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 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.
"""
objective = self._get_objective()
if len(objective) > 1:
raise ValueError(
"Getting the best trials for multi-objective optimization "
"is not supported."
)
maximizing = (utils.format_goal(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].get("value"),
reverse=maximizing,
)
best_optimizer_trials = sorted_trials[:num_trials]
best_trials = []
# Convert completed Optimizer trials to KerasTuner Trial instances.
for optimizer_trial in best_optimizer_trials:
kerastuner_trial = (
utils.convert_completed_optimizer_trial_to_keras_trial(
optimizer_trial,
self.hyperparameters.copy()))
best_trials.append(kerastuner_trial)
return best_trials