31a16fbb40
### What changes were proposed in this pull request? This PR proposes migration of [`pyspark-stubs`](https://github.com/zero323/pyspark-stubs) into Spark codebase. ### Why are the changes needed? ### Does this PR introduce _any_ user-facing change? Yes. This PR adds type annotations directly to Spark source. This can impact interaction with development tools for users, which haven't used `pyspark-stubs`. ### How was this patch tested? - [x] MyPy tests of the PySpark source ``` mypy --no-incremental --config python/mypy.ini python/pyspark ``` - [x] MyPy tests of Spark examples ``` MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming ``` - [x] Existing Flake8 linter - [x] Existing unit tests Tested against: - `mypy==0.790+dev.e959952d9001e9713d329a2f9b196705b028f894` - `mypy==0.782` Closes #29591 from zero323/SPARK-32681. Authored-by: zero323 <mszymkiewicz@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
104 lines
3.6 KiB
Python
104 lines
3.6 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 (
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Callable,
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Generic,
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Iterable,
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List,
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Optional,
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Tuple,
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)
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from pyspark.ml._typing import M, P, T, ParamMap
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import _thread
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import abc
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from abc import abstractmethod
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from pyspark import since as since # noqa: F401
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from pyspark.ml.common import inherit_doc as inherit_doc # noqa: F401
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from pyspark.ml.param.shared import (
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HasFeaturesCol as HasFeaturesCol,
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HasInputCol as HasInputCol,
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HasLabelCol as HasLabelCol,
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HasOutputCol as HasOutputCol,
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HasPredictionCol as HasPredictionCol,
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Params as Params,
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)
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from pyspark.sql.functions import udf as udf # noqa: F401
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from pyspark.sql.types import ( # noqa: F401
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DataType,
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StructField as StructField,
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StructType as StructType,
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)
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from pyspark.sql.dataframe import DataFrame
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class _FitMultipleIterator:
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fitSingleModel: Callable[[int], Transformer]
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numModel: int
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counter: int = ...
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lock: _thread.LockType
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def __init__(
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self, fitSingleModel: Callable[[int], Transformer], numModels: int
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) -> None: ...
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def __iter__(self) -> _FitMultipleIterator: ...
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def __next__(self) -> Tuple[int, Transformer]: ...
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def next(self) -> Tuple[int, Transformer]: ...
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class Estimator(Generic[M], Params, metaclass=abc.ABCMeta):
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@overload
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def fit(self, dataset: DataFrame, params: Optional[ParamMap] = ...) -> M: ...
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@overload
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def fit(self, dataset: DataFrame, params: List[ParamMap]) -> List[M]: ...
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def fitMultiple(
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self, dataset: DataFrame, params: List[ParamMap]
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) -> Iterable[Tuple[int, M]]: ...
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class Transformer(Params, metaclass=abc.ABCMeta):
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def transform(
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self, dataset: DataFrame, params: Optional[ParamMap] = ...
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) -> DataFrame: ...
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class Model(Transformer, metaclass=abc.ABCMeta): ...
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class UnaryTransformer(HasInputCol, HasOutputCol, Transformer, metaclass=abc.ABCMeta):
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def createTransformFunc(self) -> Callable: ...
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def outputDataType(self) -> DataType: ...
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def validateInputType(self, inputType: DataType) -> None: ...
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def transformSchema(self, schema: StructType) -> StructType: ...
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def setInputCol(self: M, value: str) -> M: ...
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def setOutputCol(self: M, value: str) -> M: ...
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class _PredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol): ...
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class Predictor(Estimator[M], _PredictorParams, metaclass=abc.ABCMeta):
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def setLabelCol(self: P, value: str) -> P: ...
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def setFeaturesCol(self: P, value: str) -> P: ...
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def setPredictionCol(self: P, value: str) -> P: ...
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class PredictionModel(Generic[T], Model, _PredictorParams, metaclass=abc.ABCMeta):
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def setFeaturesCol(self: M, value: str) -> M: ...
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def setPredictionCol(self: M, value: str) -> M: ...
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@property
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@abc.abstractmethod
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def numFeatures(self) -> int: ...
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@abstractmethod
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def predict(self, value: T) -> float: ...
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