optuna.integration.skorch 源代码

from typing import Any

import optuna

with optuna._imports.try_import() as _imports:
    from skorch.callbacks import Callback
    from skorch.net import NeuralNet

if not _imports.is_successful():
    Callback = object  # NOQA

[文档]class SkorchPruningCallback(Callback): """Skorch callback to prune unpromising trials. .. versionadded:: 2.1.0 Args: trial: A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the objective function. monitor: An evaluation metric for pruning, e.g. ``val_loss`` or ``val_acc``. The metrics are obtained from the returned dictionaries, i.e., ``net.histroy``. The names thus depend on how this dictionary is formatted. """ def __init__(self, trial: optuna.trial.Trial, monitor: str) -> None: _imports.check() super().__init__() self._trial = trial self._monitor = monitor def on_epoch_end(self, net: "NeuralNet", **kwargs: Any) -> None: history = net.history if not history: return epoch = len(history) - 1 current_score = history[-1, self._monitor] self._trial.report(current_score, epoch) if self._trial.should_prune(): message = "Trial was pruned at epoch {}.".format(epoch) raise optuna.TrialPruned(message)