[backport] Fix pylint and mypy.

python-package/xgboost/core.py

@@ -229,7 +229,7 @@ def _numpy2ctypes_type(dtype):
     }
     if np.intc is not np.int32:  # Windows
         _NUMPY_TO_CTYPES_MAPPING[np.intc] = _NUMPY_TO_CTYPES_MAPPING[np.int32]
-    if dtype not in _NUMPY_TO_CTYPES_MAPPING.keys():
+    if dtype not in _NUMPY_TO_CTYPES_MAPPING:
         raise TypeError(
             f"Supported types: {_NUMPY_TO_CTYPES_MAPPING.keys()}, got: {dtype}"
         )
@@ -266,7 +266,7 @@ def ctypes2cupy(cptr, length, dtype):
     from cupy.cuda.memory import UnownedMemory
 
     CUPY_TO_CTYPES_MAPPING = {cupy.float32: ctypes.c_float, cupy.uint32: ctypes.c_uint}
-    if dtype not in CUPY_TO_CTYPES_MAPPING.keys():
+    if dtype not in CUPY_TO_CTYPES_MAPPING:
         raise RuntimeError(f"Supported types: {CUPY_TO_CTYPES_MAPPING.keys()}")
     addr = ctypes.cast(cptr, ctypes.c_void_p).value
     # pylint: disable=c-extension-no-member,no-member

python-package/xgboost/dask.py

@@ -1834,7 +1834,7 @@ async def _predict_proba_async(
         vstack = update_wrapper(
             partial(da.vstack, allow_unknown_chunksizes=True), da.vstack
         )
-        return _cls_predict_proba(getattr(self, "n_classes_", None), predts, vstack)
+        return _cls_predict_proba(getattr(self, "n_classes_", 0), predts, vstack)
 
     # pylint: disable=missing-function-docstring
     def predict_proba(

python-package/xgboost/data.py

@@ -814,7 +814,7 @@ def dispatch_data_backend(
 
 def _to_data_type(dtype: str, name: str):
     dtype_map = {'float32': 1, 'float64': 2, 'uint32': 3, 'uint64': 4}
-    if dtype not in dtype_map.keys():
+    if dtype not in dtype_map:
         raise TypeError(
             f'Expecting float32, float64, uint32, uint64, got {dtype} ' +
             f'for {name}.')

python-package/xgboost/sklearn.py

@@ -1354,9 +1354,7 @@ def predict_proba(
             iteration_range=iteration_range
         )
         # If model is loaded from a raw booster there's no `n_classes_`
-        return _cls_predict_proba(
-            getattr(self, "n_classes_", None), class_probs, np.vstack
-        )
+        return _cls_predict_proba(getattr(self, "n_classes_", 0), class_probs, np.vstack)
 
     def evals_result(self) -> TrainingCallback.EvalsLog:
         """Return the evaluation results.