import optuna
from optuna import type_checking
if type_checking.TYPE_CHECKING:
from optuna.trial import Trial # NOQA
with optuna._imports.try_import() as _imports:
from ignite.engine import Engine # NOQA
[docs]class PyTorchIgnitePruningHandler(object):
"""PyTorch Ignite handler to prune unpromising trials.
See `the example <https://github.com/optuna/optuna/blob/master/
examples/pytorch_ignite_simple.py>`__
if you want to add a pruning handler which observes validation accuracy.
Args:
trial:
A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the
objective function.
metric:
A name of metric for pruning, e.g., ``accuracy`` and ``loss``.
trainer:
A trainer engine of PyTorch Ignite. Please refer to `ignite.engine.Engine reference
<https://pytorch.org/ignite/engine.html#ignite.engine.Engine>`_ for further details.
"""
[docs] def __init__(self, trial, metric, trainer):
# type: (Trial, str, Engine) -> None
_imports.check()
self._trial = trial
self._metric = metric
self._trainer = trainer
def __call__(self, engine):
# type: (Engine) -> None
score = engine.state.metrics[self._metric]
self._trial.report(score, self._trainer.state.epoch)
if self._trial.should_prune():
message = "Trial was pruned at {} epoch.".format(self._trainer.state.epoch)
raise optuna.TrialPruned(message)