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

    from ignite.engine import Engine  # NOQA
    _available = True
except ImportError as e:
    _import_error = e
    # PyTorchIgnitePruningHandler is disabled because pytorch-ignite is not available.
    _available = False

[docs]class PyTorchIgnitePruningHandler(object): """PyTorch Ignite handler to prune unpromising trials. Example: Add a pruning handler which observes validation accuracy. .. code:: evaluator = create_supervised_evaluator(model, metrics={'accuracy': Accuracy()}, device=device) handler = PyTorchIgnitePruningHandler(trial, 'accuracy', trainer) evaluator.add_event_handler(Events.COMPLETED, handler) @trainer.on(Events.EPOCH_COMPLETED) def log_validation_results(engine): 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. """ def __init__(self, trial, metric, trainer): # type: (Trial, str, Engine) -> None 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.exceptions.TrialPruned(message)
def _check_pytorch_ignite_availability(): # type: () -> None if not _available: raise ImportError( 'PyTorch Ignite is not available. Please install PyTorch Ignite to use this feature. ' 'PyTorch Ignite can be installed by executing `$ pip install pytorch-ignite`. ' 'For further information, please refer to the installation guide of PyTorch Ignite. ' '(The actual import error is as follows: ' + str(_import_error) + ')')