665817bd4f
### What changes were proposed in this pull request? This pull request: - Adds following flags to the main mypy configuration: - [`strict_optional`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-strict_optional) - [`no_implicit_optional`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-no_implicit_optional) - [`disallow_untyped_defs`](https://mypy.readthedocs.io/en/stable/config_file.html#confval-disallow_untyped_calls) These flags are enabled only for public API and disabled for tests and internal modules. Additionally, these PR fixes missing annotations. ### Why are the changes needed? Primary reason to propose this changes is to use standard configuration as used by typeshed project. This will allow us to be more strict, especially when interacting with JVM code. See for example https://github.com/apache/spark/pull/29122#pullrequestreview-513112882 Additionally, it will allow us to detect cases where annotations have unintentionally omitted. ### Does this PR introduce _any_ user-facing change? Annotations only. ### How was this patch tested? `dev/lint-python`. Closes #30382 from zero323/SPARK-33457. Authored-by: zero323 <mszymkiewicz@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
252 lines
8.5 KiB
Python
252 lines
8.5 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from typing import overload
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from typing import Any, Dict, Iterable, List, NoReturn, Optional, Tuple, Type, Union
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from pyspark.ml import linalg as newlinalg # noqa: F401
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from pyspark.sql.types import StructType, UserDefinedType
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from numpy import float64, ndarray # type: ignore[import]
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class VectorUDT(UserDefinedType):
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@classmethod
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def sqlType(cls) -> StructType: ...
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@classmethod
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def module(cls) -> str: ...
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@classmethod
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def scalaUDT(cls) -> str: ...
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def serialize(
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self, obj: Vector
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) -> Tuple[int, Optional[int], Optional[List[int]], List[float]]: ...
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def deserialize(self, datum: Any) -> Vector: ...
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def simpleString(self) -> str: ...
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class MatrixUDT(UserDefinedType):
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@classmethod
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def sqlType(cls) -> StructType: ...
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@classmethod
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def module(cls) -> str: ...
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@classmethod
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def scalaUDT(cls) -> str: ...
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def serialize(
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self, obj: Matrix
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) -> Tuple[
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int, int, int, Optional[List[int]], Optional[List[int]], List[float], bool
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]: ...
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def deserialize(self, datum: Any) -> Matrix: ...
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def simpleString(self) -> str: ...
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class Vector:
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__UDT__: VectorUDT
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def toArray(self) -> ndarray: ...
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class DenseVector(Vector):
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array: ndarray
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@overload
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def __init__(self, *elements: float) -> None: ...
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@overload
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def __init__(self, __arr: bytes) -> None: ...
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@overload
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def __init__(self, __arr: Iterable[float]) -> None: ...
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def __reduce__(self) -> Tuple[Type[DenseVector], bytes]: ...
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def numNonzeros(self) -> int: ...
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def norm(self, p: Union[float, str]) -> float64: ...
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def dot(self, other: Iterable[float]) -> float64: ...
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def squared_distance(self, other: Iterable[float]) -> float64: ...
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def toArray(self) -> ndarray: ...
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@property
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def values(self) -> ndarray: ...
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def __getitem__(self, item: int) -> float64: ...
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def __len__(self) -> int: ...
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def __eq__(self, other: Any) -> bool: ...
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def __ne__(self, other: Any) -> bool: ...
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def __hash__(self) -> int: ...
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def __getattr__(self, item: str) -> Any: ...
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def __neg__(self) -> DenseVector: ...
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def __add__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __sub__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __mul__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __div__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __truediv__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __mod__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __radd__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __rsub__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __rmul__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __rdiv__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __rtruediv__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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def __rmod__(self, other: Union[float, Iterable[float]]) -> DenseVector: ...
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class SparseVector(Vector):
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size: int
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indices: ndarray
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values: ndarray
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@overload
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def __init__(self, size: int, *args: Tuple[int, float]) -> None: ...
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@overload
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def __init__(self, size: int, __indices: bytes, __values: bytes) -> None: ...
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@overload
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def __init__(
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self, size: int, __indices: Iterable[int], __values: Iterable[float]
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) -> None: ...
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@overload
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def __init__(self, size: int, __pairs: Iterable[Tuple[int, float]]) -> None: ...
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@overload
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def __init__(self, size: int, __map: Dict[int, float]) -> None: ...
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def numNonzeros(self) -> int: ...
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def norm(self, p: Union[float, str]) -> float64: ...
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def __reduce__(self) -> Tuple[Type[SparseVector], Tuple[int, bytes, bytes]]: ...
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def dot(self, other: Iterable[float]) -> float64: ...
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def squared_distance(self, other: Iterable[float]) -> float64: ...
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def toArray(self) -> ndarray: ...
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def __len__(self) -> int: ...
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def __eq__(self, other: Any) -> bool: ...
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def __getitem__(self, index: int) -> float64: ...
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def __ne__(self, other: Any) -> bool: ...
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def __hash__(self) -> int: ...
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class Vectors:
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@overload
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@staticmethod
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def sparse(size: int, *args: Tuple[int, float]) -> SparseVector: ...
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@overload
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@staticmethod
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def sparse(size: int, __indices: bytes, __values: bytes) -> SparseVector: ...
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@overload
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@staticmethod
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def sparse(
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size: int, __indices: Iterable[int], __values: Iterable[float]
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) -> SparseVector: ...
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@overload
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@staticmethod
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def sparse(size: int, __pairs: Iterable[Tuple[int, float]]) -> SparseVector: ...
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@overload
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@staticmethod
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def sparse(size: int, __map: Dict[int, float]) -> SparseVector: ...
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@overload
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@staticmethod
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def dense(*elements: float) -> DenseVector: ...
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@overload
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@staticmethod
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def dense(__arr: bytes) -> DenseVector: ...
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@overload
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@staticmethod
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def dense(__arr: Iterable[float]) -> DenseVector: ...
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@staticmethod
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def stringify(vector: Vector) -> str: ...
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@staticmethod
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def squared_distance(v1: Vector, v2: Vector) -> float64: ...
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@staticmethod
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def norm(vector: Vector, p: Union[float, str]) -> float64: ...
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@staticmethod
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def zeros(size: int) -> DenseVector: ...
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class Matrix:
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__UDT__: MatrixUDT
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numRows: int
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numCols: int
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isTransposed: bool
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def __init__(
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self, numRows: int, numCols: int, isTransposed: bool = ...
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) -> None: ...
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def toArray(self) -> NoReturn: ...
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class DenseMatrix(Matrix):
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values: Any
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@overload
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def __init__(
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self, numRows: int, numCols: int, values: bytes, isTransposed: bool = ...
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) -> None: ...
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@overload
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def __init__(
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self,
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numRows: int,
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numCols: int,
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values: Iterable[float],
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isTransposed: bool = ...,
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) -> None: ...
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def __reduce__(self) -> Tuple[Type[DenseMatrix], Tuple[int, int, bytes, int]]: ...
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def toArray(self) -> ndarray: ...
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def toSparse(self) -> SparseMatrix: ...
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def __getitem__(self, indices: Tuple[int, int]) -> float64: ...
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def __eq__(self, other: Any) -> bool: ...
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class SparseMatrix(Matrix):
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colPtrs: ndarray
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rowIndices: ndarray
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values: ndarray
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@overload
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def __init__(
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self,
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numRows: int,
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numCols: int,
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colPtrs: bytes,
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rowIndices: bytes,
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values: bytes,
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isTransposed: bool = ...,
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) -> None: ...
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@overload
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def __init__(
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self,
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numRows: int,
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numCols: int,
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colPtrs: Iterable[int],
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rowIndices: Iterable[int],
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values: Iterable[float],
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isTransposed: bool = ...,
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) -> None: ...
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def __reduce__(
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self,
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) -> Tuple[Type[SparseMatrix], Tuple[int, int, bytes, bytes, bytes, int]]: ...
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def __getitem__(self, indices: Tuple[int, int]) -> float64: ...
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def toArray(self) -> ndarray: ...
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def toDense(self) -> DenseMatrix: ...
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def __eq__(self, other: Any) -> bool: ...
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class Matrices:
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@overload
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@staticmethod
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def dense(
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numRows: int, numCols: int, values: bytes, isTransposed: bool = ...
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) -> DenseMatrix: ...
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@overload
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@staticmethod
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def dense(
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numRows: int, numCols: int, values: Iterable[float], isTransposed: bool = ...
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) -> DenseMatrix: ...
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@overload
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@staticmethod
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def sparse(
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numRows: int,
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numCols: int,
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colPtrs: bytes,
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rowIndices: bytes,
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values: bytes,
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isTransposed: bool = ...,
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) -> SparseMatrix: ...
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@overload
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@staticmethod
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def sparse(
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numRows: int,
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numCols: int,
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colPtrs: Iterable[int],
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rowIndices: Iterable[int],
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values: Iterable[float],
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isTransposed: bool = ...,
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) -> SparseMatrix: ...
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