syne_tune.optimizer.schedulers.searchers.bayesopt.datatypes.tuning_job_state module

class syne_tune.optimizer.schedulers.searchers.bayesopt.datatypes.tuning_job_state.TuningJobState(hp_ranges, config_for_trial, trials_evaluations, failed_trials=None, pending_evaluations=None)

Bases: object

Collects all data determining the state of a tuning experiment. Trials are indexed by trial_id. The configurations associated with trials are listed in config_for_trial. trials_evaluations contains observations, failed_trials lists trials for which evaluations have failed, pending_evaluations lists trials for which observations are pending.

trials_evaluations may store values for different metrics in each record, and each such value may be a dict (see:class:TrialEvaluations). For example, for multi-fidelity schedulers, trials_evaluations[i].metrics[k][str(r)] is the value for metric k and trial trials_evaluations[i].trial_id observed at resource level r.

static empty_state(hp_ranges)

Return type:

TuningJobState

metrics_for_trial(trial_id, config=None)

Helper for inserting new entry into trials_evaluations. If trial_id is already contained there, the corresponding eval.metrics is returned. Otherwise, a new entry new_eval is appended to trials_evaluations and its new_eval.metrics is returned (empty dict). In the latter case, config needs to be passed, because it may not yet feature in config_for_trial.

Return type:

Union[float, Dict[str, float]]

num_observed_cases(metric_name='target', resource=None)

Counts the number of observations for metric metric_name.

Parameters:

• metric_name (str) – Defaults to INTERNAL_METRIC_NAME

• resource (Optional[int]) – In the multi-fidelity case, we only count observations at this resource level

Return type:

int

Returns:

Number of observations

observed_data_for_metric(metric_name='target', resource_attr_name=None)

Extracts datapoints from trials_evaluations for particular metric metric_name, in the form of a list of configs and a list of metric values. If metric_name is a dict-valued metric, the dict keys must be resource values, and the returned configs are extended. Here, the name of the resource attribute can be passed in resource_attr_name (if not given, it can be obtained from hp_ranges if this is extended).

Note: Implements the default behaviour, namely to return extended configs for dict-valued metrics, which also require hp_ranges to be extended. This is not correct for some specific multi-fidelity surrogate models, which should access the data directly.

Parameters:

• metric_name (str)

• resource_attr_name (Optional[str])

Return type:

(List[Dict[str, Union[int, float, str]]], List[float])

Returns:

configs, metric_values

is_pending(trial_id, resource=None)

Return type:

bool

is_labeled(trial_id, metric_name='target', resource=None)

Checks whether trial_id has observed data under metric_name. If resource is given, the observation must be at that resource level.

Return type:

bool

append_pending(trial_id, config=None, resource=None)

Appends new pending evaluation. If the trial has not been registered here, config must be given. Otherwise, it is ignored.

remove_pending(trial_id, resource=None)

Return type:

bool

pending_configurations(resource_attr_name=None)

Returns list of configurations corresponding to pending evaluations. If the latter have resource values, the configs are extended.

Return type:

List[Dict[str, Union[int, float, str]]]

all_configurations(filter_observed_data=None)

Returns list of configurations for all trials represented here, whether observed, pending, or failed. If filter_observed_data is given, the configurations for observed trials are filtered with this predicate.

Parameters:

filter_observed_data (Optional[Callable[[Dict[str, Union[int, float, str]]], bool]]) – See above, optional

Return type:

List[Dict[str, Union[int, float, str]]]

Returns:

List of all configurations