Source code for optuna.integration.pytorch_ignite

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 < examples/>`__ 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 <>`_ 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._trainer.state.epoch) if self._trial.should_prune(): message = "Trial was pruned at {} epoch.".format(self._trainer.state.epoch) raise optuna.TrialPruned(message)