LightGBMClassifier

Instance Constructors

new LightGBMClassifier()
new LightGBMClassifier(uid: String)

uid
The unique ID.

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
val baggingFraction: DoubleParam

Definition Classes
LightGBMParams
val baggingFreq: IntParam

Definition Classes
LightGBMParams
val baggingSeed: IntParam

Definition Classes
LightGBMParams
val categoricalSlotIndexes: IntArrayParam

Definition Classes
LightGBMParams
val categoricalSlotNames: StringArrayParam

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

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
LightGBMClassifier → 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
val defaultListenPort: IntParam

Definition Classes
LightGBMParams
val earlyStoppingRound: IntParam

Definition Classes
LightGBMParams
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
def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

Attributes
protected
Definition Classes
Classifier
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
val featureFraction: DoubleParam

Definition Classes
LightGBMParams
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[_]): LightGBMClassificationModel

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

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

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

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
def getBaggingFraction: Double

Definition Classes
LightGBMParams
def getBaggingFreq: Int

Definition Classes
LightGBMParams
def getBaggingSeed: Int

Definition Classes
LightGBMParams
def getCategoricalSlotIndexes: Array[Int]

Definition Classes
LightGBMParams
def getCategoricalSlotNames: Array[String]

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

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

Definition Classes
Params
def getDefaultListenPort: Int

Definition Classes
LightGBMParams
def getEarlyStoppingRound: Int

Definition Classes
LightGBMParams
def getFeatureFraction: Double

Definition Classes
LightGBMParams
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
def getIsUnbalance: Boolean
final def getLabelCol: String

Definition Classes
HasLabelCol
def getLearningRate: Double

Definition Classes
LightGBMParams
def getMaxBin: Int

Definition Classes
LightGBMParams
def getMaxDepth: Int

Definition Classes
LightGBMParams
def getMinSumHessianInLeaf: Double

Definition Classes
LightGBMParams
def getModelString: String

Definition Classes
LightGBMParams
def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int

Attributes
protected
Definition Classes
Classifier
def getNumIterations: Int

Definition Classes
LightGBMParams
def getNumLeaves: Int

Definition Classes
LightGBMParams
def getObjective: String

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

Definition Classes
Params
def getParallelism: String

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

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getProbabilityCol: String

Definition Classes
HasProbabilityCol
final def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
def getThresholds: Array[Double]

Definition Classes
HasThresholds
def getTimeout: Double

Definition Classes
LightGBMParams
def getVerbosity: Int

Definition Classes
LightGBMParams
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
val isUnbalance: BooleanParam
final val labelCol: Param[String]

Definition Classes
HasLabelCol
val learningRate: DoubleParam

Definition Classes
LightGBMParams
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 maxBin: IntParam

Definition Classes
LightGBMParams
val maxDepth: IntParam

Definition Classes
LightGBMParams
val minSumHessianInLeaf: DoubleParam

Definition Classes
LightGBMParams
val modelString: Param[String]

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numIterations: IntParam

Definition Classes
LightGBMParams
val numLeaves: IntParam

Definition Classes
LightGBMParams
val objective: Param[String]

Definition Classes
LightGBMParams
val parallelism: Param[String]

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

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
final val probabilityCol: Param[String]

Definition Classes
HasProbabilityCol
final val rawPredictionCol: Param[String]

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

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

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

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

Definition Classes
Params
def setBaggingFraction(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setBaggingFreq(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setBaggingSeed(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setCategoricalSlotIndexes(value: Array[Int]): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setCategoricalSlotNames(value: Array[String]): LightGBMClassifier.this.type

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

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

Attributes
protected
Definition Classes
Params
def setDefaultListenPort(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setEarlyStoppingRound(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setFeatureFraction(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setFeaturesCol(value: String): LightGBMClassifier

Definition Classes
Predictor
def setIsUnbalance(value: Boolean): LightGBMClassifier.this.type
def setLabelCol(value: String): LightGBMClassifier

Definition Classes
Predictor
def setLearningRate(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setMaxBin(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setMaxDepth(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setMinSumHessianInLeaf(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setModelString(value: String): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setNumIterations(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setNumLeaves(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setObjective(value: String): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setParallelism(value: String): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setPredictionCol(value: String): LightGBMClassifier

Definition Classes
Predictor
def setProbabilityCol(value: String): LightGBMClassifier

Definition Classes
ProbabilisticClassifier
def setRawPredictionCol(value: String): LightGBMClassifier

Definition Classes
Classifier
def setThresholds(value: Array[Double]): LightGBMClassifier

Definition Classes
ProbabilisticClassifier
def setTimeout(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setVerbosity(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setWeightCol(value: String): LightGBMClassifier.this.type

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

Definition Classes
AnyRef
final val thresholds: DoubleArrayParam

Definition Classes
HasThresholds
val timeout: DoubleParam

Definition Classes
LightGBMParams
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def train(dataset: Dataset[_]): LightGBMClassificationModel

Trains the LightGBM Classification model.
Trains the LightGBM Classification model.
dataset
The input dataset to train.
returns
The trained model.

Attributes
protected
Definition Classes
LightGBMClassifier → 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

The unique ID.
The unique ID.

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

Attributes
protected
Definition Classes
ProbabilisticClassifierParams → ClassifierParams → PredictorParams
val verbosity: IntParam

Definition Classes
LightGBMParams
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( ... )
val weightCol: Param[String]

The name of the weight column
The name of the weight column

Definition Classes
HasWeightCol
def write: MLWriter

Definition Classes
DefaultParamsWritable → MLWritable

Inherited from LightGBMParams

Inherited from HasWeightCol

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Wrappable

Inherited from ProbabilisticClassifier[Vector, LightGBMClassifier, LightGBMClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[Vector, LightGBMClassifier, LightGBMClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, LightGBMClassifier, LightGBMClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from org.apache.spark.ml.param.shared.HasFeaturesCol

Inherited from org.apache.spark.ml.param.shared.HasLabelCol

Inherited from Estimator[LightGBMClassificationModel]

Inherited from PipelineStage

Inherited from org.apache.spark.internal.Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters

Parameter setters

Parameter getters

Members

Related Docs: object LightGBMClassifier | package spark

class LightGBMClassifier extends ProbabilisticClassifier[Vector, LightGBMClassifier, LightGBMClassificationModel] with LightGBMParams

Instance Constructors

new LightGBMClassifier()

new LightGBMClassifier(uid: String)

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

val baggingFraction: DoubleParam

val baggingFreq: IntParam

val baggingSeed: IntParam

val categoricalSlotIndexes: IntArrayParam

val categoricalSlotNames: StringArrayParam

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

def clone(): AnyRef

def copy(extra: ParamMap): LightGBMClassifier

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

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

val defaultListenPort: IntParam

val earlyStoppingRound: IntParam

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

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

def explainParams(): String

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

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

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

val featureFraction: DoubleParam

final val featuresCol: Param[String]

def finalize(): Unit

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

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

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

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

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

def getBaggingFraction: Double

def getBaggingFreq: Int

def getBaggingSeed: Int

def getCategoricalSlotIndexes: Array[Int]

def getCategoricalSlotNames: Array[String]

final def getClass(): Class[_]

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

def getDefaultListenPort: Int

def getEarlyStoppingRound: Int

def getFeatureFraction: Double

final def getFeaturesCol: String

def getIsUnbalance: Boolean

final def getLabelCol: String

def getLearningRate: Double

def getMaxBin: Int

def getMaxDepth: Int

def getMinSumHessianInLeaf: Double

def getModelString: String

def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int

def getNumIterations: Int

def getNumLeaves: Int

def getObjective: String

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

def getParallelism: String

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

final def getPredictionCol: String

final def getProbabilityCol: String

final def getRawPredictionCol: String

def getThresholds: Array[Double]

def getTimeout: Double

def getVerbosity: Int

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