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>
438 lines
14 KiB
Python
438 lines
14 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 Any, List, Optional
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from pyspark.ml.linalg import Matrix, Vector
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from pyspark.ml.util import (
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GeneralJavaMLWritable,
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HasTrainingSummary,
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JavaMLReadable,
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JavaMLWritable,
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)
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from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams, JavaWrapper
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from pyspark.ml.param.shared import (
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HasAggregationDepth,
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HasCheckpointInterval,
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HasDistanceMeasure,
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HasFeaturesCol,
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HasMaxIter,
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HasPredictionCol,
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HasProbabilityCol,
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HasSeed,
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HasTol,
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HasWeightCol,
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)
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from pyspark.ml.param import Param
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from pyspark.ml.stat import MultivariateGaussian
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from pyspark.sql.dataframe import DataFrame
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from numpy import ndarray # type: ignore[import]
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class ClusteringSummary(JavaWrapper):
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@property
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def predictionCol(self) -> str: ...
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@property
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def predictions(self) -> DataFrame: ...
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@property
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def featuresCol(self) -> str: ...
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@property
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def k(self) -> int: ...
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@property
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def cluster(self) -> DataFrame: ...
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@property
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def clusterSizes(self) -> List[int]: ...
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@property
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def numIter(self) -> int: ...
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class _GaussianMixtureParams(
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HasMaxIter,
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HasFeaturesCol,
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HasSeed,
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HasPredictionCol,
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HasProbabilityCol,
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HasTol,
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HasAggregationDepth,
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HasWeightCol,
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):
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k: Param[int]
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def __init__(self, *args: Any): ...
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def getK(self) -> int: ...
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class GaussianMixtureModel(
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JavaModel,
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_GaussianMixtureParams,
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JavaMLWritable,
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JavaMLReadable[GaussianMixtureModel],
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HasTrainingSummary[GaussianMixtureSummary],
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):
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def setFeaturesCol(self, value: str) -> GaussianMixtureModel: ...
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def setPredictionCol(self, value: str) -> GaussianMixtureModel: ...
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def setProbabilityCol(self, value: str) -> GaussianMixtureModel: ...
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@property
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def weights(self) -> List[float]: ...
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@property
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def gaussians(self) -> List[MultivariateGaussian]: ...
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@property
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def gaussiansDF(self) -> DataFrame: ...
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@property
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def summary(self) -> GaussianMixtureSummary: ...
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def predict(self, value: Vector) -> int: ...
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def predictProbability(self, value: Vector) -> Vector: ...
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class GaussianMixture(
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JavaEstimator[GaussianMixtureModel],
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_GaussianMixtureParams,
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JavaMLWritable,
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JavaMLReadable[GaussianMixture],
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):
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def __init__(
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self,
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*,
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featuresCol: str = ...,
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predictionCol: str = ...,
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k: int = ...,
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probabilityCol: str = ...,
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tol: float = ...,
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maxIter: int = ...,
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seed: Optional[int] = ...,
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aggregationDepth: int = ...,
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weightCol: Optional[str] = ...
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) -> None: ...
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def setParams(
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self,
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*,
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featuresCol: str = ...,
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predictionCol: str = ...,
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k: int = ...,
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probabilityCol: str = ...,
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tol: float = ...,
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maxIter: int = ...,
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seed: Optional[int] = ...,
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aggregationDepth: int = ...,
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weightCol: Optional[str] = ...
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) -> GaussianMixture: ...
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def setK(self, value: int) -> GaussianMixture: ...
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def setMaxIter(self, value: int) -> GaussianMixture: ...
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def setFeaturesCol(self, value: str) -> GaussianMixture: ...
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def setPredictionCol(self, value: str) -> GaussianMixture: ...
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def setProbabilityCol(self, value: str) -> GaussianMixture: ...
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def setWeightCol(self, value: str) -> GaussianMixture: ...
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def setSeed(self, value: int) -> GaussianMixture: ...
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def setTol(self, value: float) -> GaussianMixture: ...
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def setAggregationDepth(self, value: int) -> GaussianMixture: ...
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class GaussianMixtureSummary(ClusteringSummary):
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@property
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def probabilityCol(self) -> str: ...
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@property
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def probability(self) -> DataFrame: ...
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@property
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def logLikelihood(self) -> float: ...
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class KMeansSummary(ClusteringSummary):
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def trainingCost(self) -> float: ...
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class _KMeansParams(
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HasMaxIter,
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HasFeaturesCol,
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HasSeed,
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HasPredictionCol,
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HasTol,
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HasDistanceMeasure,
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HasWeightCol,
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):
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k: Param[int]
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initMode: Param[str]
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initSteps: Param[int]
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def __init__(self, *args: Any): ...
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def getK(self) -> int: ...
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def getInitMode(self) -> str: ...
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def getInitSteps(self) -> int: ...
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class KMeansModel(
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JavaModel,
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_KMeansParams,
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GeneralJavaMLWritable,
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JavaMLReadable[KMeansModel],
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HasTrainingSummary[KMeansSummary],
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):
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def setFeaturesCol(self, value: str) -> KMeansModel: ...
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def setPredictionCol(self, value: str) -> KMeansModel: ...
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def clusterCenters(self) -> List[ndarray]: ...
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@property
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def summary(self) -> KMeansSummary: ...
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def predict(self, value: Vector) -> int: ...
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class KMeans(
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JavaEstimator[KMeansModel], _KMeansParams, JavaMLWritable, JavaMLReadable[KMeans]
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):
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def __init__(
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self,
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*,
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featuresCol: str = ...,
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predictionCol: str = ...,
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k: int = ...,
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initMode: str = ...,
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initSteps: int = ...,
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tol: float = ...,
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maxIter: int = ...,
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seed: Optional[int] = ...,
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distanceMeasure: str = ...,
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weightCol: Optional[str] = ...
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) -> None: ...
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def setParams(
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self,
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*,
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featuresCol: str = ...,
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predictionCol: str = ...,
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k: int = ...,
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initMode: str = ...,
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initSteps: int = ...,
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tol: float = ...,
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maxIter: int = ...,
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seed: Optional[int] = ...,
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distanceMeasure: str = ...,
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weightCol: Optional[str] = ...
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) -> KMeans: ...
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def setK(self, value: int) -> KMeans: ...
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def setInitMode(self, value: str) -> KMeans: ...
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def setInitSteps(self, value: int) -> KMeans: ...
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def setDistanceMeasure(self, value: str) -> KMeans: ...
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def setMaxIter(self, value: int) -> KMeans: ...
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def setFeaturesCol(self, value: str) -> KMeans: ...
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def setPredictionCol(self, value: str) -> KMeans: ...
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def setSeed(self, value: int) -> KMeans: ...
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def setTol(self, value: float) -> KMeans: ...
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def setWeightCol(self, value: str) -> KMeans: ...
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class _BisectingKMeansParams(
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HasMaxIter,
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HasFeaturesCol,
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HasSeed,
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HasPredictionCol,
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HasDistanceMeasure,
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HasWeightCol,
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):
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k: Param[int]
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minDivisibleClusterSize: Param[float]
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def __init__(self, *args: Any): ...
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def getK(self) -> int: ...
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def getMinDivisibleClusterSize(self) -> float: ...
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class BisectingKMeansModel(
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JavaModel,
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_BisectingKMeansParams,
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JavaMLWritable,
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JavaMLReadable[BisectingKMeansModel],
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HasTrainingSummary[BisectingKMeansSummary],
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):
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def setFeaturesCol(self, value: str) -> BisectingKMeansModel: ...
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def setPredictionCol(self, value: str) -> BisectingKMeansModel: ...
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def clusterCenters(self) -> List[ndarray]: ...
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def computeCost(self, dataset: DataFrame) -> float: ...
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@property
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def summary(self) -> BisectingKMeansSummary: ...
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def predict(self, value: Vector) -> int: ...
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class BisectingKMeans(
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JavaEstimator[BisectingKMeansModel],
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_BisectingKMeansParams,
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JavaMLWritable,
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JavaMLReadable[BisectingKMeans],
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):
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def __init__(
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self,
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*,
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featuresCol: str = ...,
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predictionCol: str = ...,
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maxIter: int = ...,
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seed: Optional[int] = ...,
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k: int = ...,
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minDivisibleClusterSize: float = ...,
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distanceMeasure: str = ...,
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weightCol: Optional[str] = ...
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) -> None: ...
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def setParams(
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self,
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*,
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featuresCol: str = ...,
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predictionCol: str = ...,
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maxIter: int = ...,
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seed: Optional[int] = ...,
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k: int = ...,
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minDivisibleClusterSize: float = ...,
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distanceMeasure: str = ...,
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weightCol: Optional[str] = ...
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) -> BisectingKMeans: ...
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def setK(self, value: int) -> BisectingKMeans: ...
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def setMinDivisibleClusterSize(self, value: float) -> BisectingKMeans: ...
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def setDistanceMeasure(self, value: str) -> BisectingKMeans: ...
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def setMaxIter(self, value: int) -> BisectingKMeans: ...
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def setFeaturesCol(self, value: str) -> BisectingKMeans: ...
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def setPredictionCol(self, value: str) -> BisectingKMeans: ...
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def setSeed(self, value: int) -> BisectingKMeans: ...
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def setWeightCol(self, value: str) -> BisectingKMeans: ...
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class BisectingKMeansSummary(ClusteringSummary):
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@property
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def trainingCost(self) -> float: ...
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class _LDAParams(HasMaxIter, HasFeaturesCol, HasSeed, HasCheckpointInterval):
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k: Param[int]
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optimizer: Param[str]
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learningOffset: Param[float]
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learningDecay: Param[float]
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subsamplingRate: Param[float]
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optimizeDocConcentration: Param[bool]
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docConcentration: Param[List[float]]
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topicConcentration: Param[float]
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topicDistributionCol: Param[str]
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keepLastCheckpoint: Param[bool]
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def __init__(self, *args: Any): ...
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def setK(self, value: int) -> LDA: ...
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def getOptimizer(self) -> str: ...
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def getLearningOffset(self) -> float: ...
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def getLearningDecay(self) -> float: ...
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def getSubsamplingRate(self) -> float: ...
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def getOptimizeDocConcentration(self) -> bool: ...
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def getDocConcentration(self) -> List[float]: ...
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def getTopicConcentration(self) -> float: ...
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def getTopicDistributionCol(self) -> str: ...
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def getKeepLastCheckpoint(self) -> bool: ...
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class LDAModel(JavaModel, _LDAParams):
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def setFeaturesCol(self, value: str) -> LDAModel: ...
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def setSeed(self, value: int) -> LDAModel: ...
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def setTopicDistributionCol(self, value: str) -> LDAModel: ...
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def isDistributed(self) -> bool: ...
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def vocabSize(self) -> int: ...
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def topicsMatrix(self) -> Matrix: ...
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def logLikelihood(self, dataset: DataFrame) -> float: ...
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def logPerplexity(self, dataset: DataFrame) -> float: ...
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def describeTopics(self, maxTermsPerTopic: int = ...) -> DataFrame: ...
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def estimatedDocConcentration(self) -> Vector: ...
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class DistributedLDAModel(
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LDAModel, JavaMLReadable[DistributedLDAModel], JavaMLWritable
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):
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def toLocal(self) -> LDAModel: ...
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def trainingLogLikelihood(self) -> float: ...
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def logPrior(self) -> float: ...
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def getCheckpointFiles(self) -> List[str]: ...
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class LocalLDAModel(LDAModel, JavaMLReadable[LocalLDAModel], JavaMLWritable): ...
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class LDA(JavaEstimator[LDAModel], _LDAParams, JavaMLReadable[LDA], JavaMLWritable):
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def __init__(
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self,
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*,
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featuresCol: str = ...,
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maxIter: int = ...,
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seed: Optional[int] = ...,
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checkpointInterval: int = ...,
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k: int = ...,
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optimizer: str = ...,
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learningOffset: float = ...,
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learningDecay: float = ...,
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subsamplingRate: float = ...,
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optimizeDocConcentration: bool = ...,
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docConcentration: Optional[List[float]] = ...,
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topicConcentration: Optional[float] = ...,
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topicDistributionCol: str = ...,
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keepLastCheckpoint: bool = ...
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) -> None: ...
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def setParams(
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self,
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*,
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featuresCol: str = ...,
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maxIter: int = ...,
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seed: Optional[int] = ...,
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checkpointInterval: int = ...,
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k: int = ...,
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optimizer: str = ...,
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learningOffset: float = ...,
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learningDecay: float = ...,
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subsamplingRate: float = ...,
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optimizeDocConcentration: bool = ...,
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docConcentration: Optional[List[float]] = ...,
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topicConcentration: Optional[float] = ...,
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topicDistributionCol: str = ...,
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keepLastCheckpoint: bool = ...
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) -> LDA: ...
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def setCheckpointInterval(self, value: int) -> LDA: ...
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def setSeed(self, value: int) -> LDA: ...
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def setK(self, value: int) -> LDA: ...
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def setOptimizer(self, value: str) -> LDA: ...
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def setLearningOffset(self, value: float) -> LDA: ...
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def setLearningDecay(self, value: float) -> LDA: ...
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def setSubsamplingRate(self, value: float) -> LDA: ...
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def setOptimizeDocConcentration(self, value: bool) -> LDA: ...
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def setDocConcentration(self, value: List[float]) -> LDA: ...
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def setTopicConcentration(self, value: float) -> LDA: ...
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def setTopicDistributionCol(self, value: str) -> LDA: ...
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def setKeepLastCheckpoint(self, value: bool) -> LDA: ...
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def setMaxIter(self, value: int) -> LDA: ...
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def setFeaturesCol(self, value: str) -> LDA: ...
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class _PowerIterationClusteringParams(HasMaxIter, HasWeightCol):
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k: Param[int]
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initMode: Param[str]
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srcCol: Param[str]
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dstCol: Param[str]
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def __init__(self, *args: Any): ...
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def getK(self) -> int: ...
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def getInitMode(self) -> str: ...
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def getSrcCol(self) -> str: ...
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def getDstCol(self) -> str: ...
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class PowerIterationClustering(
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_PowerIterationClusteringParams,
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JavaParams,
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JavaMLReadable[PowerIterationClustering],
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JavaMLWritable,
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):
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def __init__(
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self,
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*,
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k: int = ...,
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maxIter: int = ...,
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initMode: str = ...,
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srcCol: str = ...,
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dstCol: str = ...,
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weightCol: Optional[str] = ...
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) -> None: ...
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def setParams(
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self,
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*,
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k: int = ...,
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maxIter: int = ...,
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initMode: str = ...,
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srcCol: str = ...,
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dstCol: str = ...,
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weightCol: Optional[str] = ...
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) -> PowerIterationClustering: ...
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def setK(self, value: int) -> PowerIterationClustering: ...
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def setInitMode(self, value: str) -> PowerIterationClustering: ...
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def setSrcCol(self, value: str) -> str: ...
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def setDstCol(self, value: str) -> PowerIterationClustering: ...
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def setMaxIter(self, value: int) -> PowerIterationClustering: ...
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def setWeightCol(self, value: str) -> PowerIterationClustering: ...
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def assignClusters(self, dataset: DataFrame) -> DataFrame: ...
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