Merge pull request optuna#4662 from toshihikoyanase/remove-tfkeras-in…

…tegration

Remove `tf.keras` integration.

.github/workflows/tests-integration.yml

@@ -87,7 +87,6 @@ jobs:
             --ignore tests/integration_tests/test_skopt.py \
             --ignore tests/integration_tests/test_tensorboard.py \
             --ignore tests/integration_tests/test_tensorflow.py \
-            --ignore tests/integration_tests/test_tfkeras.py \
             --ignore tests/integration_tests/lightgbm_tuner_tests \
             --ignore tests/importance_tests/test_init.py \
             --ignore tests/samplers_tests/test_samplers.py
@@ -111,7 +110,6 @@ jobs:
             --ignore tests/integration_tests/test_skopt.py \
             --ignore tests/integration_tests/test_tensorboard.py \
             --ignore tests/integration_tests/test_tensorflow.py \
-            --ignore tests/integration_tests/test_tfkeras.py \
             --ignore tests/integration_tests/lightgbm_tuner_tests \
             --ignore tests/importance_tests/test_init.py \
             --ignore tests/samplers_tests/test_samplers.py

docs/source/reference/integration.rst

@@ -151,7 +151,6 @@ TensorFlow
 
    optuna.integration.TensorBoardCallback
    optuna.integration.TensorFlowPruningHook
-   optuna.integration.TFKerasPruningCallback
 
 XGBoost
 -------

optuna/integration/tfkeras.py

@@ -1,60 +1,4 @@
-from typing import Any
-from typing import Dict
-from typing import Optional
-import warnings
+from optuna_integration.tfkeras import TFKerasPruningCallback
 
-import optuna
 
-
-with optuna._imports.try_import() as _imports:
-    from tensorflow.keras.callbacks import Callback
-
-if not _imports.is_successful():
-    Callback = object  # NOQA
-
-
-class TFKerasPruningCallback(Callback):
-    """tf.keras callback to prune unpromising trials.
-
-    This callback is intend to be compatible for TensorFlow v1 and v2,
-    but only tested with TensorFlow v2.
-
-    See `the example <https://github.com/optuna/optuna-examples/blob/main/
-    tfkeras/tfkeras_integration.py>`__
-    if you want to add a pruning callback which observes the validation accuracy.
-
-    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``.
-    """
-
-    def __init__(self, trial: optuna.trial.Trial, monitor: str) -> None:
-        super().__init__()
-
-        _imports.check()
-
-        self._trial = trial
-        self._monitor = monitor
-
-    def on_epoch_end(self, epoch: int, logs: Optional[Dict[str, Any]] = None) -> None:
-        logs = logs or {}
-        current_score = logs.get(self._monitor)
-
-        if current_score is None:
-            message = (
-                "The metric '{}' is not in the evaluation logs for pruning. "
-                "Please make sure you set the correct metric name.".format(self._monitor)
-            )
-            warnings.warn(message)
-            return
-
-        # Report current score and epoch to Optuna's trial.
-        self._trial.report(float(current_score), step=epoch)
-
-        # Prune trial if needed
-        if self._trial.should_prune():
-            message = "Trial was pruned at epoch {}.".format(epoch)
-            raise optuna.TrialPruned(message)
+__all__ = ["TFKerasPruningCallback"]