[ci] [python-package] check for untyped definitions with mypy (#6339)

.ci/test.sh

@@ -74,6 +74,7 @@ if [[ $TASK == "lint" ]]; then
         ${CONDA_PYTHON_REQUIREMENT} \
         cmakelint \
         cpplint \
+        'matplotlib>=3.8.3' \
         mypy \
         'pre-commit>=3.6.0' \
         'pyarrow>=14.0' \

python-package/lightgbm/basic.py

@@ -13,7 +13,7 @@ from os import SEEK_END, environ
 from os.path import getsize
 from pathlib import Path
 from tempfile import NamedTemporaryFile
-from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, Iterator, List, Optional, Set, Tuple, Union
 
 import numpy as np
 import scipy.sparse
@@ -537,13 +537,13 @@ def _param_dict_to_str(data: Optional[Dict[str, Any]]) -> str:
 class _TempFile:
     """Proxy class to workaround errors on Windows."""
 
-    def __enter__(self):
+    def __enter__(self) -> "_TempFile":
         with NamedTemporaryFile(prefix="lightgbm_tmp_", delete=True) as f:
             self.name = f.name
             self.path = Path(self.name)
         return self
 
-    def __exit__(self, exc_type, exc_val, exc_tb):
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
         if self.path.is_file():
             self.path.unlink()
 
@@ -595,7 +595,7 @@ class _ConfigAliases:
         )
 
     @classmethod
-    def get(cls, *args) -> Set[str]:
+    def get(cls, *args: str) -> Set[str]:
         if cls.aliases is None:
             cls.aliases = cls._get_all_param_aliases()
         ret = set()
@@ -610,7 +610,7 @@ class _ConfigAliases:
         return cls.aliases.get(name, [name])
 
     @classmethod
-    def get_by_alias(cls, *args) -> Set[str]:
+    def get_by_alias(cls, *args: str) -> Set[str]:
         if cls.aliases is None:
             cls.aliases = cls._get_all_param_aliases()
         ret = set(args)
@@ -1563,7 +1563,7 @@ class _InnerPredictor:
         start_iteration: int,
         num_iteration: int,
         predict_type: int,
-    ):
+    ) -> Tuple[Union[List[scipy.sparse.csc_matrix], List[scipy.sparse.csr_matrix]], int]:
         ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr)
         ptr_data, type_ptr_data, _ = _c_float_array(csc.data)
         csc_indices = csc.indices.astype(np.int32, copy=False)
@@ -1813,7 +1813,7 @@ class Dataset:
         self._need_slice = True
         self._predictor: Optional[_InnerPredictor] = None
         self.pandas_categorical: Optional[List[List]] = None
-        self._params_back_up = None
+        self._params_back_up: Optional[Dict[str, Any]] = None
         self.version = 0
         self._start_row = 0  # Used when pushing rows one by one.
 
@@ -2195,7 +2195,7 @@ class Dataset:
         return self.set_feature_name(feature_name)
 
     @staticmethod
-    def _yield_row_from_seqlist(seqs: List[Sequence], indices: Iterable[int]):
+    def _yield_row_from_seqlist(seqs: List[Sequence], indices: Iterable[int]) -> Iterator[np.ndarray]:
         offset = 0
         seq_id = 0
         seq = seqs[seq_id]
@@ -2697,7 +2697,7 @@ class Dataset:
             return self
         params = deepcopy(params)
 
-        def update():
+        def update() -> None:
             if not self.params:
                 self.params = params
             else:
@@ -3704,7 +3704,7 @@ class Booster:
     def __copy__(self) -> "Booster":
         return self.__deepcopy__(None)
 
-    def __deepcopy__(self, _) -> "Booster":
+    def __deepcopy__(self, *args: Any, **kwargs: Any) -> "Booster":
         model_str = self.model_to_string(num_iteration=-1)
         return Booster(model_str=model_str)
 
@@ -4757,7 +4757,7 @@ class Booster:
         dataset_params: Optional[Dict[str, Any]] = None,
         free_raw_data: bool = True,
         validate_features: bool = False,
-        **kwargs,
+        **kwargs: Any,
     ) -> "Booster":
         """Refit the existing Booster by new data.
 

python-package/lightgbm/compat.py

 # coding: utf-8
 """Compatibility library."""
 
-from typing import List
+from typing import Any, List
 
 """pandas"""
 try:
@@ -20,19 +20,19 @@ except ImportError:
     class pd_Series:  # type: ignore
         """Dummy class for pandas.Series."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class pd_DataFrame:  # type: ignore
         """Dummy class for pandas.DataFrame."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class pd_CategoricalDtype:  # type: ignore
         """Dummy class for pandas.CategoricalDtype."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     concat = None
@@ -45,7 +45,7 @@ except ImportError:
     class np_random_Generator:  # type: ignore
         """Dummy class for np.random.Generator."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
 
@@ -80,7 +80,7 @@ except ImportError:
     class dt_DataTable:  # type: ignore
         """Dummy class for datatable.DataTable."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
 
@@ -104,7 +104,7 @@ try:
         from sklearn.utils.validation import check_consistent_length
 
         # dummy function to support older version of scikit-learn
-        def _check_sample_weight(sample_weight, X, dtype=None):
+        def _check_sample_weight(sample_weight: Any, X: Any, dtype: Any = None) -> Any:
             check_consistent_length(sample_weight, X)
             return sample_weight
 
@@ -176,31 +176,31 @@ except ImportError:
     class Client:  # type: ignore
         """Dummy class for dask.distributed.Client."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class Future:  # type: ignore
         """Dummy class for dask.distributed.Future."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class dask_Array:  # type: ignore
         """Dummy class for dask.array.Array."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class dask_DataFrame:  # type: ignore
         """Dummy class for dask.dataframe.DataFrame."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class dask_Series:  # type: ignore
         """Dummy class for dask.dataframe.Series."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
 
@@ -222,19 +222,19 @@ except ImportError:
     class pa_Array:  # type: ignore
         """Dummy class for pa.Array."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class pa_ChunkedArray:  # type: ignore
         """Dummy class for pa.ChunkedArray."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class pa_Table:  # type: ignore
         """Dummy class for pa.Table."""
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class arrow_cffi:  # type: ignore
@@ -245,7 +245,7 @@ except ImportError:
         cast = None
         new = None
 
-        def __init__(self, *args, **kwargs):
+        def __init__(self, *args: Any, **kwargs: Any):
             pass
 
     class pa_compute:  # type: ignore

python-package/lightgbm/plotting.py

@@ -3,7 +3,7 @@
 import math
 from copy import deepcopy
 from io import BytesIO
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -19,6 +19,9 @@ __all__ = [
     "plot_tree",
 ]
 
+if TYPE_CHECKING:
+    import matplotlib
+
 
 def _check_not_tuple_of_2_elements(obj: Any, obj_name: str) -> None:
     """Check object is not tuple or does not have 2 elements."""
@@ -32,7 +35,7 @@ def _float2str(value: float, precision: Optional[int]) -> str:
 
 def plot_importance(
     booster: Union[Booster, LGBMModel],
-    ax=None,
+    ax: "Optional[matplotlib.axes.Axes]" = None,
     height: float = 0.2,
     xlim: Optional[Tuple[float, float]] = None,
     ylim: Optional[Tuple[float, float]] = None,
@@ -168,7 +171,7 @@ def plot_split_value_histogram(
     booster: Union[Booster, LGBMModel],
     feature: Union[int, str],
     bins: Union[int, str, None] = None,
-    ax=None,
+    ax: "Optional[matplotlib.axes.Axes]" = None,
     width_coef: float = 0.8,
     xlim: Optional[Tuple[float, float]] = None,
     ylim: Optional[Tuple[float, float]] = None,
@@ -284,7 +287,7 @@ def plot_metric(
     booster: Union[Dict, LGBMModel],
     metric: Optional[str] = None,
     dataset_names: Optional[List[str]] = None,
-    ax=None,
+    ax: "Optional[matplotlib.axes.Axes]" = None,
     xlim: Optional[Tuple[float, float]] = None,
     ylim: Optional[Tuple[float, float]] = None,
     title: Optional[str] = "Metric during training",
@@ -735,7 +738,7 @@ def create_tree_digraph(
 
 def plot_tree(
     booster: Union[Booster, LGBMModel],
-    ax=None,
+    ax: "Optional[matplotlib.axes.Axes]" = None,
     tree_index: int = 0,
     figsize: Optional[Tuple[float, float]] = None,
     dpi: Optional[int] = None,

python-package/lightgbm/sklearn.py

@@ -478,7 +478,7 @@ class LGBMModel(_LGBMModelBase):
         random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None,
         n_jobs: Optional[int] = None,
         importance_type: str = "split",
-        **kwargs,
+        **kwargs: Any,
     ):
         r"""Construct a gradient boosting model.
 

python-package/pyproject.toml

@@ -92,6 +92,7 @@ skip_glob = [
 ]
 
 [tool.mypy]
+disallow_untyped_defs = true
 exclude = 'build/*|compile/*|docs/*|examples/*|external_libs/*|lightgbm-python/*|tests/*'
 ignore_missing_imports = true