[python-package] make public API members explicit with module-level __all__ variables (#5655)

python-package/lightgbm/basic.py

@@ -20,6 +20,15 @@ import scipy.sparse
 from .compat import PANDAS_INSTALLED, concat, dt_DataTable, pd_CategoricalDtype, pd_DataFrame, pd_Series
 from .libpath import find_lib_path
 
+__all__ = [
+    'Booster',
+    'Dataset',
+    'LGBMDeprecationWarning',
+    'LightGBMError',
+    'register_logger',
+    'Sequence',
+]
+
 _DatasetHandle = ctypes.c_void_p
 _LGBM_EvalFunctionResultType = Tuple[str, float, bool]
 _LGBM_BoosterEvalMethodResultType = Tuple[str, str, float, bool]

python-package/lightgbm/callback.py

@@ -6,6 +6,13 @@ from typing import Any, Callable, Dict, List, Tuple, Union
 
 from .basic import _ConfigAliases, _LGBM_BoosterEvalMethodResultType, _log_info, _log_warning
 
+__all__ = [
+    'early_stopping',
+    'log_evaluation',
+    'record_evaluation',
+    'reset_parameter',
+]
+
 _EvalResultTuple = Union[
     List[_LGBM_BoosterEvalMethodResultType],
     List[Tuple[str, str, float, bool, float]]

python-package/lightgbm/compat.py

@@ -194,3 +194,5 @@ except ImportError:
 
         def _LGBMCpuCount(only_physical_cores: bool = True):
             return cpu_count()
+
+__all__ = []

python-package/lightgbm/dask.py

@@ -25,6 +25,12 @@ from .sklearn import (LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor, _LGB
                       _LGBM_ScikitEvalMetricType, _lgbmmodel_doc_custom_eval_note, _lgbmmodel_doc_fit,
                       _lgbmmodel_doc_predict)
 
+__all__ = [
+    'DaskLGBMClassifier',
+    'DaskLGBMRanker',
+    'DaskLGBMRegressor',
+]
+
 _DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series]
 _DaskMatrixLike = Union[dask_Array, dask_DataFrame]
 _DaskVectorLike = Union[dask_Array, dask_Series]

python-package/lightgbm/engine.py

@@ -14,6 +14,13 @@ from .basic import (Booster, Dataset, LightGBMError, _choose_param_value, _Confi
                     _LGBM_CustomObjectiveFunction, _log_warning)
 from .compat import SKLEARN_INSTALLED, _LGBMBaseCrossValidator, _LGBMGroupKFold, _LGBMStratifiedKFold
 
+__all__ = [
+    'cv',
+    'CVBooster',
+    'train',
+]
+
+
 _LGBM_CustomMetricFunction = Callable[
     [np.ndarray, Dataset],
     Tuple[str, float, bool]

python-package/lightgbm/libpath.py

@@ -4,6 +4,8 @@ from pathlib import Path
 from platform import system
 from typing import List
 
+__all__ = []
+
 
 def find_lib_path() -> List[str]:
     """Find the path to LightGBM library files.

python-package/lightgbm/plotting.py

@@ -11,6 +11,14 @@ from .basic import Booster, _data_from_pandas, _is_zero, _log_warning, _MissingT
 from .compat import GRAPHVIZ_INSTALLED, MATPLOTLIB_INSTALLED, pd_DataFrame
 from .sklearn import LGBMModel
 
+__all__ = [
+    'create_tree_digraph',
+    'plot_importance',
+    'plot_metric',
+    'plot_split_value_histogram',
+    'plot_tree',
+]
+
 
 def _check_not_tuple_of_2_elements(obj: Any, obj_name: str = 'obj') -> None:
     """Check object is not tuple or does not have 2 elements."""

python-package/lightgbm/sklearn.py

@@ -16,6 +16,13 @@ from .compat import (SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite
                      dt_DataTable, pd_DataFrame)
 from .engine import train
 
+__all__ = [
+    'LGBMClassifier',
+    'LGBMModel',
+    'LGBMRanker',
+    'LGBMRegressor',
+]
+
 _LGBM_ScikitCustomObjectiveFunction = Union[
     Callable[
         [np.ndarray, np.ndarray],