OpGeneralizedLinearRegression

Instance Constructors

new OpGeneralizedLinearRegression(uid: String = UID[OpGeneralizedLinearRegression])

uid
stage uid

Type Members

final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

Definition Classes
OpPipelineStage2 → OpPipelineStage → InputParams
final type OutputFeatures = FeatureLike[Prediction]

Definition Classes
OpPipelineStage → OpPipelineStageBase

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def checkInputLength(features: Array[_]): Boolean

Definition Classes
OpPipelineStage2 → InputParams
def checkSerializable: Try[Unit]

Definition Classes
OpPipelineStageBase
final def clear(param: Param[_]): OpGeneralizedLinearRegression.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def copy(extra: ParamMap): OpGeneralizedLinearRegression.this.type

Definition Classes
OpPipelineStageBase → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
final val family: Param[String]

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.0.0" )
final val featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataset: Dataset[_]): OpPredictorWrapperModel[GeneralizedLinearRegressionModel]

Function that fits the binary model
Function that fits the binary model

Definition Classes
OpPredictorWrapper → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[GeneralizedLinearRegressionModel]]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], paramMap: ParamMap): OpPredictorWrapperModel[GeneralizedLinearRegressionModel]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OpPredictorWrapperModel[GeneralizedLinearRegressionModel]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
final val fitIntercept: BooleanParam

Definition Classes
HasFitIntercept
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getFamily: String

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.0.0" )
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept
def getInputColParamNames(): Array[String]

Definition Classes
SparkWrapperParams
final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

Definition Classes
InputParams
final def getInputFeatures(): Array[OPFeature]

Definition Classes
InputParams
final def getInputSchema(): StructType

Definition Classes
OpPipelineStageParams
final def getLabelCol: String

Definition Classes
HasLabelCol
def getLink: String

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.0.0" )
def getLinkPower: Double

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.2.0" )
def getLinkPredictionCol: String

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.0.0" )
final def getMaxIter: Int

Definition Classes
HasMaxIter
final def getMetadata(): Metadata

Definition Classes
OpPipelineStageParams
def getOffsetCol: String

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.3.0" )
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getOutput(): FeatureLike[Prediction]

Definition Classes
HasOut → OpPipelineStageBase
def getOutputColParamNames(): Array[String]

Definition Classes
SparkWrapperParams
final def getOutputFeatureName: String

Definition Classes
OpPipelineStage
def getParam(paramName: String): Param[Any]

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getRegParam: Double

Definition Classes
HasRegParam
final def getSolver: String

Definition Classes
HasSolver
def getSparkMlStage(): Option[GeneralizedLinearRegression]

Definition Classes
SparkWrapperParams
def getStageSavePath(): Option[String]

Definition Classes
SparkWrapperParams
final def getTol: Double

Definition Classes
HasTol
final def getTransientFeature(i: Int): Option[TransientFeature]

Definition Classes
InputParams
final def getTransientFeatures(): Array[TransientFeature]

Definition Classes
InputParams
def getVariancePower: Double

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.2.0" )
final def getWeightCol: String

Definition Classes
HasWeightCol
final def hasDefault[T](param: Param[T]): Boolean

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
final def in1: TransientFeature

Attributes
protected
Definition Classes
HasIn1
final def in2: TransientFeature

Attributes
protected
Definition Classes
HasIn2
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def inputAsArray(in: InputFeatures): Array[OPFeature]

Definition Classes
OpPipelineStage2 → InputParams
val inputParam1Name: String

Definition Classes
OpPredictorWrapper
val inputParam2Name: String

Definition Classes
OpPredictorWrapper
final def isDefined(param: Param[_]): Boolean

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def isSet(param: Param[_]): Boolean

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
final val labelCol: Param[String]

Definition Classes
HasLabelCol
final val link: Param[String]

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.0.0" )
final val linkPower: DoubleParam

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.2.0" )
final val linkPredictionCol: Param[String]

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.0.0" )
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final val maxIter: IntParam

Definition Classes
HasMaxIter
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final val offsetCol: Param[String]

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.3.0" )
def onGetMetadata(): Unit

Attributes
protected
Definition Classes
OpPipelineStageParams
def onSetInput(): Unit

Attributes
protected
Definition Classes
OpGeneralizedLinearRegression → InputParams
val operationName: String

Definition Classes
OpPredictorWrapper → OpPipelineStageBase
final def outputAsArray(out: OutputFeatures): Array[OPFeature]

Definition Classes
OpPipelineStage → OpPipelineStageBase
def outputFeatureUid: String

Attributes
protected[com.salesforce.op]
Definition Classes
OpPipelineStage2 → OpPipelineStage
def outputIsResponse: Boolean

Definition Classes
OpPipelineStage
val outputParamName: String

Definition Classes
OpPredictorWrapper
lazy val params: Array[Param[_]]

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
val predictor: GeneralizedLinearRegression

the predictor to wrap
the predictor to wrap

Definition Classes
OpPredictorWrapper
final val regParam: DoubleParam

Definition Classes
HasRegParam
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): OpGeneralizedLinearRegression.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): OpGeneralizedLinearRegression.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): OpGeneralizedLinearRegression.this.type

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): OpGeneralizedLinearRegression.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): OpGeneralizedLinearRegression.this.type

Attributes
protected
Definition Classes
Params
def setFamily(value: String): OpGeneralizedLinearRegression.this.type

Sets the value of param family.
Sets the value of param family. Default is "gaussian".
def setFitIntercept(value: Boolean): OpGeneralizedLinearRegression.this.type

Sets if we should fit the intercept.
Sets if we should fit the intercept. Default is true.
final def setInput(features: InputFeatures): OpGeneralizedLinearRegression.this.type

Definition Classes
OpPipelineStageBase
final def setInputFeatures[S <: OPFeature](features: Array[S]): OpGeneralizedLinearRegression.this.type

Attributes
protected
Definition Classes
InputParams
def setLink(value: String): OpGeneralizedLinearRegression.this.type

Sets the value of param link.
Sets the value of param link. Used only when family is not "tweedie".
def setLinkPower(value: Double): OpGeneralizedLinearRegression.this.type

Sets the value of param linkPower.
Sets the value of param linkPower. Used only when family is "tweedie".
def setLinkPredictionCol(value: String): OpGeneralizedLinearRegression.this.type

Sets the link prediction (linear predictor) column name.
def setMaxIter(value: Int): OpGeneralizedLinearRegression.this.type

Sets the maximum number of iterations (applicable for solver "irls").
Sets the maximum number of iterations (applicable for solver "irls"). Default is 25.
final def setMetadata(m: Metadata): OpGeneralizedLinearRegression.this.type

Definition Classes
OpPipelineStageParams
def setOutputFeatureName(name: String): OpGeneralizedLinearRegression.this.type

Definition Classes
OpPipelineStage
def setRegParam(value: Double): OpGeneralizedLinearRegression.this.type

Sets the regularization parameter for L2 regularization.
Sets the regularization parameter for L2 regularization. The regularization term is
$$ 0.5 * regParam * L2norm(coefficients)^2 $$
Default is 0.0.
def setSolver(value: String): OpGeneralizedLinearRegression.this.type

Sets the solver algorithm used for optimization.
Sets the solver algorithm used for optimization. Currently only supports "irls" which is also the default solver.
def setSparkMlStage(stage: Option[GeneralizedLinearRegression]): OpGeneralizedLinearRegression.this.type

Attributes
protected
Definition Classes
SparkWrapperParams
def setStageSavePath(path: String): OpGeneralizedLinearRegression.this.type

Definition Classes
SparkWrapperParams
def setTol(value: Double): OpGeneralizedLinearRegression.this.type

Sets the convergence tolerance of iterations.
Sets the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.
def setVariancePower(value: Double): OpGeneralizedLinearRegression.this.type

Sets the value of param variancePower.
Sets the value of param variancePower. Used only when family is "tweedie". Default is 0.0, which corresponds to the "gaussian" family.
def setWeightCol(value: String): OpGeneralizedLinearRegression.this.type

Sets the value of param weightCol.
Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one. In the Binomial family, weights correspond to number of trials and should be integer. Non-integer weights are rounded to integer in AIC calculation.
final val solver: Param[String]

Definition Classes
GeneralizedLinearRegressionBase → HasSolver
Annotations
@Since( "2.0.0" )
final val sparkInputColParamNames: StringArrayParam

Definition Classes
SparkWrapperParams
final val sparkMlStage: SparkStageParam[GeneralizedLinearRegression]

Definition Classes
SparkWrapperParams
final val sparkOutputColParamNames: StringArrayParam

Definition Classes
SparkWrapperParams
final def stageName: String

Definition Classes
OpPipelineStageBase
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
final val tol: DoubleParam

Definition Classes
HasTol
final def transformSchema(schema: StructType): StructType

Definition Classes
OpPipelineStageBase
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

Definition Classes
OpPredictorWrapper
implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

Definition Classes
OpPredictorWrapper
implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

Definition Classes
OpPredictorWrapper → HasOut
implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

Definition Classes
OpPredictorWrapper → HasOut
val uid: String

stage uid
stage uid

Definition Classes
OpPredictorWrapper → Identifiable
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

Definition Classes
GeneralizedLinearRegressionBase → PredictorParams
Annotations
@Since( "2.0.0" )
final val variancePower: DoubleParam

Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since( "2.2.0" )
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final val weightCol: Param[String]

Definition Classes
HasWeightCol
final def write: MLWriter

Definition Classes
OpPipelineStageBase → MLWritable

Inherited from OpGeneralizedLinearRegressionParams

Inherited from GeneralizedLinearRegressionBase

Inherited from HasSolver

Inherited from HasWeightCol

Inherited from HasRegParam

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasFitIntercept

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[GeneralizedLinearRegression, GeneralizedLinearRegressionModel]

Inherited from SparkWrapperParams[GeneralizedLinearRegression]

Inherited from OpPipelineStage2[RealNN, OPVector, Prediction]

Inherited from HasOut[Prediction]

Inherited from HasIn2

Inherited from HasIn1

Inherited from OpPipelineStage[Prediction]

Inherited from OpPipelineStageBase

Inherited from MLWritable

Inherited from OpPipelineStageParams

Inherited from InputParams

Inherited from Estimator[OpPredictorWrapperModel[GeneralizedLinearRegressionModel]]

Related Doc: package regression

class OpGeneralizedLinearRegression extends OpPredictorWrapper[GeneralizedLinearRegression, GeneralizedLinearRegressionModel] with OpGeneralizedLinearRegressionParams

Instance Constructors

new OpGeneralizedLinearRegression(uid: String = UID[OpGeneralizedLinearRegression])

Type Members

final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

final type OutputFeatures = FeatureLike[Prediction]

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def checkInputLength(features: Array[_]): Boolean

def checkSerializable: Try[Unit]

final def clear(param: Param[_]): OpGeneralizedLinearRegression.this.type

def clone(): AnyRef

final def copy(extra: ParamMap): OpGeneralizedLinearRegression.this.type

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val family: Param[String]

final val featuresCol: Param[String]

def finalize(): Unit

def fit(dataset: Dataset[_]): OpPredictorWrapperModel[GeneralizedLinearRegressionModel]

def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[GeneralizedLinearRegressionModel]]

def fit(dataset: Dataset[_], paramMap: ParamMap): OpPredictorWrapperModel[GeneralizedLinearRegressionModel]

def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OpPredictorWrapperModel[GeneralizedLinearRegressionModel]

final val fitIntercept: BooleanParam

final def get[T](param: Param[T]): Option[T]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

def getFamily: String

final def getFeaturesCol: String

final def getFitIntercept: Boolean

def getInputColParamNames(): Array[String]

final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

final def getInputFeatures(): Array[OPFeature]

final def getInputSchema(): StructType

final def getLabelCol: String

def getLink: String

def getLinkPower: Double

def getLinkPredictionCol: String

final def getMaxIter: Int

final def getMetadata(): Metadata

def getOffsetCol: String

final def getOrDefault[T](param: Param[T]): T

def getOutput(): FeatureLike[Prediction]

def getOutputColParamNames(): Array[String]

final def getOutputFeatureName: String

def getParam(paramName: String): Param[Any]

final def getPredictionCol: String

final def getRegParam: Double

final def getSolver: String

def getSparkMlStage(): Option[GeneralizedLinearRegression]

def getStageSavePath(): Option[String]

final def getTol: Double

final def getTransientFeature(i: Int): Option[TransientFeature]

final def getTransientFeatures(): Array[TransientFeature]

def getVariancePower: Double

final def getWeightCol: String

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hashCode(): Int

final def in1: TransientFeature

final def in2: TransientFeature

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def inputAsArray(in: InputFeatures): Array[OPFeature]

val inputParam1Name: String

val inputParam2Name: String

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean