if no new config can be suggested. The returned configuration is a dictionary that maps hyperparameters to its values.

get_state()

Together with clone_from_state(), this is needed in order to store and re-create the mutable state of the searcher. The state returned here must be pickle-able.

Return type:

Dict[str, Any]

Returns:

Pickle-able mutable state of searcher

clone_from_state(state)

Together with get_state(), this is needed in order to store and re-create the mutable state of the searcher.

Given state as returned by get_state(), this method combines the non-pickle-able part of the immutable state from self with state and returns the corresponding searcher clone. Afterwards, self is not used anymore.

Parameters:

state (Dict[str, Any]) – See above

Returns:

New searcher object