Merge pull request #5249 from y0z/feature/migrate-lightgbm

Migrate LightGBM integration

.readthedocs.yml

@@ -19,7 +19,7 @@ formats: all
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  # `sphinx` requires either Python >= 3.8 or `typed-ast` to reflect type comments 
+  # `sphinx` requires either Python >= 3.8 or `typed-ast` to reflect type comments
   # in the documentation. See: https://github.com/sphinx-doc/sphinx/pull/6984
   install:
     - method: pip

docs/source/reference/integration.rst

@@ -18,18 +18,6 @@ For most of the ML frameworks supported by Optuna, the corresponding Optuna inte
 
 For scikit-learn, an integrated :class:`~optuna.integration.OptunaSearchCV` estimator is available that combines scikit-learn BaseEstimator functionality with access to a class-level ``Study`` object.
 
-LightGBM
---------
-
-.. autosummary::
-   :toctree: generated/
-   :nosignatures:
-
-   optuna.integration.LightGBMPruningCallback
-   optuna.integration.lightgbm.train
-   optuna.integration.lightgbm.LightGBMTuner
-   optuna.integration.lightgbm.LightGBMTunerCV
-
 MLflow
 ------
 

optuna/integration/__init__.py

@@ -35,6 +35,41 @@
 }
 
 
+__all__ = [
+    "AllenNLPExecutor",
+    "AllenNLPPruningCallback",
+    "BoTorchSampler",
+    "CatalystPruningCallback",
+    "CatBoostPruningCallback",
+    "ChainerPruningExtension",
+    "ChainerMNStudy",
+    "CmaEsSampler",
+    "PyCmaSampler",
+    "DaskStorage",
+    "MLflowCallback",
+    "WeightsAndBiasesCallback",
+    "KerasPruningCallback",
+    "LightGBMPruningCallback",
+    "LightGBMTuner",
+    "LightGBMTunerCV",
+    "TorchDistributedTrial",
+    "PyTorchIgnitePruningHandler",
+    "PyTorchLightningPruningCallback",
+    "OptunaSearchCV",
+    "ShapleyImportanceEvaluator",
+    "SkorchPruningCallback",
+    "MXNetPruningCallback",
+    "SkoptSampler",
+    "TensorBoardCallback",
+    "TensorFlowPruningHook",
+    "TFKerasPruningCallback",
+    "XGBoostPruningCallback",
+    "FastAIV1PruningCallback",
+    "FastAIV2PruningCallback",
+    "FastAIPruningCallback",
+]
+
+
 if TYPE_CHECKING:
     from optuna.integration.allennlp import AllenNLPExecutor
     from optuna.integration.allennlp import AllenNLPPruningCallback
@@ -77,6 +112,7 @@ class _IntegrationModule(ModuleType):
         imports all submodules and their dependencies (e.g., chainer, keras, lightgbm) all at once.
         """
 
+        __all__ = __all__
         __file__ = globals()["__file__"]
         __path__ = [os.path.dirname(__file__)]
 
@@ -113,37 +149,3 @@ def _get_module(self, module_name: str) -> ModuleType:
                 )
 
     sys.modules[__name__] = _IntegrationModule(__name__)
-
-__all__ = [
-    "AllenNLPExecutor",
-    "AllenNLPPruningCallback",
-    "BoTorchSampler",
-    "CatalystPruningCallback",
-    "CatBoostPruningCallback",
-    "ChainerPruningExtension",
-    "ChainerMNStudy",
-    "CmaEsSampler",
-    "PyCmaSampler",
-    "DaskStorage",
-    "MLflowCallback",
-    "WeightsAndBiasesCallback",
-    "KerasPruningCallback",
-    "LightGBMPruningCallback",
-    "LightGBMTuner",
-    "LightGBMTunerCV",
-    "TorchDistributedTrial",
-    "PyTorchIgnitePruningHandler",
-    "PyTorchLightningPruningCallback",
-    "OptunaSearchCV",
-    "ShapleyImportanceEvaluator",
-    "SkorchPruningCallback",
-    "MXNetPruningCallback",
-    "SkoptSampler",
-    "TensorBoardCallback",
-    "TensorFlowPruningHook",
-    "TFKerasPruningCallback",
-    "XGBoostPruningCallback",
-    "FastAIV1PruningCallback",
-    "FastAIV2PruningCallback",
-    "FastAIPruningCallback",
-]