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>
113 lines
3.8 KiB
Python
113 lines
3.8 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 List, Sequence
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from pyspark.ml._typing import P, T
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from pyspark.ml.linalg import Vector
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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 import Param, Params as Params
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from pyspark.ml.param.shared import ( # noqa: F401
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HasCheckpointInterval as HasCheckpointInterval,
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HasMaxIter as HasMaxIter,
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HasSeed as HasSeed,
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HasStepSize as HasStepSize,
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HasValidationIndicatorCol as HasValidationIndicatorCol,
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HasWeightCol as HasWeightCol,
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Param as Param,
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TypeConverters as TypeConverters,
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)
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from pyspark.ml.wrapper import JavaPredictionModel as JavaPredictionModel
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class _DecisionTreeModel(JavaPredictionModel[T]):
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@property
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def numNodes(self) -> int: ...
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@property
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def depth(self) -> int: ...
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@property
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def toDebugString(self) -> str: ...
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def predictLeaf(self, value: Vector) -> float: ...
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class _DecisionTreeParams(HasCheckpointInterval, HasSeed, HasWeightCol):
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leafCol: Param[str]
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maxDepth: Param[int]
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maxBins: Param[int]
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minInstancesPerNode: Param[int]
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minWeightFractionPerNode: Param[float]
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minInfoGain: Param[float]
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maxMemoryInMB: Param[int]
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cacheNodeIds: Param[bool]
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def __init__(self) -> None: ...
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def setLeafCol(self: P, value: str) -> P: ...
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def getLeafCol(self) -> str: ...
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def getMaxDepth(self) -> int: ...
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def getMaxBins(self) -> int: ...
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def getMinInstancesPerNode(self) -> int: ...
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def getMinInfoGain(self) -> float: ...
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def getMaxMemoryInMB(self) -> int: ...
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def getCacheNodeIds(self) -> bool: ...
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class _TreeEnsembleModel(JavaPredictionModel[T]):
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@property
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def trees(self) -> Sequence[_DecisionTreeModel]: ...
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@property
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def getNumTrees(self) -> int: ...
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@property
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def treeWeights(self) -> List[float]: ...
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@property
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def totalNumNodes(self) -> int: ...
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@property
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def toDebugString(self) -> str: ...
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class _TreeEnsembleParams(_DecisionTreeParams):
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subsamplingRate: Param[float]
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supportedFeatureSubsetStrategies: List[str]
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featureSubsetStrategy: Param[str]
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def __init__(self) -> None: ...
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def getSubsamplingRate(self) -> float: ...
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def getFeatureSubsetStrategy(self) -> str: ...
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class _RandomForestParams(_TreeEnsembleParams):
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numTrees: Param[int]
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bootstrap: Param[bool]
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def __init__(self) -> None: ...
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def getNumTrees(self) -> int: ...
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def getBootstrap(self) -> bool: ...
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class _GBTParams(
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_TreeEnsembleParams, HasMaxIter, HasStepSize, HasValidationIndicatorCol
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):
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stepSize: Param[float]
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validationTol: Param[float]
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def getValidationTol(self) -> float: ...
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class _HasVarianceImpurity(Params):
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supportedImpurities: List[str]
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impurity: Param[str]
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def __init__(self) -> None: ...
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def getImpurity(self) -> str: ...
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class _TreeClassifierParams(Params):
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supportedImpurities: List[str]
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impurity: Param[str]
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def __init__(self) -> None: ...
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def getImpurity(self) -> str: ...
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class _TreeRegressorParams(_HasVarianceImpurity): ...
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