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>
58 lines
1.9 KiB
Python
58 lines
1.9 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 Generic, List, TypeVar
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from pyspark.context import SparkContext
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from pyspark.rdd import RDD
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from pyspark.mllib.common import JavaModelWrapper
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from pyspark.mllib.util import JavaSaveable, JavaLoader
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T = TypeVar("T")
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class FPGrowthModel(
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JavaModelWrapper, JavaSaveable, JavaLoader[FPGrowthModel], Generic[T]
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):
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def freqItemsets(self) -> RDD[FPGrowth.FreqItemset[T]]: ...
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@classmethod
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def load(cls, sc: SparkContext, path: str) -> FPGrowthModel: ...
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class FPGrowth:
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@classmethod
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def train(
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cls, data: RDD[List[T]], minSupport: float = ..., numPartitions: int = ...
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) -> FPGrowthModel[T]: ...
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class FreqItemset(Generic[T]):
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items = ... # List[T]
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freq = ... # int
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class PrefixSpanModel(JavaModelWrapper, Generic[T]):
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def freqSequences(self) -> RDD[PrefixSpan.FreqSequence[T]]: ...
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class PrefixSpan:
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@classmethod
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def train(
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cls,
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data: RDD[List[List[T]]],
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minSupport: float = ...,
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maxPatternLength: int = ...,
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maxLocalProjDBSize: int = ...,
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) -> PrefixSpanModel[T]: ...
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class FreqSequence(tuple, Generic[T]):
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sequence: List[T]
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freq: int
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