from typing import Any
from packaging import version
import optuna
from optuna._deprecated import deprecated
from optuna._imports import try_import
with try_import() as _imports:
import fastai
if version.parse(fastai.__version__) >= version.parse("2.0.0"):
raise ImportError(
f"You don't have fastai V1 installed! Fastai version: {fastai.__version__}"
)
from fastai.basic_train import Learner
from fastai.callbacks import TrackerCallback
if not _imports.is_successful():
TrackerCallback = object # NOQA
[docs]@deprecated("2.4.0")
class FastAIV1PruningCallback(TrackerCallback):
"""FastAI callback to prune unpromising trials for fastai.
.. note::
This callback is for fastai<2.0.
See `the example <https://github.com/optuna/optuna-examples/blob/main/
fastai/fastaiv1_simple.py>`__
if you want to add a pruning callback which monitors validation loss of a ``Learner``.
Example:
Register a pruning callback to ``learn.fit`` and ``learn.fit_one_cycle``.
.. code::
learn.fit(n_epochs, callbacks=[FastAIPruningCallback(learn, trial, "valid_loss")])
learn.fit_one_cycle(
n_epochs,
cyc_len,
max_lr,
callbacks=[FastAIPruningCallback(learn, trial, "valid_loss")],
)
Args:
learn:
`fastai.basic_train.Learner <https://docs.fast.ai/basic_train.html#Learner>`_.
trial:
A :class:`~optuna.trial.Trial` corresponding to the current
evaluation of the objective function.
monitor:
An evaluation metric for pruning, e.g. ``valid_loss`` and ``Accuracy``.
Please refer to `fastai.callbacks.TrackerCallback reference
<https://fastai1.fast.ai/callbacks.tracker.html#TrackerCallback>`_ for further
details.
"""
def __init__(self, learn: "Learner", trial: optuna.trial.Trial, monitor: str) -> None:
super().__init__(learn, monitor)
_imports.check()
self._trial = trial
def on_epoch_end(self, epoch: int, **kwargs: Any) -> None:
value = self.get_monitor_value()
if value is None:
return
# This conversion is necessary to avoid problems reported in issues.
# - https://github.com/optuna/optuna/issue/642
# - https://github.com/optuna/optuna/issue/655.
self._trial.report(float(value), step=epoch)
if self._trial.should_prune():
message = "Trial was pruned at epoch {}.".format(epoch)
raise optuna.TrialPruned(message)