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SPARK-29656 Add aggregationDepth to GeneralizedLinearRegression and GaussianMixture #305

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Jan 14, 2020
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SPARK-29656 Add aggregationDepth to GeneralizedLinearRegression and GaussianMixture #305

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8 changes: 4 additions & 4 deletions third_party/3/pyspark/ml/clustering.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ class ClusteringSummary(JavaWrapper):
@property
def numIter(self) -> int: ...

class _GaussianMixtureParams(HasMaxIter, HasFeaturesCol, HasSeed, HasPredictionCol, HasProbabilityCol, HasTol):
class _GaussianMixtureParams(HasMaxIter, HasFeaturesCol, HasSeed, HasPredictionCol, HasProbabilityCol, HasTol, HasAggregationDepth):
k: Param[int]
def getK(self) -> int: ...

Expand All @@ -45,8 +45,8 @@ class GaussianMixtureModel(JavaModel, _GaussianMixtureParams, JavaMLWritable, Ja
def predictProbability(self, value: Vector) -> Vector: ...

class GaussianMixture(JavaEstimator[GaussianMixtureModel], _GaussianMixtureParams, JavaMLWritable, JavaMLReadable[GaussianMixture]):
def __init__(self, *, featuresCol: str = ..., predictionCol: str = ..., k: int = ..., probabilityCol: str = ..., tol: float = ..., maxIter: int = ..., seed: Optional[int] = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., predictionCol: str = ..., k: int = ..., probabilityCol: str = ..., tol: float = ..., maxIter: int = ..., seed: Optional[int] = ...) -> GaussianMixture: ...
def __init__(self, *, featuresCol: str = ..., predictionCol: str = ..., k: int = ..., probabilityCol: str = ..., tol: float = ..., maxIter: int = ..., seed: Optional[int] = ..., aggregationDepth: int = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., predictionCol: str = ..., k: int = ..., probabilityCol: str = ..., tol: float = ..., maxIter: int = ..., seed: Optional[int] = ..., aggregationDepth: int = ...) -> GaussianMixture: ...
def setK(self, value: int) -> GaussianMixture: ...
def setMaxIter(self, value: int) -> GaussianMixture: ...
def setFeaturesCol(self, value: str) -> GaussianMixture: ...
Expand Down Expand Up @@ -206,5 +206,5 @@ class PowerIterationClustering(_PowerIterationClusteringParams, JavaParams, Java
def setSrcCol(self, value: str) -> str: ...
def setDstCol(self, value: str) -> PowerIterationClustering: ...
def setMaxIter(self, value: int) -> PowerIterationClustering: ...
def setWeightCol(self, value: str) -> PowerIterationClustering: ...
def setWeightCol(self, value: str) -> PowerIterationClustering: ...
def assignClusters(self, dataset: DataFrame) -> DataFrame: ...
6 changes: 3 additions & 3 deletions third_party/3/pyspark/ml/regression.pyi
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Expand Up @@ -220,7 +220,7 @@ class AFTSurvivalRegressionModel(JavaModel, _AFTSurvivalRegressionParams, JavaML
def predictQuantiles(self, features: Vector) -> Vector: ...
def predict(self, features: Vector) -> float: ...

class _GeneralizedLinearRegressionParams(_JavaPredictorParams, HasFitIntercept, HasMaxIter, HasTol, HasRegParam, HasWeightCol, HasSolver):
class _GeneralizedLinearRegressionParams(_JavaPredictorParams, HasFitIntercept, HasMaxIter, HasTol, HasRegParam, HasWeightCol, HasSolver, HasAggregationDepth):
family: Param[str]
link: Param[str]
linkPredictionCol: Param[str]
Expand All @@ -236,8 +236,8 @@ class _GeneralizedLinearRegressionParams(_JavaPredictorParams, HasFitIntercept,
def getOffsetCol(self) -> str: ...

class GeneralizedLinearRegression(JavaPredictor[GeneralizedLinearRegressionModel], _GeneralizedLinearRegressionParams, JavaMLWritable, JavaMLReadable[GeneralizedLinearRegression]):
def __init__(self, *, labelCol: str = ..., featuresCol: str = ..., predictionCol: str = ..., family: str = ..., link: Optional[str] = ..., fitIntercept: bool = ..., maxIter: int = ..., tol: float = ..., regParam: float = ..., weightCol: Optional[str] = ..., solver: str = ..., linkPredictionCol: Optional[str] = ..., variancePower: float = ..., linkPower: Optional[float] = ..., offsetCol: Optional[str] = ...) -> None: ...
def setParams(self, *, labelCol: str = ..., featuresCol: str = ..., predictionCol: str = ..., family: str = ..., link: Optional[str] = ..., fitIntercept: bool = ..., maxIter: int = ..., tol: float = ..., regParam: float = ..., weightCol: Optional[str] = ..., solver: str = ..., linkPredictionCol: Optional[str] = ..., variancePower: float = ..., linkPower: Optional[float] = ..., offsetCol: Optional[str] = ...) -> GeneralizedLinearRegression: ...
def __init__(self, *, labelCol: str = ..., featuresCol: str = ..., predictionCol: str = ..., family: str = ..., link: Optional[str] = ..., fitIntercept: bool = ..., maxIter: int = ..., tol: float = ..., regParam: float = ..., weightCol: Optional[str] = ..., solver: str = ..., linkPredictionCol: Optional[str] = ..., variancePower: float = ..., linkPower: Optional[float] = ..., offsetCol: Optional[str] = ..., aggregationDepth: int = ...) -> None: ...
def setParams(self, *, labelCol: str = ..., featuresCol: str = ..., predictionCol: str = ..., family: str = ..., link: Optional[str] = ..., fitIntercept: bool = ..., maxIter: int = ..., tol: float = ..., regParam: float = ..., weightCol: Optional[str] = ..., solver: str = ..., linkPredictionCol: Optional[str] = ..., variancePower: float = ..., linkPower: Optional[float] = ..., offsetCol: Optional[str] = ..., aggregationDepth: int = ...) -> GeneralizedLinearRegression: ...
def setFamily(self, value: str) -> GeneralizedLinearRegression: ...
def setLinkPredictionCol(self, value: str) -> GeneralizedLinearRegression: ...
def setLink(self, value: str) -> GeneralizedLinearRegression: ...
Expand Down