GBMRegressor

Instance Constructors

new GBMRegressor()
new GBMRegressor(uid: String)

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
val alpha: Param[Double]

The alpha-quantile of the huber loss function and the quantile loss function.
The alpha-quantile of the huber loss function and the quantile loss function. Only if loss="huber" or loss="quantile". (default = 0.9)

Definition Classes
GBMRegressorParams
final def asInstanceOf[T0]: T0

Definition Classes
Any
def avgLoss(lossColName: String, boostProbaColName: String)(df: DataFrame): Double

Definition Classes
BoostingParams
val baseLearner: Param[EnsemblePredictorType]

param for the estimator that will be used by the ensemble learner as a base learner
param for the estimator that will be used by the ensemble learner as a base learner

Definition Classes
HasBaseLearner
def beta(avgl: Double, numClasses: Int = 2): Double

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

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
GBMRegressor → Predictor → Estimator → PipelineStage → 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 evaluateOnValidation(model: GBMClassificationModel, labelColName: String, loss: (Double, Double) ⇒ Double)(df: DataFrame): Double

Definition Classes
GBMParams
def evaluateOnValidation(model: GBMRegressionModel, labelColName: String, loss: (Double, Double) ⇒ Double)(df: DataFrame): Double

Definition Classes
GBMParams
def evaluateOnValidation(numClasses: Int, weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

Definition Classes
BoostingParams
def evaluateOnValidation(weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

Definition Classes
BoostingParams
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
def extractBoostedBag(poissonProbaColName: String, seed: Long)(df: DataFrame): DataFrame

Definition Classes
BoostingParams
def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

Attributes
protected
Definition Classes
Predictor
final def extractParamMap(): ParamMap

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

Definition Classes
Params
def extractSubBag(bagColName: String, index: Int, featuresColName: String, subspace: SubSpace)(df: DataFrame): DataFrame

Definition Classes
HasSubBag
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 findOptimizedConst(labelColName: String, loss: (Double, Double) ⇒ Double, maxIter: Int, tol: Double)(df: DataFrame): Double

Definition Classes
GBMParams
def findOptimizedConst(labelColName: String, loss: (Double, Double) ⇒ Double, grad: (Double, Double) ⇒ Double, maxIter: Int, tol: Double)(df: DataFrame): Double

Definition Classes
GBMParams
def findOptimizedWeight(labelColName: String, currentPredictionColName: String, boosterPredictionColName: String, loss: (Double, Double) ⇒ Double, maxIter: Int, tol: Double)(df: DataFrame): Double

Definition Classes
GBMParams
def findOptimizedWeight(labelColName: String, currentPredictionColName: String, boosterPredictionColName: String, loss: (Double, Double) ⇒ Double, grad: (Double, Double) ⇒ Double, maxIter: Int, tol: Double)(df: DataFrame): Double

Definition Classes
GBMParams
def fit(dataset: Dataset[_]): GBMRegressionModel

Definition Classes
Predictor → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[GBMRegressionModel]

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

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

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
def fitBaseLearner(baseLearner: EnsemblePredictorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType

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

Definition Classes
Params
def getAlpha: Double

Definition Classes
GBMRegressorParams
def getBaseLearner: EnsemblePredictorType

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

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

Definition Classes
Params
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getLabelCol: String

Definition Classes
HasLabelCol
def getLearningRate: Double

Definition Classes
HasLearningRate
def getLoss: String

Definition Classes
GBMRegressorParams
final def getMaxIter: Int

Definition Classes
HasMaxIter
def getNumBaseLearners: Int

Definition Classes
HasNumBaseLearners
def getNumFeatures(dataset: DataFrame, featuresCol: String): Int

Definition Classes
HasSubBag
def getNumRound: Int

Definition Classes
HasNumRound
def getOptimizedWeights: Boolean

Definition Classes
GBMParams
final def getOrDefault[T](param: Param[T]): T

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

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
def getReplacement: Boolean

Definition Classes
HasSubBag
def getSampleRatio: Double

Definition Classes
HasSubBag
final def getSeed: Long

Definition Classes
HasSeed
def getSubspaceRatio: Double

Definition Classes
HasSubBag
final def getTol: Double

Definition Classes
HasTol
final def getValidationIndicatorCol: String

Definition Classes
HasValidationIndicatorCol
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
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

Attributes
protected
Definition Classes
Logging
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
val learningRate: Param[Double]

param for the learning rate of the algorithm
param for the learning rate of the algorithm

Definition Classes
HasLearningRate
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
val loss: Param[String]

Loss function which Boosting tries to minimize.
Loss function which Boosting tries to minimize. (case-insensitive) Supported: "squared", "absolute", "huber", "quantile". (default = ls)

Definition Classes
GBMRegressorParams
final val maxIter: IntParam

Definition Classes
HasMaxIter
def mkSubspace(sampleRatio: Double, numFeatures: Int, seed: Long): SubSpace

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numBaseLearners: Param[Int]

param for the number of base learners of the algorithm
param for the number of base learners of the algorithm

Definition Classes
HasNumBaseLearners
val numRound: Param[Int]

param for the number of round waiting for next decrease in validation set
param for the number of round waiting for next decrease in validation set

Definition Classes
HasNumRound
val optimizedWeights: Param[Boolean]

param for using optimized weights in GBM
param for using optimized weights in GBM

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

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
def probabilize(boostWeightColName: String, boostProbaColName: String, poissonProbaColName: String)(df: DataFrame): DataFrame

Definition Classes
BoostingParams
val replacement: Param[Boolean]

param for whether samples are drawn with replacement
param for whether samples are drawn with replacement

Definition Classes
HasSubBag
val sampleRatio: Param[Double]

param for ratio of rows sampled out of the dataset
param for ratio of rows sampled out of the dataset

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

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final val seed: LongParam

Definition Classes
HasSeed
final def set(paramPair: ParamPair[_]): GBMRegressor.this.type

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

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

Definition Classes
Params
def setAlpha(value: Double): GBMRegressor.this.type
def setBaseLearner(value: Predictor[_, _, _]): GBMRegressor.this.type
final def setDefault(paramPairs: ParamPair[_]*): GBMRegressor.this.type

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

Attributes
protected
Definition Classes
Params
def setFeaturesCol(value: String): GBMRegressor

Definition Classes
Predictor
def setLabelCol(value: String): GBMRegressor

Definition Classes
Predictor
def setLearningRate(value: Double): GBMRegressor.this.type
def setLoss(value: String): GBMRegressor.this.type
def setMaxIter(value: Int): GBMRegressor.this.type
def setNumBaseLearners(value: Int): GBMRegressor.this.type
def setOptimizedWeights(value: Boolean): GBMRegressor.this.type
def setPredictionCol(value: String): GBMRegressor

Definition Classes
Predictor
def setSeed(value: Long): GBMRegressor.this.type
def setTol(value: Double): GBMRegressor.this.type
def setValidationIndicatorCol(value: String): GBMRegressor.this.type
def setWeightCol(value: String): GBMRegressor.this.type
def slicer(subspace: SubSpace): (Vector) ⇒ Vector

Definition Classes
HasSubBag
val subspaceRatio: Param[Double]

param for ratio of rows sampled out of the dataset
param for ratio of rows sampled out of the dataset

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

Definition Classes
AnyRef
def terminate(weight: Double, learningRate: Double, withValidation: Boolean, error: Double, verror: Double, tol: Double, numRound: Int, numTry: Int, iter: Int, instrumentation: Instrumentation): (Int, Double, Int)

Definition Classes
GBMParams
def terminate(weights: Array[Double], learningRate: Double, withValidation: Boolean, error: Double, verror: Double, tol: Double, numRound: Int, numTry: Int, iter: Int, instrumentation: Instrumentation): (Int, Double, Int)

Definition Classes
GBMParams
def terminate(avgl: Double, withValidation: Boolean, error: Double, verror: Double, tol: Double, numRound: Int, numTry: Int, iter: Int, instrumentation: Instrumentation, numClasses: Double = 2.0): (Int, Double, Int)

Definition Classes
BoostingParams
def terminateVal(withValidation: Boolean, error: Double, verror: Double, tol: Double, numRound: Int, numTry: Int, iter: Int, instrumentation: Instrumentation): (Int, Double, Int)

Definition Classes
BoostingParams
def toString(): String

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

Definition Classes
HasTol
def train(dataset: Dataset[_]): GBMRegressionModel

Attributes
protected
Definition Classes
GBMRegressor → Predictor
def transformSchema(schema: StructType): StructType

Definition Classes
Predictor → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String

Definition Classes
GBMRegressor → Identifiable
def updateWeights(boostWeightColName: String, lossColName: String, beta: Double, updatedBoostWeightColName: String)(df: DataFrame): DataFrame

Definition Classes
BoostingParams
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

Attributes
protected
Definition Classes
PredictorParams
final val validationIndicatorCol: Param[String]

Definition Classes
HasValidationIndicatorCol
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( ... )
def weight(beta: Double): Double

Definition Classes
BoostingParams
final val weightCol: Param[String]

Definition Classes
HasWeightCol
def withBag(withReplacement: Boolean, sampleRatio: Double, numberSamples: Int, seed: Long, bagColName: String)(df: DataFrame): DataFrame

Definition Classes
HasSubBag
def write: MLWriter

Definition Classes
GBMRegressor → MLWritable

Related Docs: object GBMRegressor | package regression

class GBMRegressor extends Predictor[Vector, GBMRegressor, GBMRegressionModel] with GBMRegressorParams with MLWritable

Instance Constructors

new GBMRegressor()

new GBMRegressor(uid: String)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

val alpha: Param[Double]

final def asInstanceOf[T0]: T0

def avgLoss(lossColName: String, boostProbaColName: String)(df: DataFrame): Double

val baseLearner: Param[EnsemblePredictorType]

def beta(avgl: Double, numClasses: Int = 2): Double

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

def clone(): AnyRef

def copy(extra: ParamMap): GBMRegressor

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 evaluateOnValidation(model: GBMClassificationModel, labelColName: String, loss: (Double, Double) ⇒ Double)(df: DataFrame): Double

def evaluateOnValidation(model: GBMRegressionModel, labelColName: String, loss: (Double, Double) ⇒ Double)(df: DataFrame): Double

def evaluateOnValidation(numClasses: Int, weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

def evaluateOnValidation(weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

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

def explainParams(): String

def extractBoostedBag(poissonProbaColName: String, seed: Long)(df: DataFrame): DataFrame

def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def extractSubBag(bagColName: String, index: Int, featuresColName: String, subspace: SubSpace)(df: DataFrame): DataFrame

final val featuresCol: Param[String]

def finalize(): Unit

def findOptimizedConst(labelColName: String, loss: (Double, Double) ⇒ Double, maxIter: Int, tol: Double)(df: DataFrame): Double

def findOptimizedConst(labelColName: String, loss: (Double, Double) ⇒ Double, grad: (Double, Double) ⇒ Double, maxIter: Int, tol: Double)(df: DataFrame): Double

def findOptimizedWeight(labelColName: String, currentPredictionColName: String, boosterPredictionColName: String, loss: (Double, Double) ⇒ Double, maxIter: Int, tol: Double)(df: DataFrame): Double

def findOptimizedWeight(labelColName: String, currentPredictionColName: String, boosterPredictionColName: String, loss: (Double, Double) ⇒ Double, grad: (Double, Double) ⇒ Double, maxIter: Int, tol: Double)(df: DataFrame): Double

def fit(dataset: Dataset[_]): GBMRegressionModel

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

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

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

def fitBaseLearner(baseLearner: EnsemblePredictorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType

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

def getAlpha: Double

def getBaseLearner: EnsemblePredictorType

final def getClass(): Class[_]

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

final def getFeaturesCol: String

final def getLabelCol: String

def getLearningRate: Double

def getLoss: String

final def getMaxIter: Int

def getNumBaseLearners: Int

def getNumFeatures(dataset: DataFrame, featuresCol: String): Int

def getNumRound: Int

def getOptimizedWeights: Boolean

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

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

final def getPredictionCol: String

def getReplacement: Boolean

def getSampleRatio: Double

final def getSeed: Long

def getSubspaceRatio: Double

final def getTol: Double

final def getValidationIndicatorCol: String

final def getWeightCol: String

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

final val labelCol: Param[String]

val learningRate: Param[Double]