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[backport] Fix pylint and mypy. (#7563)

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* Fix Python typehint with upgraded mypy. (#7513)

* Fix pylint. (#7498)
This commit is contained in:
Jiaming Yuan 2022-01-14 14:23:09 +08:00 committed by GitHub
parent 87ddcf308e
commit ed8ba2150b
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 5 additions and 7 deletions
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4
python-package/xgboost/core.py
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@ -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

2
python-package/xgboost/dask.py
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@ -1834,7 +1834,7 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
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(

2
python-package/xgboost/data.py
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@ -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}.')

4
python-package/xgboost/sklearn.py
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@ -1354,9 +1354,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
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.