import optuna
from optuna import type_checking
with optuna._imports.try_import() as _imports:
import chainer
from chainer.training.extension import Extension
from chainer.training import triggers
if not _imports.is_successful():
Extension = object # NOQA
if type_checking.TYPE_CHECKING:
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.
See `the example <https://github.com/optuna/optuna/blob/master/
examples/pruning/chainer_integration.py>`__
if you want to add a pruning extension which observes validation
accuracy of a `Chainer Trainer <https://docs.chainer.org/en/stable/
reference/generated/chainer.training.Trainer.html>`_.
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 <https://docs.chainer.org/en/stable/reference/
util/generated/chainer.Reporter.html>`_ for further details.
pruner_trigger:
A trigger to execute pruning. ``pruner_trigger`` is an instance of
`IntervalTrigger <https://docs.chainer.org/en/stable/reference/generated/
chainer.training.triggers.IntervalTrigger.html>`_ or
`ManualScheduleTrigger <https://docs.chainer.org/en/stable/reference/generated/
chainer.training.triggers.ManualScheduleTrigger.html>`_. `IntervalTrigger <https://
docs.chainer.org/en/stable/reference/generated/chainer.training.triggers.
IntervalTrigger.html>`_ can be specified by a tuple of the interval length and its
unit like ``(1, 'epoch')``.
"""
[docs] def __init__(self, trial, observation_key, pruner_trigger):
# type: (optuna.trial.Trial, str, TriggerType) -> None
_imports.check()
self._trial = trial
self._observation_key = observation_key
self._pruner_trigger = chainer.training.get_trigger(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
_imports.check()
if isinstance(observation_value, chainer.Variable):
observation_value = observation_value.data
try:
observation_value = float(observation_value)
except TypeError:
raise TypeError(
"Type of observation value is not supported by ChainerPruningExtension.\n"
"{} cannot be cast to float.".format(type(observation_value))
)
return observation_value
def _observation_exists(self, trainer):
# type: (chainer.training.Trainer) -> bool
return self._pruner_trigger(trainer) and self._observation_key in trainer.observation
def __call__(self, trainer):
# type: (chainer.training.Trainer) -> 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)
self._trial.report(current_score, step=current_step)
if self._trial.should_prune():
message = "Trial was pruned at {} {}.".format(self._pruner_trigger.unit, current_step)
raise optuna.TrialPruned(message)