Merge branch 'master' into test-integration

optuna/_hypervolume/__init__.py

@@ -1,4 +1,12 @@
-from optuna._hypervolume.base import BaseHypervolume  # NOQA
-from optuna._hypervolume.utils import _compute_2d  # NOQA
-from optuna._hypervolume.utils import _compute_2points_volume  # NOQA
-from optuna._hypervolume.wfg import WFG  # NOQA
+from optuna._hypervolume.base import BaseHypervolume
+from optuna._hypervolume.utils import _compute_2d
+from optuna._hypervolume.utils import _compute_2points_volume
+from optuna._hypervolume.wfg import WFG
+
+
+__all__ = [
+    "BaseHypervolume",
+    "_compute_2d",
+    "_compute_2points_volume",
+    "WFG",
+]

optuna/importance/_fanova/__init__.py

@@ -1 +1,4 @@
-from optuna.importance._fanova._evaluator import FanovaImportanceEvaluator  # NOQA
+from optuna.importance._fanova._evaluator import FanovaImportanceEvaluator
+
+
+__all__ = ["FanovaImportanceEvaluator"]

optuna/integration/__init__.py

@@ -34,40 +34,37 @@
 }
 
 
-__all__ = list(_import_structure.keys()) + sum(_import_structure.values(), [])
-
-
 if TYPE_CHECKING:
-    from optuna.integration.allennlp import AllenNLPExecutor  # NOQA
-    from optuna.integration.allennlp import AllenNLPPruningCallback  # NOQA
-    from optuna.integration.botorch import BoTorchSampler  # NOQA
-    from optuna.integration.catalyst import CatalystPruningCallback  # NOQA
-    from optuna.integration.catboost import CatBoostPruningCallback  # NOQA
-    from optuna.integration.chainer import ChainerPruningExtension  # NOQA
-    from optuna.integration.chainermn import ChainerMNStudy  # NOQA
-    from optuna.integration.cma import CmaEsSampler  # NOQA
-    from optuna.integration.cma import PyCmaSampler  # NOQA
-    from optuna.integration.fastaiv1 import FastAIV1PruningCallback  # NOQA
-    from optuna.integration.fastaiv2 import FastAIPruningCallback  # NOQA
-    from optuna.integration.fastaiv2 import FastAIV2PruningCallback  # NOQA
-    from optuna.integration.keras import KerasPruningCallback  # NOQA
-    from optuna.integration.lightgbm import LightGBMPruningCallback  # NOQA
-    from optuna.integration.lightgbm import LightGBMTuner  # NOQA
-    from optuna.integration.lightgbm import LightGBMTunerCV  # NOQA
-    from optuna.integration.mlflow import MLflowCallback  # NOQA
-    from optuna.integration.mxnet import MXNetPruningCallback  # NOQA
-    from optuna.integration.pytorch_distributed import TorchDistributedTrial  # NOQA
-    from optuna.integration.pytorch_ignite import PyTorchIgnitePruningHandler  # NOQA
-    from optuna.integration.pytorch_lightning import PyTorchLightningPruningCallback  # NOQA
-    from optuna.integration.shap import ShapleyImportanceEvaluator  # NOQA
-    from optuna.integration.sklearn import OptunaSearchCV  # NOQA
-    from optuna.integration.skopt import SkoptSampler  # NOQA
-    from optuna.integration.skorch import SkorchPruningCallback  # NOQA
-    from optuna.integration.tensorboard import TensorBoardCallback  # NOQA
-    from optuna.integration.tensorflow import TensorFlowPruningHook  # NOQA
-    from optuna.integration.tfkeras import TFKerasPruningCallback  # NOQA
-    from optuna.integration.wandb import WeightsAndBiasesCallback  # NOQA
-    from optuna.integration.xgboost import XGBoostPruningCallback  # NOQA
+    from optuna.integration.allennlp import AllenNLPExecutor
+    from optuna.integration.allennlp import AllenNLPPruningCallback
+    from optuna.integration.botorch import BoTorchSampler
+    from optuna.integration.catalyst import CatalystPruningCallback
+    from optuna.integration.catboost import CatBoostPruningCallback
+    from optuna.integration.chainer import ChainerPruningExtension
+    from optuna.integration.chainermn import ChainerMNStudy
+    from optuna.integration.cma import CmaEsSampler
+    from optuna.integration.cma import PyCmaSampler
+    from optuna.integration.fastaiv1 import FastAIV1PruningCallback
+    from optuna.integration.fastaiv2 import FastAIPruningCallback
+    from optuna.integration.fastaiv2 import FastAIV2PruningCallback
+    from optuna.integration.keras import KerasPruningCallback
+    from optuna.integration.lightgbm import LightGBMPruningCallback
+    from optuna.integration.lightgbm import LightGBMTuner
+    from optuna.integration.lightgbm import LightGBMTunerCV
+    from optuna.integration.mlflow import MLflowCallback
+    from optuna.integration.mxnet import MXNetPruningCallback
+    from optuna.integration.pytorch_distributed import TorchDistributedTrial
+    from optuna.integration.pytorch_ignite import PyTorchIgnitePruningHandler
+    from optuna.integration.pytorch_lightning import PyTorchLightningPruningCallback
+    from optuna.integration.shap import ShapleyImportanceEvaluator
+    from optuna.integration.sklearn import OptunaSearchCV
+    from optuna.integration.skopt import SkoptSampler
+    from optuna.integration.skorch import SkorchPruningCallback
+    from optuna.integration.tensorboard import TensorBoardCallback
+    from optuna.integration.tensorflow import TensorFlowPruningHook
+    from optuna.integration.tfkeras import TFKerasPruningCallback
+    from optuna.integration.wandb import WeightsAndBiasesCallback
+    from optuna.integration.xgboost import XGBoostPruningCallback
 else:
 
     class _IntegrationModule(ModuleType):
@@ -107,3 +104,36 @@ def _get_module(self, module_name: str) -> ModuleType:
             return importlib.import_module("." + module_name, self.__name__)
 
     sys.modules[__name__] = _IntegrationModule(__name__)
+
+__all__ = [
+    "AllenNLPExecutor",
+    "AllenNLPPruningCallback",
+    "BoTorchSampler",
+    "CatalystPruningCallback",
+    "CatBoostPruningCallback",
+    "ChainerPruningExtension",
+    "ChainerMNStudy",
+    "CmaEsSampler",
+    "PyCmaSampler",
+    "MLflowCallback",
+    "WeightsAndBiasesCallback",
+    "KerasPruningCallback",
+    "LightGBMPruningCallback",
+    "LightGBMTuner",
+    "LightGBMTunerCV",
+    "TorchDistributedTrial",
+    "PyTorchIgnitePruningHandler",
+    "PyTorchLightningPruningCallback",
+    "OptunaSearchCV",
+    "ShapleyImportanceEvaluator",
+    "SkorchPruningCallback",
+    "MXNetPruningCallback",
+    "SkoptSampler",
+    "TensorBoardCallback",
+    "TensorFlowPruningHook",
+    "TFKerasPruningCallback",
+    "XGBoostPruningCallback",
+    "FastAIV1PruningCallback",
+    "FastAIV2PruningCallback",
+    "FastAIPruningCallback",
+]

optuna/integration/_lightgbm_tuner/__init__.py

@@ -2,13 +2,15 @@
 
 from optuna.integration._lightgbm_tuner.optimize import _imports
 from optuna.integration._lightgbm_tuner.optimize import LightGBMTuner
-from optuna.integration._lightgbm_tuner.optimize import LightGBMTunerCV  # NOQA
+from optuna.integration._lightgbm_tuner.optimize import LightGBMTunerCV
 
 
 if _imports.is_successful():
-    from optuna.integration._lightgbm_tuner.sklearn import LGBMClassifier  # NOQA
-    from optuna.integration._lightgbm_tuner.sklearn import LGBMModel  # NOQA
-    from optuna.integration._lightgbm_tuner.sklearn import LGBMRegressor  # NOQA
+    from optuna.integration._lightgbm_tuner.sklearn import LGBMClassifier
+    from optuna.integration._lightgbm_tuner.sklearn import LGBMModel
+    from optuna.integration._lightgbm_tuner.sklearn import LGBMRegressor
+
+__all__ = ["LightGBMTuner", "LightGBMTunerCV", "LGBMClassifier", "LGBMModel", "LGBMRegressor"]
 
 
 def train(*args: Any, **kwargs: Any) -> Any:

optuna/integration/allennlp/__init__.py

@@ -1,3 +1,6 @@
-from optuna.integration.allennlp._dump_best_config import dump_best_config  # NOQA
-from optuna.integration.allennlp._executor import AllenNLPExecutor  # NOQA
-from optuna.integration.allennlp._pruner import AllenNLPPruningCallback  # NOQA
+from optuna.integration.allennlp._dump_best_config import dump_best_config
+from optuna.integration.allennlp._executor import AllenNLPExecutor
+from optuna.integration.allennlp._pruner import AllenNLPPruningCallback
+
+
+__all__ = ["dump_best_config", "AllenNLPExecutor", "AllenNLPPruningCallback"]

optuna/integration/allennlp/_pruner.py

@@ -116,7 +116,7 @@ def __init__(
     ):
         _imports.check()
 
-        if version.parse(allennlp.__version__) < version.parse("2.0.0"):
+        if version.parse(allennlp.__version__) < version.parse("2.0.0"):  # type: ignore
             raise ImportError(
                 "`AllenNLPPruningCallback` requires AllenNLP>=v2.0.0."
                 "If you want to use a callback with an old version of AllenNLP, "

optuna/integration/chainer.py

@@ -64,12 +64,14 @@ def __init__(
             )
 
     @staticmethod
-    def _get_float_value(observation_value: Union[float, "chainer.Variable"]) -> float:
+    def _get_float_value(
+        observation_value: Union[float, "chainer.Variable"]  # type: ignore
+    ) -> float:
 
         _imports.check()
 
         try:
-            if isinstance(observation_value, chainer.Variable):
+            if isinstance(observation_value, chainer.Variable):  # type: ignore
                 return float(observation_value.data)  # type: ignore
             else:
                 return float(observation_value)
@@ -79,11 +81,11 @@ def _get_float_value(observation_value: Union[float, "chainer.Variable"]) -> flo
                 "{} cannot be cast to float.".format(type(observation_value))
             ) from None
 
-    def _observation_exists(self, trainer: "chainer.training.Trainer") -> bool:
+    def _observation_exists(self, trainer: "chainer.training.Trainer") -> bool:  # type: ignore
 
         return self._pruner_trigger(trainer) and self._observation_key in trainer.observation
 
-    def __call__(self, trainer: "chainer.training.Trainer") -> None:
+    def __call__(self, trainer: "chainer.training.Trainer") -> None:  # type: ignore
 
         if not self._observation_exists(trainer):
             return

optuna/integration/lightgbm.py

@@ -8,16 +8,16 @@
 
 
 with try_import() as _imports:
-    import lightgbm as lgb  # NOQA
-    from lightgbm.callback import CallbackEnv  # NOQA
+    import lightgbm as lgb
+    from lightgbm.callback import CallbackEnv
 
 # Attach lightgbm API.
 if _imports.is_successful():
     # To pass tests/integration_tests/lightgbm_tuner_tests/test_optimize.py.
-    from lightgbm import Dataset  # NOQA
+    from lightgbm import Dataset
 
-    from optuna.integration._lightgbm_tuner import LightGBMTuner  # NOQA
-    from optuna.integration._lightgbm_tuner import LightGBMTunerCV  # NOQA
+    from optuna.integration._lightgbm_tuner import LightGBMTuner
+    from optuna.integration._lightgbm_tuner import LightGBMTunerCV
 
     _names_from_tuners = ["train", "LGBMModel", "LGBMClassifier", "LGBMRegressor"]
 
@@ -36,6 +36,8 @@
     setattr(sys.modules[__name__], "LightGBMTuner", tuner.__dict__["LightGBMTuner"])
     setattr(sys.modules[__name__], "LightGBMTunerCV", tuner.__dict__["LightGBMTunerCV"])
 
+__all__ = ["Dataset", "LightGBMTuner", "LightGBMTunerCV"]
+
 
 class LightGBMPruningCallback:
     """Callback for LightGBM to prune unpromising trials.

optuna/integration/pytorch_lightning.py

@@ -61,7 +61,7 @@ def on_init_start(self, trainer: Trainer) -> None:
             trainer._accelerator_connector.distributed_backend is not None  # type: ignore
         )
         if self.is_ddp_backend:
-            if version.parse(pl.__version__) < version.parse("1.5.0"):
+            if version.parse(pl.__version__) < version.parse("1.5.0"):  # type: ignore
                 raise ValueError("PyTorch Lightning>=1.5.0 is required in DDP.")
             if not (
                 isinstance(self._trial.study._storage, _CachedStorage)

optuna/logging.py

@@ -1,17 +1,27 @@
 import logging
-from logging import CRITICAL  # NOQA
-from logging import DEBUG  # NOQA
-from logging import ERROR  # NOQA
-from logging import FATAL  # NOQA
-from logging import INFO  # NOQA
-from logging import WARN  # NOQA
-from logging import WARNING  # NOQA
+from logging import CRITICAL
+from logging import DEBUG
+from logging import ERROR
+from logging import FATAL
+from logging import INFO
+from logging import WARN
+from logging import WARNING
 import threading
 from typing import Optional
 
 import colorlog
 
 
+__all__ = [
+    "CRITICAL",
+    "DEBUG",
+    "ERROR",
+    "FATAL",
+    "INFO",
+    "WARN",
+    "WARNING",
+]
+
 _lock: threading.Lock = threading.Lock()
 _default_handler: Optional[logging.Handler] = None
 

optuna/multi_objective/__init__.py

@@ -1,9 +1,17 @@
 from optuna._imports import _LazyImport
-from optuna.multi_objective import samplers  # NOQA
-from optuna.multi_objective import study  # NOQA
-from optuna.multi_objective import trial  # NOQA
-from optuna.multi_objective.study import create_study  # NOQA
-from optuna.multi_objective.study import load_study  # NOQA
+from optuna.multi_objective import samplers
+from optuna.multi_objective import study
+from optuna.multi_objective import trial
+from optuna.multi_objective.study import create_study
+from optuna.multi_objective.study import load_study
 
 
 visualization = _LazyImport("optuna.multi_objective.visualization")
+
+__all__ = [
+    "samplers",
+    "study",
+    "trial",
+    "create_study",
+    "load_study",
+]

optuna/multi_objective/samplers/__init__.py

@@ -1,5 +1,14 @@
-from optuna.multi_objective.samplers._adapter import _MultiObjectiveSamplerAdapter  # NOQA
-from optuna.multi_objective.samplers._base import BaseMultiObjectiveSampler  # NOQA
-from optuna.multi_objective.samplers._motpe import MOTPEMultiObjectiveSampler  # NOQA
-from optuna.multi_objective.samplers._nsga2 import NSGAIIMultiObjectiveSampler  # NOQA
-from optuna.multi_objective.samplers._random import RandomMultiObjectiveSampler  # NOQA
+from optuna.multi_objective.samplers._adapter import _MultiObjectiveSamplerAdapter
+from optuna.multi_objective.samplers._base import BaseMultiObjectiveSampler
+from optuna.multi_objective.samplers._motpe import MOTPEMultiObjectiveSampler
+from optuna.multi_objective.samplers._nsga2 import NSGAIIMultiObjectiveSampler
+from optuna.multi_objective.samplers._random import RandomMultiObjectiveSampler
+
+
+__all__ = [
+    "_MultiObjectiveSamplerAdapter",
+    "BaseMultiObjectiveSampler",
+    "MOTPEMultiObjectiveSampler",
+    "NSGAIIMultiObjectiveSampler",
+    "RandomMultiObjectiveSampler",
+]

optuna/multi_objective/visualization/__init__.py

@@ -1,2 +1,5 @@
-from optuna.multi_objective.visualization._pareto_front import plot_pareto_front  # NOQA
-from optuna.visualization import is_available  # NOQA
+from optuna.multi_objective.visualization._pareto_front import plot_pareto_front
+from optuna.visualization import is_available
+
+
+__all__ = ["plot_pareto_front", "is_available"]

optuna/samplers/nsgaii/_sampler.py

@@ -15,8 +15,8 @@
 import numpy as np
 
 import optuna
-from optuna._experimental import ExperimentalWarning
 from optuna.distributions import BaseDistribution
+from optuna.exceptions import ExperimentalWarning
 from optuna.samplers._base import _CONSTRAINTS_KEY
 from optuna.samplers._base import _process_constraints_after_trial
 from optuna.samplers._base import BaseSampler

optuna/study/_dataframe.py

@@ -19,6 +19,8 @@
 if not _imports.is_successful():
     pd = object  # NOQA
 
+__all__ = ["pd"]
+
 
 def _create_records_and_aggregate_column(
     study: "optuna.Study", attrs: Tuple[str, ...]

optuna/visualization/_plotly_imports.py

@@ -3,13 +3,13 @@
 from optuna._imports import try_import
 
 
-with try_import() as _imports:  # NOQA
-    import plotly  # NOQA
+with try_import() as _imports:
+    import plotly
     from plotly import __version__ as plotly_version
-    import plotly.graph_objs as go  # NOQA
-    from plotly.graph_objs import Contour  # NOQA
-    from plotly.graph_objs import Scatter  # NOQA
-    from plotly.subplots import make_subplots  # NOQA
+    import plotly.graph_objs as go
+    from plotly.graph_objs import Contour
+    from plotly.graph_objs import Scatter
+    from plotly.subplots import make_subplots
 
     if version.parse(plotly_version) < version.parse("4.0.0"):
         raise ImportError(
@@ -19,3 +19,5 @@
             "For further information, please refer to the installation guide of plotly. ",
             name="plotly",
         )
+
+__all__ = ["_imports", "plotly", "go", "Contour", "Scatter", "make_subplots"]