Source code for optuna.integration.chainer

from __future__ import absolute_import

import optuna
from optuna import types

    import chainer
    from import Extension
    from import triggers
    _available = True
except ImportError as e:
    _import_error = e
    # ChainerPruningExtension is disabled because Chainer is not available.
    _available = False
    # This alias is required to avoid ImportError at ChainerPruningExtension definition.
    Extension = object

    from typing import Tuple
    from typing import Union

    TriggerType = Union[Tuple[(int,
                               str)], triggers.IntervalTrigger, triggers.ManualScheduleTrigger]

[docs]class ChainerPruningExtension(Extension): """Chainer extension to prune unpromising trials. Example: Add a pruning extension which observes validation losses to `Chainer Trainer <>`_. .. code:: trainer.extend( ChainerPruningExtension(trial, 'validation/main/loss', (1, 'epoch'))) Args: trial: A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the objective function. observation_key: An evaluation metric for pruning, e.g., ``main/loss`` and ``validation/main/accuracy``. Please refer to `chainer.Reporter reference < util/generated/chainer.Reporter.html>`_ for further details. pruner_trigger: A trigger to execute pruning. ``pruner_trigger`` is an instance of `IntervalTrigger <>`_ or `ManualScheduleTrigger <>`_. `IntervalTrigger <https:// IntervalTrigger.html>`_ can be specified by a tuple of the interval length and its unit like ``(1, 'epoch')``. """ def __init__(self, trial, observation_key, pruner_trigger): # type: (optuna.trial.Trial, str, TriggerType) -> None _check_chainer_availability() self.trial = trial self.observation_key = observation_key self.pruner_trigger = if not (isinstance(self.pruner_trigger, triggers.IntervalTrigger) or isinstance(self.pruner_trigger, triggers.ManualScheduleTrigger)): pruner_type = type(self.pruner_trigger) raise TypeError("Invalid trigger class: " + str(pruner_type) + "\n" "Pruner trigger is supposed to be an instance of " "IntervalTrigger or ManualScheduleTrigger.") @staticmethod def _get_float_value(observation_value): # type: (Union[float, chainer.Variable]) -> float _check_chainer_availability() if isinstance(observation_value, chainer.Variable): observation_value = try: observation_value = float(observation_value) except TypeError: raise TypeError( 'Type of observation value is not supported by ChainerPruningExtension.\n' '{} cannot be casted to float.'.format(type(observation_value))) return observation_value def _observation_exists(self, trainer): # type: ( -> bool return self.pruner_trigger(trainer) and self.observation_key in trainer.observation def __call__(self, trainer): # type: ( -> None if not self._observation_exists(trainer): return current_score = self._get_float_value(trainer.observation[self.observation_key]) current_step = getattr(trainer.updater, self.pruner_trigger.unit), step=current_step) if self.trial.should_prune(current_step): message = "Trial was pruned at {} {}.".format(self.pruner_trigger.unit, current_step) raise optuna.structs.TrialPruned(message)
def _check_chainer_availability(): # type: () -> None if not _available: raise ImportError( 'Chainer is not available. Please install Chainer to use this feature. ' 'Chainer can be installed by executing `$ pip install chainer`. ' 'For further information, please refer to the installation guide of Chainer. ' '(The actual import error is as follows: ' + str(_import_error) + ')')