def get_best_trials(self, num_trials: int = 1) -> List[trial_module.Trial]:
"""Returns the trials with the best objective values found so far.
Arguments:
num_trials: positive int, number of trials to return.
Returns:
List of KerasTuner Trials.
"""
if len(self.objective) > 1:
raise ValueError(
"Getting the best trials for multi-objective optimization "
"is not supported. "
)
maximizing = (
utils.format_goal(self.objective[0].direction) == "MAXIMIZE")
# List all trials associated with the same study
trial_list = self.service.list_trials()
optimizer_trials = [t for t in trial_list if t["state"] == "COMPLETED"]
if not optimizer_trials:
return []
sorted_trials = sorted(
optimizer_trials,
key=lambda t: t["finalMeasurement"]["metrics"][0]["value"],
reverse=maximizing,
)
best_optimizer_trials = sorted_trials[:num_trials]
best_trials = []
# Convert Optimizer trials to KerasTuner Trial instance
for optimizer_trial in best_optimizer_trials:
final_measurement = optimizer_trial["finalMeasurement"]
kerastuner_trial = trial_module.Trial(
hyperparameters=utils.convert_optimizer_trial_to_hps(
self.hyperparameters.copy(), optimizer_trial
),
trial_id=utils.get_trial_id(optimizer_trial),
status=trial_module.TrialStatus.COMPLETED,
)
# If trial had ended before having intermediate metric reporting,
# set epoch = 1.
kerastuner_trial.best_step = final_measurement.get("stepCount", 1)
kerastuner_trial.score = final_measurement["metrics"][0]["value"]
best_trials.append(kerastuner_trial)
return best_trials
def create_trial(self, tuner_id: Text) -> trial_module.Trial:
"""Create a new `Trial` to be run by the `Tuner`.
Args:
tuner_id: An ID that identifies the `Tuner` requesting a `Trial`.
`Tuners` that should run the same trial (for instance, when
running a multi-worker model) should have the same ID. If
multiple suggestTrialsRequests have the same tuner_id, the
service will return the identical suggested trial if the trial
is PENDING, and provide a new trial if the last suggested trial
was completed.
Returns:
A `Trial` object containing a set of hyperparameter values to run
in a `Tuner`.
Raises:
SuggestionInactiveError: Indicates that a suggestion was requested
from an inactive study.
"""
# List all trials from the same study and see if any
# trial.status=STOPPED or if number of trials >= max_limit.
trial_list = self.service.list_trials()
# Note that KerasTunerTrialStatus - 'STOPPED' is equivalent to
# OptimizerTrialState - 'STOPPING'.
stopping_trials = [t for t in trial_list if t["state"] == "STOPPING"]
if (self.max_trials and
len(trial_list) >= self.max_trials) or stopping_trials:
trial_id = "n"
hyperparameters = self.hyperparameters.copy()
hyperparameters.values = {}
# This will break the search loop later.
return trial_module.Trial(
hyperparameters=hyperparameters,
trial_id=trial_id,
status=trial_module.TrialStatus.STOPPED,
)
# Get suggestions
suggestions = self.service.get_suggestions(tuner_id)
if "trials" not in suggestions:
return trial_module.Trial(
hyperparameters={}, status=trial_module.TrialStatus.STOPPED
)
# Fetches the suggested trial.
# Optimizer Trial instance
optimizer_trial = suggestions["trials"][0]
trial_id = utils.get_trial_id(optimizer_trial)
# KerasTuner Trial instance
kerastuner_trial = trial_module.Trial(
hyperparameters=utils.convert_optimizer_trial_to_hps(
self.hyperparameters.copy(), optimizer_trial
),
trial_id=trial_id,
status=trial_module.TrialStatus.RUNNING,
)
tf.get_logger().info(
"Hyperparameters requested by tuner ({}): {} ".format(
tuner_id, kerastuner_trial.hyperparameters.values
)
)
self._start_time = time.time()
self.trials[trial_id] = kerastuner_trial
self.ongoing_trials[tuner_id] = kerastuner_trial
self._save_trial(kerastuner_trial)
self.save()
return kerastuner_trial