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regression.pyi
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regression.pyi
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# Stubs for pyspark.ml.regression (Python 3)
from typing import Any, List, Optional, Sequence
from pyspark.ml._typing import JM, P, T
from pyspark.ml.param.shared import *
from pyspark.ml.linalg import Matrix, Vector
from pyspark.ml.util import *
from pyspark.ml.tree import _DecisionTreeModel, _DecisionTreeParams, _TreeEnsembleModel, _TreeEnsembleParams, _RandomForestParams, _GBTParams, _HasVarianceImpurity, _TreeRegressorParams
from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaPredictionModel, JavaPredictor, _JavaPredictorParams, JavaWrapper
from pyspark.sql.dataframe import DataFrame
class JavaRegressor(JavaPredictor[JM], _JavaPredictorParams): ...
class JavaRegressionModel(JavaPredictionModel[T], _JavaPredictorParams): ...
class _LinearRegressionParams(_JavaPredictorParams, HasRegParam, HasElasticNetParam, HasMaxIter, HasTol, HasFitIntercept, HasStandardization, HasWeightCol, HasSolver, HasAggregationDepth, HasLoss):
solver: Param[str]
loss: Param[str]
epsilon: Param[float]
def getEpsilon(self) -> float: ...
class LinearRegression(JavaPredictor[LinearRegressionModel], _LinearRegressionParams, JavaMLWritable, JavaMLReadable[LinearRegression]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., standardization: bool = ..., solver: str = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., epsilon: float = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., standardization: bool = ..., solver: str = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., epsilon: float = ...) -> LinearRegression: ...
def setEpsilon(self, value: float) -> LinearRegression: ...
def setMaxIter(self, value: int) -> LinearRegression: ...
def setRegParam(self, value: float) -> LinearRegression: ...
def setTol(self, value: float) -> LinearRegression: ...
def setElasticNetParam(self, value: float) -> LinearRegression: ...
def setFitIntercept(self, value: bool) -> LinearRegression: ...
def setStandardization(self, value: bool) -> LinearRegression: ...
def setWeightCol(self, value: str) -> LinearRegression: ...
def setSolver(self, value: str) -> LinearRegression: ...
def setAggregationDepth(self, value: int) -> LinearRegression: ...
def setLoss(self, value: str) -> LinearRegression: ...
class LinearRegressionModel(JavaPredictionModel[Vector], _LinearRegressionParams, GeneralJavaMLWritable, JavaMLReadable[LinearRegressionModel], HasTrainingSummary[LinearRegressionSummary]):
@property
def coefficients(self) -> Vector: ...
@property
def intercept(self) -> float: ...
@property
def summary(self) -> LinearRegressionTrainingSummary: ...
@property
def hasSummary(self) -> bool: ...
def evaluate(self, dataset: DataFrame) -> LinearRegressionSummary: ...
class LinearRegressionSummary(JavaWrapper):
@property
def predictions(self) -> DataFrame: ...
@property
def predictionCol(self) -> str: ...
@property
def labelCol(self) -> str: ...
@property
def featuresCol(self) -> str: ...
@property
def explainedVariance(self) -> float: ...
@property
def meanAbsoluteError(self) -> float: ...
@property
def meanSquaredError(self) -> float: ...
@property
def rootMeanSquaredError(self) -> float: ...
@property
def r2(self) -> float: ...
@property
def r2adj(self) -> float: ...
@property
def residuals(self) -> DataFrame: ...
@property
def numInstances(self) -> int: ...
@property
def devianceResiduals(self) -> List[float]: ...
@property
def coefficientStandardErrors(self) -> List[float]: ...
@property
def tValues(self) -> List[float]: ...
@property
def pValues(self) -> List[float]: ...
class LinearRegressionTrainingSummary(LinearRegressionSummary):
@property
def objectiveHistory(self) -> List[float]: ...
@property
def totalIterations(self) -> int: ...
class _IsotonicRegressionParams(HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol):
isotonic: Param[bool]
featureIndex: Param[int]
def getIsotonic(self) -> bool: ...
def getFeatureIndex(self) -> int: ...
class IsotonicRegression(JavaEstimator[IsotonicRegressionModel], _IsotonicRegressionParams, HasWeightCol, JavaMLWritable, JavaMLReadable[IsotonicRegression]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., weightCol: Optional[str] = ..., isotonic: bool = ..., featureIndex: int = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., weightCol: Optional[str] = ..., isotonic: bool = ..., featureIndex: int = ...) -> IsotonicRegression: ...
def setIsotonic(self, value: bool) -> IsotonicRegression: ...
def setFeatureIndex(self, value: int) -> IsotonicRegression: ...
def setFeaturesCol(self, value: str) -> IsotonicRegression: ...
def setPredictionCol(self, value: str) -> IsotonicRegression: ...
def setLabelCol(self, value: str) -> IsotonicRegression: ...
def setWeightCol(self, value: str) -> IsotonicRegression: ...
class IsotonicRegressionModel(JavaModel, _IsotonicRegressionParams, JavaMLWritable, JavaMLReadable[IsotonicRegressionModel]):
def setFeaturesCol(self, value: str) -> IsotonicRegressionModel: ...
def setPredictionCol(self, value: str) -> IsotonicRegressionModel: ...
def setFeatureIndex(self, value: int) -> IsotonicRegressionModel: ...
@property
def boundaries(self) -> Vector: ...
@property
def predictions(self) -> Vector: ...
@property
def numFeatures(self) -> int: ...
def predict(self, value: float) -> float: ...
class _DecisionTreeRegressorParams(_DecisionTreeParams, _TreeRegressorParams, HasVarianceCol): ...
class DecisionTreeRegressor(JavaPredictor[DecisionTreeRegressionModel], _DecisionTreeRegressorParams, JavaMLWritable, JavaMLReadable[DecisionTreeRegressor], HasVarianceCol):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., seed: Optional[int] = ..., varianceCol: Optional[str] = ..., weightCol: 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 = ..., impurity: str = ..., seed: Optional[int] = ..., varianceCol: Optional[str] = ..., weightCol: Optional[str] = ..., leafCol: str = ..., minWeightFractionPerNode: float = ...) -> DecisionTreeRegressor: ...
def setMaxDepth(self, value: int) -> DecisionTreeRegressor: ...
def setMaxBins(self, value: int) -> DecisionTreeRegressor: ...
def setMinInstancesPerNode(self, value: int) -> DecisionTreeRegressor: ...
def setMinWeightFractionPerNode(self, value: float) -> DecisionTreeRegressor: ...
def setMinInfoGain(self, value: float) -> DecisionTreeRegressor: ...
def setMaxMemoryInMB(self, value: int) -> DecisionTreeRegressor: ...
def setCacheNodeIds(self, value: bool) -> DecisionTreeRegressor: ...
def setImpurity(self, value: str) -> DecisionTreeRegressor: ...
def setCheckpointInterval(self, value: int) -> DecisionTreeRegressor: ...
def setSeed(self, value: int) -> DecisionTreeRegressor: ...
def setWeightCol(self, value: str) -> DecisionTreeRegressor: ...
def setVarianceCol(self, value: str) -> DecisionTreeRegressor: ...
class DecisionTreeRegressionModel(_DecisionTreeModel[T], JavaMLWritable, JavaMLReadable[DecisionTreeRegressionModel]):
def setVarianceCol(self, value: str) -> DecisionTreeRegressionModel: ...
@property
def featureImportances(self) -> Vector: ...
class _RandomForestRegressorParams(_RandomForestParams, _TreeRegressorParams): ...
class RandomForestRegressor(JavaPredictor[RandomForestRegressionModel], _RandomForestRegressorParams, JavaMLWritable, JavaMLReadable[RandomForestRegressor]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., subsamplingRate: float = ..., seed: Optional[int] = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ..., bootstrap: Optional[bool] = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., checkpointInterval: int = ..., impurity: str = ..., subsamplingRate: float = ..., seed: Optional[int] = ..., numTrees: int = ..., featureSubsetStrategy: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ..., bootstrap: Optional[bool] = ...) -> RandomForestRegressor: ...
def setMaxDepth(self, value: int) -> RandomForestRegressor: ...
def setMaxBins(self, value: int) -> RandomForestRegressor: ...
def setMinInstancesPerNode(self, value: int) -> RandomForestRegressor: ...
def setMinInfoGain(self, value: float) -> RandomForestRegressor: ...
def setMaxMemoryInMB(self, value: int) -> RandomForestRegressor: ...
def setCacheNodeIds(self, value: bool) -> RandomForestRegressor: ...
def setImpurity(self, value: str) -> RandomForestRegressor: ...
def setNumTrees(self, value: int) -> RandomForestRegressor: ...
def setBootstrap(self, value: bool) -> RandomForestRegressor: ...
def setSubsamplingRate(self, value: float) -> RandomForestRegressor: ...
def setFeatureSubsetStrategy(self, value: str) -> RandomForestRegressor: ...
def setCheckpointInterval(self, value: int) -> RandomForestRegressor: ...
def setSeed(self, value: int) -> RandomForestRegressor: ...
def setWeightCol(self, value: str) -> RandomForestRegressor: ...
def setMinWeightFractionPerNode(self, value: float) -> RandomForestRegressor: ...
class RandomForestRegressionModel(_TreeEnsembleModel[Vector], _RandomForestRegressorParams, JavaMLWritable, JavaMLReadable[RandomForestRegressionModel]):
@property
def trees(self) -> Sequence[DecisionTreeRegressionModel]: ...
@property
def featureImportances(self) -> Vector: ...
class _GBTRegressorParams(_GBTParams, _TreeRegressorParams):
supportedLossTypes: List[str]
lossType: Param[str]
def getLossType(self) -> str: ...
class GBTRegressor(JavaPredictor[GBTRegressionModel], _GBTRegressorParams, JavaMLWritable, JavaMLReadable[GBTRegressor]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., subsamplingRate: float = ..., checkpointInterval: int = ..., lossType: str = ..., maxIter: int = ..., stepSize: float = ..., seed: Optional[int] = ..., impurity: str = ..., featureSubsetStrategy: str = ..., validationTol: float = ..., validationIndicatorCol: Optional[str] = ..., leafCol: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxDepth: int = ..., maxBins: int = ..., minInstancesPerNode: int = ..., minInfoGain: float = ..., maxMemoryInMB: int = ..., cacheNodeIds: bool = ..., subsamplingRate: float = ..., checkpointInterval: int = ..., lossType: str = ..., maxIter: int = ..., stepSize: float = ..., seed: Optional[int] = ..., impuriy: str = ..., featureSubsetStrategy: str = ..., validationTol: float = ..., validationIndicatorCol: Optional[str] = ..., leafCol: str = ..., minWeightFractionPerNode: float = ..., weightCol: Optional[str] = ...) -> GBTRegressor: ...
def setMaxDepth(self, value: int) -> GBTRegressor: ...
def setMaxBins(self, value: int) -> GBTRegressor: ...
def setMinInstancesPerNode(self, value: int) -> GBTRegressor: ...
def setMinInfoGain(self, value: float) -> GBTRegressor: ...
def setMaxMemoryInMB(self, value: int) -> GBTRegressor: ...
def setCacheNodeIds(self, value: bool) -> GBTRegressor: ...
def setImpurity(self, value: str) -> GBTRegressor: ...
def setLossType(self, value: str) -> GBTRegressor: ...
def setSubsamplingRate(self, value: float) -> GBTRegressor: ...
def setFeatureSubsetStrategy(self, value: str) -> GBTRegressor: ...
def setValidationIndicatorCol(self, value: str) -> GBTRegressor: ...
def setMaxIter(self, value: int) -> GBTRegressor: ...
def setCheckpointInterval(self, value: int) -> GBTRegressor: ...
def setSeed(self, value: int) -> GBTRegressor: ...
def setStepSize(self, value: float) -> GBTRegressor: ...
def setWeightCol(self, value: str) -> GBTRegressor: ...
def setMinWeightFractionPerNode(self, value: float) -> GBTRegressor: ...
class GBTRegressionModel(_TreeEnsembleModel[Vector], _GBTRegressorParams, JavaMLWritable, JavaMLReadable[GBTRegressionModel]):
@property
def featureImportances(self) -> Vector: ...
@property
def trees(self) -> Sequence[DecisionTreeRegressionModel]: ...
def evaluateEachIteration(self, dataset: DataFrame, loss: str) -> List[float]: ...
class _AFTSurvivalRegressionParams(_JavaPredictorParams, HasMaxIter, HasTol, HasFitIntercept, HasAggregationDepth):
censorCol: Param[str]
quantileProbabilities: Param[List[float]]
quantilesCol: Param[str]
def getCensorCol(self) -> str: ...
def getQuantileProbabilities(self) -> List[float]: ...
def getQuantilesCol(self) -> str: ...
class AFTSurvivalRegression(JavaPredictor[AFTSurvivalRegressionModel], _AFTSurvivalRegressionParams, JavaMLWritable, JavaMLReadable[AFTSurvivalRegression]):
def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., fitIntercept: bool = ..., maxIter: int = ..., tol: float = ..., censorCol: str = ..., quantileProbabilities: List[float] = ..., quantilesCol: Optional[str] = ..., aggregationDepth: int = ...) -> None: ...
def setParams(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., fitIntercept: bool = ..., maxIter: int = ..., tol: float = ..., censorCol: str = ..., quantileProbabilities: List[float] = ..., quantilesCol: Optional[str] = ..., aggregationDepth: int = ...) -> AFTSurvivalRegression: ...
def setCensorCol(self, value: str) -> AFTSurvivalRegression: ...
def setQuantileProbabilities(self, value: List[float]) -> AFTSurvivalRegression: ...
def setQuantilesCol(self, value: str) -> AFTSurvivalRegression: ...
def setMaxIter(self, value: int) -> AFTSurvivalRegression: ...
def setTol(self, value: float) -> AFTSurvivalRegression: ...
def setFitIntercept(self, value: bool) -> AFTSurvivalRegression: ...
def setAggregationDepth(self, value: int) -> AFTSurvivalRegression: ...
class AFTSurvivalRegressionModel(JavaPredictionModel[Vector], _AFTSurvivalRegressionParams, JavaMLWritable, JavaMLReadable[AFTSurvivalRegressionModel]):
def setQuantileProbabilities(self, value: List[float]) -> AFTSurvivalRegressionModel: ...
def setQuantilesCol(self, value: str) -> AFTSurvivalRegressionModel: ...
@property
def coefficients(self) -> Vector: ...
@property
def intercept(self) -> float: ...
@property
def scale(self) -> float: ...
def predictQuantiles(self, features: Vector) -> Vector: ...
def predict(self, features: Vector) -> float: ...
class _GeneralizedLinearRegressionParams(_JavaPredictorParams, HasFitIntercept, HasMaxIter, HasTol, HasRegParam, HasWeightCol, HasSolver, HasAggregationDepth):
family: Param[str]
link: Param[str]
linkPredictionCol: Param[str]
variancePower: Param[float]
linkPower: Param[float]
solver: Param[str]
offsetCol: Param[str]
def getFamily(self) -> str: ...
def getLinkPredictionCol(self) -> str: ...
def getLink(self) -> str: ...
def getVariancePower(self) -> float: ...
def getLinkPower(self) -> float: ...
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] = ..., 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: ...
def setVariancePower(self, value: float) -> GeneralizedLinearRegression: ...
def setLinkPower(self, value: float) -> GeneralizedLinearRegression: ...
def setOffsetCol(self, value: str) -> GeneralizedLinearRegression: ...
def setMaxIter(self, value: int) -> GeneralizedLinearRegression: ...
def setRegParam(self, value: float) -> GeneralizedLinearRegression: ...
def setTol(self, value: float) -> GeneralizedLinearRegression: ...
def setFitIntercept(self, value: bool) -> GeneralizedLinearRegression: ...
def setWeightCol(self, value: str) -> GeneralizedLinearRegression: ...
def setSolver(self, value: str) -> GeneralizedLinearRegression: ...
def setAggregationDepth(self, value: int) -> GeneralizedLinearRegression: ...
class GeneralizedLinearRegressionModel(JavaPredictionModel[Vector], _GeneralizedLinearRegressionParams, JavaMLWritable, JavaMLReadable[GeneralizedLinearRegressionModel], HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]):
def setLinkPredictionCol(self, value: str) -> GeneralizedLinearRegressionModel: ...
@property
def coefficients(self) -> Vector: ...
@property
def intercept(self) -> float: ...
@property
def summary(self) -> GeneralizedLinearRegressionTrainingSummary: ...
@property
def hasSummary(self) -> bool: ...
def evaluate(self, dataset: DataFrame) -> GeneralizedLinearRegressionSummary: ...
class GeneralizedLinearRegressionSummary(JavaWrapper):
@property
def predictions(self) -> DataFrame: ...
@property
def predictionCol(self) -> str: ...
@property
def rank(self) -> int: ...
@property
def degreesOfFreedom(self) -> int: ...
@property
def residualDegreeOfFreedom(self) -> int: ...
@property
def residualDegreeOfFreedomNull(self) -> int: ...
def residuals(self, residualsType: str = ...) -> DataFrame: ...
@property
def nullDeviance(self) -> float: ...
@property
def deviance(self) -> float: ...
@property
def dispersion(self) -> float: ...
@property
def aic(self) -> float: ...
class GeneralizedLinearRegressionTrainingSummary(GeneralizedLinearRegressionSummary):
@property
def numIterations(self) -> int: ...
@property
def solver(self) -> str: ...
@property
def coefficientStandardErrors(self) -> List[float]: ...
@property
def tValues(self) -> List[float]: ...
@property
def pValues(self) -> List[float]: ...
class _FactorizationMachinesParams(_JavaPredictorParams, HasMaxIter, HasStepSize, HasTol, HasSolver, HasSeed, HasFitIntercept, HasRegParam):
factorSize: Param[int]
fitLinear: Param[bool]
miniBatchFraction: Param[float]
initStd: Param[float]
solver: Param[str]
def getFactorSize(self): ...
def getFitLinear(self): ...
def getMiniBatchFraction(self): ...
def getInitStd(self): ...
class FMRegressor(JavaPredictor[FMRegressionModel], _FactorizationMachinesParams, JavaMLWritable, JavaMLReadable[FMRegressor]):
factorSize: Param[int]
fitLinear: Param[bool]
miniBatchFraction: Param[float]
initStd: Param[float]
solver: Param[str]
def __init__(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., factorSize: int = ..., fitIntercept: bool = ..., fitLinear: bool = ..., regParam: float = ..., miniBatchFraction: float = ..., initStd: float = ..., maxIter: int = ..., stepSize: float = ..., tol: float = ..., solver: str = ..., seed: Optional[int] = ...) -> None: ...
def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., factorSize: int = ..., fitIntercept: bool = ..., fitLinear: bool = ..., regParam: float = ..., miniBatchFraction: float = ..., initStd: float = ..., maxIter: int = ..., stepSize: float = ..., tol: float = ..., solver: str = ..., seed: Optional[int] = ...) -> FMRegressor: ...
def setFactorSize(self, value: int) -> FMRegressor: ...
def setFitLinear(self, value: bool) -> FMRegressor: ...
def setMiniBatchFraction(self, value: float) -> FMRegressor: ...
def setInitStd(self, value: float) -> FMRegressor: ...
def setMaxIter(self, value: int) -> FMRegressor: ...
def setStepSize(self, value: float) -> FMRegressor: ...
def setTol(self, value: float) -> FMRegressor: ...
def setSolver(self, value: str) -> FMRegressor: ...
def setSeed(self, value: int) -> FMRegressor: ...
def setFitIntercept(self, value: bool) -> FMRegressor: ...
def setRegParam(self, value: float) -> FMRegressor: ...
class FMRegressionModel(JavaPredictionModel, _FactorizationMachinesParams, JavaMLWritable, JavaMLReadable[FMRegressionModel]):
@property
def intercept(self) -> float: ...
@property
def linear(self) -> Vector: ...
@property
def factors(self) -> Matrix: ...