def _trial_to_result(self, trial: Trial) -> Result:
checkpoint = trial.checkpoint.to_air_checkpoint()
best_checkpoints = [(checkpoint.to_air_checkpoint(),
checkpoint.metrics)
for checkpoint in trial.get_trial_checkpoints()]
result = Result(
checkpoint=checkpoint,
metrics=trial.last_result.copy(),
error=self._populate_exception(trial),
log_dir=Path(trial.logdir) if trial.logdir else None,
metrics_dataframe=self._experiment_analysis.trial_dataframes.get(
trial.logdir) if self._experiment_analysis else None,
best_checkpoints=best_checkpoints,
)
return result
def get_trial_checkpoints_paths(
self,
trial: Trial,
metric: Optional[str] = None) -> List[Tuple[str, Number]]:
"""Gets paths and metrics of all persistent checkpoints of a trial.
Args:
trial: The log directory of a trial, or a trial instance.
metric: key for trial info to return, e.g. "mean_accuracy".
"training_iteration" is used by default if no value was
passed to ``self.default_metric``.
Returns:
List of [path, metric] for all persistent checkpoints of the trial.
"""
metric = metric or self.default_metric or TRAINING_ITERATION
if isinstance(trial, str):
trial_dir = os.path.expanduser(trial)
# Get checkpoints from logdir.
chkpt_df = TrainableUtil.get_checkpoints_paths(trial_dir)
# Join with trial dataframe to get metrics.
trial_df = self.trial_dataframes[trial_dir]
path_metric_df = chkpt_df.merge(trial_df,
on="training_iteration",
how="inner")
return path_metric_df[["chkpt_path", metric]].values.tolist()
elif isinstance(trial, Trial):
checkpoints = trial.get_trial_checkpoints()
# Support metrics given as paths, e.g.
# "info/learner/default_policy/policy_loss".
return [(c.dir_or_data, unflattened_lookup(metric, c.metrics))
for c in checkpoints]
else:
raise ValueError("trial should be a string or a Trial instance.")
