LightGBMRankerModel

Instance Constructors

new LightGBMRankerModel(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
def additionalPythonMethods(): String

Definition Classes
HasAdditionalPythonMethods
final def asInstanceOf[T0]: T0

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

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
LightGBMRankerModel → Model → Transformer → 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 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 featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def featuresDataType: DataType

Attributes
protected
Definition Classes
PredictionModel
def featuresShap(features: Vector): Vector

Protected method to predict local SHAP feature importance values for an instance.
Protected method to predict local SHAP feature importance values for an instance.
features
The local instance or row to compute the local SHAP values for.
returns
The SHAP local feature importance values.

Attributes
protected
Definition Classes
LightGBMModelMethods
val featuresShapCol: Param[String]

Definition Classes
LightGBMPredictionParams
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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 getDenseFeatureShaps(features: Array[Double]): Array[Double]

Public method for pyspark API to get the dense local SHAP feature importance values for an instance.
Public method for pyspark API to get the dense local SHAP feature importance values for an instance.
features
The local instance or row to compute the SHAP values for.
returns
The local feature importance values.

Definition Classes
LightGBMModelMethods
def getFeatureImportances(importanceType: String): Array[Double]

Public method to get the global feature importance values.
Public method to get the global feature importance values.
importanceType
split or gini
returns
The global feature importance values.

Definition Classes
LightGBMModelMethods
def getFeatureShaps(features: Vector): Array[Double]

Public method to get the vector local SHAP feature importance values for an instance.
Public method to get the vector local SHAP feature importance values for an instance.
features
The local instance or row to compute the SHAP values for.
returns
The local feature importance values.

Definition Classes
LightGBMModelMethods
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
def getFeaturesShapCol: String

Definition Classes
LightGBMPredictionParams
final def getLabelCol: String

Definition Classes
HasLabelCol
def getLeafPredictionCol: String

Definition Classes
LightGBMPredictionParams
def getLightGBMBooster: LightGBMBooster

Definition Classes
LightGBMModelParams
def getModel: LightGBMBooster

Alias for same method
Alias for same method
returns
The LightGBM Booster.

Definition Classes
LightGBMModelParams
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 getSparseFeatureShaps(size: Int, indices: Array[Int], values: Array[Double]): Array[Double]

Public method for pyspark API to get the sparse local SHAP feature importance values for an instance.
Public method for pyspark API to get the sparse local SHAP feature importance values for an instance.
returns
The local feature importance values.

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

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

Definition Classes
Params
def hasParent: Boolean

Definition Classes
Model
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 leafPredictionCol: Param[String]

Definition Classes
LightGBMPredictionParams
val lightGBMBooster: LightGBMBoosterParam

Definition Classes
LightGBMModelParams
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 def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def numFeatures: Int

Definition Classes
LightGBMRankerModel → PredictionModel
lazy val params: Array[Param[_]]

Definition Classes
Params
var parent: Estimator[LightGBMRankerModel]

Definition Classes
Model
def predict(features: Vector): Double

Definition Classes
LightGBMRankerModel → PredictionModel
def predictLeaf(features: Vector): Vector

Protected method to predict leaf index.
Protected method to predict leaf index.
features
The local instance or row to compute the leaf index for.
returns
The predicted leaf index.

Attributes
protected
Definition Classes
LightGBMModelMethods
final val predictionCol: Param[String]

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

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
def saveNativeModel(filename: String, overwrite: Boolean): Unit
final def set(paramPair: ParamPair[_]): LightGBMRankerModel.this.type

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

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

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

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

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

Definition Classes
PredictionModel
def setFeaturesShapCol(value: String): LightGBMRankerModel.this.type

Definition Classes
LightGBMPredictionParams
def setLeafPredictionCol(value: String): LightGBMRankerModel.this.type

Definition Classes
LightGBMPredictionParams
def setLightGBMBooster(value: LightGBMBooster): LightGBMRankerModel.this.type

Definition Classes
LightGBMModelParams
def setParent(parent: Estimator[LightGBMRankerModel]): LightGBMRankerModel

Definition Classes
Model
def setPredictionCol(value: String): LightGBMRankerModel

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transform(dataset: Dataset[_]): DataFrame

Adds additional Leaf Index and SHAP columns if specified.
Adds additional Leaf Index and SHAP columns if specified.
dataset
input dataset
returns
transformed dataset

Definition Classes
LightGBMRankerModel → PredictionModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
def transformImpl(dataset: Dataset[_]): DataFrame

Attributes
protected
Definition Classes
PredictionModel
def transformSchema(schema: StructType): StructType

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

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

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

Attributes
protected
Definition Classes
PredictorParams
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 write: MLWriter

Definition Classes
ComplexParamsWritable → MLWritable

Related Docs: object LightGBMRankerModel | package lightgbm

class LightGBMRankerModel extends RankerModel[Vector, LightGBMRankerModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with ComplexParamsWritable

Instance Constructors

new LightGBMRankerModel(uid: String)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

def additionalPythonMethods(): String

final def asInstanceOf[T0]: T0

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

def clone(): AnyRef

def copy(extra: ParamMap): LightGBMRankerModel

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 featuresCol: Param[String]

def featuresDataType: DataType

def featuresShap(features: Vector): Vector

val featuresShapCol: Param[String]

def finalize(): Unit

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

final def getClass(): Class[_]

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

def getDenseFeatureShaps(features: Array[Double]): Array[Double]

def getFeatureImportances(importanceType: String): Array[Double]

def getFeatureShaps(features: Vector): Array[Double]

final def getFeaturesCol: String

def getFeaturesShapCol: String

final def getLabelCol: String

def getLeafPredictionCol: String

def getLightGBMBooster: LightGBMBooster

def getModel: LightGBMBooster

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

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

final def getPredictionCol: String

def getSparseFeatureShaps(size: Int, indices: Array[Int], values: Array[Double]): Array[Double]

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

def hasParam(paramName: String): Boolean

def hasParent: 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 leafPredictionCol: Param[String]

val lightGBMBooster: LightGBMBoosterParam

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def numFeatures: Int

lazy val params: Array[Param[_]]

var parent: Estimator[LightGBMRankerModel]

def predict(features: Vector): Double

def predictLeaf(features: Vector): Vector

final val predictionCol: Param[String]

def save(path: String): Unit

def saveNativeModel(filename: String, overwrite: Boolean): Unit