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SPARK-29381 #292

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34b8d80
Update ml.wrapper
zero323 Jan 11, 2020
1911328
Update ml.classification
zero323 Jan 11, 2020
5b2ea56
Update ml.classification, follow-up
zero323 Jan 11, 2020
1cff875
Update ml.regression annotations, follow-up
zero323 Jan 11, 2020
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79 changes: 44 additions & 35 deletions third_party/3/pyspark/ml/classification.pyi
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Expand Up @@ -10,64 +10,69 @@ from pyspark.ml.param.shared import *
from pyspark.ml.tree import _DecisionTreeModel, _DecisionTreeParams, _TreeEnsembleModel, _RandomForestParams, _GBTParams, _HasVarianceImpurity, _TreeClassifierParams, _TreeEnsembleParams
from pyspark.ml.regression import DecisionTreeRegressionModel
from pyspark.ml.util import *
from pyspark.ml.wrapper import JavaPredictionModel, JavaPredictor, JavaPredictorParams, JavaWrapper, JavaTransformer
from pyspark.ml.wrapper import JavaPredictionModel, JavaPredictor, _JavaPredictorParams, JavaWrapper, JavaTransformer
from pyspark.sql.dataframe import DataFrame

class JavaClassifierParams(HasRawPredictionCol, JavaPredictorParams): ...
class _JavaClassifierParams(HasRawPredictionCol, _JavaPredictorParams): ...

class JavaClassifier(JavaPredictor[JM], JavaClassifierParams, metaclass=abc.ABCMeta):
class JavaClassifier(JavaPredictor[JM], _JavaClassifierParams, metaclass=abc.ABCMeta):
def setRawPredictionCol(self: P, value: str) -> P: ...

class JavaClassificationModel(JavaPredictionModel[T], JavaClassifierParams):
class JavaClassificationModel(JavaPredictionModel[T], _JavaClassifierParams):
def setRawPredictionCol(self: P, value: str) -> P: ...
@property
def numClasses(self) -> int: ...

class JavaProbabilisticClassifierParams(HasProbabilityCol, HasThresholds, JavaClassifierParams): ...
class _JavaProbabilisticClassifierParams(HasProbabilityCol, HasThresholds, _JavaClassifierParams): ...

class JavaProbabilisticClassifier(JavaClassifier[JM], JavaProbabilisticClassifierParams, metaclass=abc.ABCMeta):
class JavaProbabilisticClassifier(JavaClassifier[JM], _JavaProbabilisticClassifierParams, metaclass=abc.ABCMeta):
def setProbabilityCol(self: P, value: str) -> P: ...
def setThresholds(self: P, value: List[float]) -> P: ...

class JavaProbabilisticClassificationModel(JavaClassificationModel[T], JavaProbabilisticClassifierParams):
class JavaProbabilisticClassificationModel(JavaClassificationModel[T], _JavaProbabilisticClassifierParams):
def setProbabilityCol(self: P, value: str) -> P: ...
def setThresholds(self, value: List[float]) -> P: ...

class LinearSVC(JavaClassifier[LinearSVCModel], HasMaxIter, HasRegParam, HasTol, HasFitIntercept, HasStandardization, HasWeightCol, HasAggregationDepth, HasThreshold, JavaMLWritable, JavaMLReadable[LinearSVC]):
class _LinearSVCParams(_JavaClassifierParams, HasRegParam, HasMaxIter, HasFitIntercept, HasTol, HasStandardization, HasWeightCol, HasAggregationDepth, HasThreshold):
threshold: Param[float]

class LinearSVC(JavaClassifier[LinearSVCModel], _LinearSVCParams, JavaMLWritable, JavaMLReadable[LinearSVC]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., tol: float = ..., rawPredictionCol: str = ..., fitIntercept: bool = ..., standardization: bool = ..., threshold: float = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., tol: float = ..., rawPredictionCol: str = ..., fitIntercept: bool = ..., standardization: bool = ..., threshold: float = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ...) -> LinearSVC: ...

class LinearSVCModel(JavaClassificationModel[Vector], JavaMLWritable, JavaMLReadable[LinearSVCModel]):
class LinearSVCModel(JavaClassificationModel[Vector], _LinearSVCParams, JavaMLWritable, JavaMLReadable[LinearSVCModel]):
@property
def coefficients(self) -> Vector: ...
@property
def intercept(self) -> float: ...

class LogisticRegression(JavaProbabilisticClassifier[LogisticRegressionModel], HasMaxIter, HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds, HasWeightCol, HasAggregationDepth, JavaMLWritable, JavaMLReadable[LogisticRegression]):
class _LogisticRegressionParams(_JavaProbabilisticClassifierParams, HasRegParam, HasElasticNetParam, HasMaxIter, HasFitIntercept, HasTol, HasStandardization, HasWeightCol, HasAggregationDepth, HasThreshold):
threshold: Param[float]
family: Param[str]
lowerBoundsOnCoefficients: Param[Matrix]
upperBoundsOnCoefficients: Param[Matrix]
lowerBoundsOnIntercepts: Param[Vector]
upperBoundsOnIntercepts: Param[Vector]
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., threshold: float = ..., thresholds: Optional[List[float]] = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., standardization: bool = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., family: str = ..., lowerBoundsOnCoefficients: Optional[Matrix] = ..., upperBoundsOnCoefficients: Optional[Matrix] = ..., lowerBoundsOnIntercepts: Optional[Vector] = ..., upperBoundsOnIntercepts: Optional[Vector] = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., threshold: float = ..., thresholds: Optional[List[float]] = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., standardization: bool = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., family: str = ..., lowerBoundsOnCoefficients: Optional[Matrix] = ..., upperBoundsOnCoefficients: Optional[Matrix] = ..., lowerBoundsOnIntercepts: Optional[Vector] = ..., upperBoundsOnIntercepts: Optional[Vector] = ...) -> LogisticRegression: ...
def setThreshold(self, value: float) -> LogisticRegression: ...
def setThreshold(self: P, value: float) -> P: ...
def getThreshold(self) -> float: ...
def setThresholds(self, value: List[float]) -> LogisticRegression: ...
def setThresholds(self: P, value: List[float]) -> P: ...
def getThresholds(self) -> List[float]: ...
def setFamily(self, value: str) -> LogisticRegression: ...
def getFamily(self) -> str: ...
def setLowerBoundsOnCoefficients(self, value: Matrix) -> LogisticRegression: ...
def getLowerBoundsOnCoefficients(self) -> Matrix : ...
def setUpperBoundsOnCoefficients(self, value: Matrix) -> LogisticRegression: ...
def getUpperBoundsOnCoefficients(self) -> Matrix: ...
def setLowerBoundsOnIntercepts(self, value: Vector) -> LogisticRegression: ...
def getLowerBoundsOnIntercepts(self) -> Vector: ...
def setUpperBoundsOnIntercepts(self, value: Vector) -> LogisticRegression: ...
def getUpperBoundsOnIntercepts(self) -> Vector: ...

class LogisticRegressionModel(JavaProbabilisticClassificationModel[Vector], JavaMLWritable, JavaMLReadable[LogisticRegressionModel], HasTrainingSummary[LogisticRegressionTrainingSummary]):
class LogisticRegression(JavaProbabilisticClassifier[LogisticRegressionModel], _LogisticRegressionParams, JavaMLWritable, JavaMLReadable[LogisticRegression]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., threshold: float = ..., thresholds: Optional[List[float]] = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., standardization: bool = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., family: str = ..., lowerBoundsOnCoefficients: Optional[Matrix] = ..., upperBoundsOnCoefficients: Optional[Matrix] = ..., lowerBoundsOnIntercepts: Optional[Vector] = ..., upperBoundsOnIntercepts: Optional[Vector] = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., threshold: float = ..., thresholds: Optional[List[float]] = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., standardization: bool = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., family: str = ..., lowerBoundsOnCoefficients: Optional[Matrix] = ..., upperBoundsOnCoefficients: Optional[Matrix] = ..., lowerBoundsOnIntercepts: Optional[Vector] = ..., upperBoundsOnIntercepts: Optional[Vector] = ...) -> LogisticRegression: ...
def setFamily(self, value: str) -> LogisticRegression: ...
def setLowerBoundsOnCoefficients(self, value: Matrix) -> LogisticRegression: ...
def setUpperBoundsOnCoefficients(self, value: Matrix) -> LogisticRegression: ...
def setLowerBoundsOnIntercepts(self, value: Vector) -> LogisticRegression: ...
def setUpperBoundsOnIntercepts(self, value: Vector) -> LogisticRegression: ...

class LogisticRegressionModel(JavaProbabilisticClassificationModel[Vector], _LogisticRegressionParams, JavaMLWritable, JavaMLReadable[LogisticRegressionModel], HasTrainingSummary[LogisticRegressionTrainingSummary]):
@property
def coefficients(self) -> Vector: ...
@property
Expand Down Expand Up @@ -178,12 +183,12 @@ class RandomForestClassificationModel(_TreeEnsembleModel, JavaProbabilisticClass
@property
def trees(self) -> List[DecisionTreeClassificationModel]: ...

class GBTClassifierParams(_GBTParams, _HasVarianceImpurity):
class _GBTClassifierParams(_GBTParams, _HasVarianceImpurity):
supportedLossTypes: List[str]
lossType: Param[str]
def getLossType(self) -> str: ...

class GBTClassifier(JavaProbabilisticClassifier[GBTClassificationModel], GBTClassifierParams, JavaMLWritable, JavaMLReadable[GBTClassifier]):
class GBTClassifier(JavaProbabilisticClassifier[GBTClassificationModel], _GBTClassifierParams, JavaMLWritable, JavaMLReadable[GBTClassifier]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., lossType: str = ..., maxIter: int = ..., stepSize: float = ..., seed: Optional[int] = ..., subsamplingRate: float = ..., featureSubsetStrategy: str = ..., validationTol: float = ..., validationIndicatorCol: Optional[str] = ..., leafCol: str = ..., minWeightFractionPerNode: float = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., lossType: str = ..., maxIter: int = ..., stepSize: float = ..., seed: Optional[int] = ..., subsamplingRate: float = ..., featureSubsetStrategy: str = ..., validationTol: float = ..., validationIndicatorCol: Optional[str] = ..., leafCol: str = ..., minWeightFractionPerNode: float = ...) -> GBTClassifier: ...
def setMaxDepth(self, value: int) -> GBTClassifier: ...
Expand All @@ -198,62 +203,66 @@ class GBTClassifier(JavaProbabilisticClassifier[GBTClassificationModel], GBTClas
def setFeatureSubsetStrategy(self, value: str) -> GBTClassifier: ...
def setValidationIndicatorCol(self, value: str) -> GBTClassifier: ...

class GBTClassificationModel(_TreeEnsembleModel, JavaProbabilisticClassificationModel[Vector], GBTClassifierParams, JavaMLWritable, JavaMLReadable[GBTClassificationModel]):
class GBTClassificationModel(_TreeEnsembleModel, JavaProbabilisticClassificationModel[Vector], _GBTClassifierParams, JavaMLWritable, JavaMLReadable[GBTClassificationModel]):
@property
def featureImportances(self) -> Vector: ...
@property
def trees(self) -> List[DecisionTreeRegressionModel]: ...
def evaluateEachIteration(self, dataset: DataFrame) -> List[float]: ...

class NaiveBayes(JavaProbabilisticClassifier[NaiveBayesModel], HasThresholds, HasWeightCol, JavaMLWritable, JavaMLReadable[NaiveBayes]):
class _NaiveBayesParams(_JavaPredictorParams, HasWeightCol):
smoothing: Param[float]
modelType: Param[str]
def getSmoothing(self) -> float: ...
def getModelType(self) -> str: ...

class NaiveBayes(JavaProbabilisticClassifier[NaiveBayesModel], _NaiveBayesParams, HasThresholds, HasWeightCol, JavaMLWritable, JavaMLReadable[NaiveBayes]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., smoothing: float = ..., modelType: str = ..., thresholds: Optional[List[float]] = ..., weightCol: Optional[str] = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., smoothing: float = ..., modelType: str = ..., thresholds: Optional[List[float]] = ..., weightCol: Optional[str] = ...) -> NaiveBayes: ...
def setSmoothing(self, value: float) -> NaiveBayes: ...
def getSmoothing(self) -> float: ...
def setModelType(self, value: str) -> NaiveBayes: ...
def getModelType(self) -> str: ...

class NaiveBayesModel(JavaProbabilisticClassificationModel[Vector], JavaMLWritable, JavaMLReadable[NaiveBayesModel]):
class NaiveBayesModel(JavaProbabilisticClassificationModel[Vector], _NaiveBayesParams, JavaMLWritable, JavaMLReadable[NaiveBayesModel]):
@property
def pi(self) -> Vector: ...
@property
def theta(self) -> Matrix: ...

class MultilayerPerceptronClassifier(JavaProbabilisticClassifier[MultilayerPerceptronClassificationModel], HasMaxIter, HasTol, HasSeed, HasStepSize, HasSolver, JavaMLWritable, JavaMLReadable[MultilayerPerceptronClassifier]):
class _MultilayerPerceptronParams(_JavaProbabilisticClassifierParams, HasSeed, HasMaxIter, HasTol, HasStepSize, HasSolver):
layers: Param[List[int]]
blockSize: Param[int]
solver: Param[str]
initialWeights: Param[Vector]
def getLayers(self) -> List[int]: ...
def getBlockSize(self) -> int: ...
def getStepSize(self) -> float: ...
def getInitialWeights(self) -> Vector: ...

class MultilayerPerceptronClassifier(JavaProbabilisticClassifier[MultilayerPerceptronClassificationModel], _MultilayerPerceptronParams, JavaMLWritable, JavaMLReadable[MultilayerPerceptronClassifier]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., tol: float = ..., seed: Optional[int] = ..., layers: Optional[List[int]] = ..., blockSize: int = ..., stepSize: float = ..., solver: str = ..., initialWeights: Optional[Vector] = ..., probabilityCol: str = ..., rawPredictionCol: str = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., tol: float = ..., seed: Optional[int] = ..., layers: Optional[List[int]] = ..., blockSize: int = ..., stepSize: float = ..., solver: str = ..., initialWeights: Optional[Vector] = ..., probabilityCol: str = ..., rawPredictionCol: str = ...) -> MultilayerPerceptronClassifier: ...
def setLayers(self, value: List[int]) -> MultilayerPerceptronClassifier: ...
def getLayers(self) -> List[int]: ...
def setBlockSize(self, value: int) -> MultilayerPerceptronClassifier: ...
def getBlockSize(self) -> int: ...
def setStepSize(self, value: float) -> MultilayerPerceptronClassifier: ...
def getStepSize(self) -> float: ...
def setInitialWeights(self, value: Vector) -> MultilayerPerceptronClassifier: ...
def getInitialWeights(self) -> Vector: ...

class MultilayerPerceptronClassificationModel(JavaProbabilisticClassificationModel[Vector], JavaMLWritable, JavaMLReadable[MultilayerPerceptronClassificationModel]):
@property
def layers(self) -> List[int]: ...
@property
def weights(self) -> Vector: ...

class OneVsRestParams(JavaClassifierParams, HasWeightCol):
class _OneVsRestParams(_JavaClassifierParams, HasWeightCol):
classifier: Param[Estimator]
def getClassifier(self) -> Estimator[M]: ...

class OneVsRest(Estimator[OneVsRestModel], OneVsRestParams, HasParallelism, JavaMLReadable[OneVsRest], JavaMLWritable):
class OneVsRest(Estimator[OneVsRestModel], _OneVsRestParams, HasParallelism, JavaMLReadable[OneVsRest], JavaMLWritable):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., rawPredictionCol: str = ..., classifier: Optional[Estimator[M]] = ..., weightCol: Optional[str] = ..., parallelism: int = ...) -> None: ...
def setParams(self, *, featuresCol: Optional[str] = ..., labelCol: Optional[str] = ..., predictionCol: Optional[str] = ..., rawPredictionCol: str = ..., classifier: Optional[Estimator[M]] = ..., weightCol: Optional[str] = ..., parallelism: int = ...) -> OneVsRest: ...
def setClassifier(self, value: Estimator[M]) -> OneVsRest: ...
def copy(self, extra: Optional[ParamMap] = ...) -> OneVsRest: ...

class OneVsRestModel(Model, OneVsRestParams, JavaMLReadable[OneVsRestModel], JavaMLWritable):
class OneVsRestModel(Model, _OneVsRestParams, JavaMLReadable[OneVsRestModel], JavaMLWritable):
models: List[Transformer]
def __init__(self, models: List[Transformer]) -> None: ...
def setClassifier(self, value: Estimator[M]) -> OneVsRest: ...
Expand Down
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