LinearCombinationModel

Instance Constructors

new LinearCombinationModel(nested: Map[String, N])
new LinearCombinationModel(uid: String)
new LinearCombinationModel(nestedModels: Map[String, N], uid: String)

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
Any
final def ##(): Int

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

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

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final val classes: StringArrayParam

Definition Classes
MultiClassCombinationModelBase
final val classesColumn: Param[String]

Definition Classes
HasClassesCol
val classesWeights: JacksonParam[Map[String, Double]]
final def clear(param: Param[_]): LinearCombinationModel.this.type

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
LinearCombinationModel → ModelWithSummary → Model → Transformer → PipelineStage → Params
def copy(summary: ModelSummary, params: ParamMap): LinearCombinationModel[N]

Definition Classes
ModelWithSummary
def copy(blocks: Map[Block, DataFrame], params: ParamMap = ParamMap()): LinearCombinationModel[N]

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

Attributes
protected
Definition Classes
Params
def create(): LinearCombinationModel[N]

Attributes
protected
Definition Classes
CombinedModel → ModelWithSummary
def createPredictionMetadata(): Metadata

Definition Classes
LinearCombinationModel → MultiClassCombinationModelBase → CombinedModel
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
def descriminantColumn: Param[String]

Definition Classes
MultiClassCombinationModelBase → HasDescriminantColumn
def directTransform(data: DataFrame): Option[Column]

returns
If possible, try to evaluate prediction directly, overwise revert to chain of transformations.

Definition Classes
LinearCombinationModel → MultiClassCombinationModelBase → HasDirectTransformOption
def disableSaveSummary(): ModelWithSummary[LinearCombinationModel[N]]

Definition Classes
ModelWithSummary
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def escalateBlocks(): Unit

Attributes
protected
Definition Classes
CombinedModel
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 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 getDescriminantColumn: String

Definition Classes
HasDescriminantColumn
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
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
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 indirectTransform(dataset: DataFrame): DataFrame

In case if direct transformation is not possible, applies less efficientt indirect logic.
In case if direct transformation is not possible, applies less efficientt indirect logic.

Attributes
protected
Definition Classes
LinearCombinationModel → MultiClassCombinationModelBase → CombinedModel
final def isDefined(param: Param[_]): Boolean

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isSaveSummaryEnabled: Boolean

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

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
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
def mayBePropagateKey(data: DataFrame, key: Any): DataFrame

Attributes
protected
Definition Classes
ForkedModelParams
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def nested: Map[String, N]

Definition Classes
CombinedModel
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

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

Definition Classes
Params
var parent: Estimator[LinearCombinationModel[N]]

Definition Classes
Model
val predictCombinations: JacksonParam[Array[Map[String, Double]]]
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
final val propagatedKeyColumn: Param[String]

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

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

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

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

Definition Classes
Params
def setClassesColumn(value: String): LinearCombinationModel.this.type

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

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

Attributes
protected
Definition Classes
Params
def setParent(parent: Estimator[LinearCombinationModel[N]]): LinearCombinationModel[N]

Definition Classes
Model
def setPredictVector(value: Map[String, Double]*): LinearCombinationModel.this.type
def setPredictionCol(value: String): LinearCombinationModel.this.type

Definition Classes
CombinedModel
def setPropagatedKeyColumn(value: String): LinearCombinationModel.this.type

Definition Classes
ForkedModelParams
def summary: ModelSummary

Definition Classes
ModelWithSummary
val summaryParam: Param[ModelSummary]

Attributes
protected
Definition Classes
ModelWithSummary
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transform(dataset: DataFrame): DataFrame

Definition Classes
CombinedModel → Transformer
def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame

Definition Classes
Transformer
def transform(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Definition Classes
Transformer
Annotations
@varargs()
def transformSchema(schema: StructType): StructType

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

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

Definition Classes
LinearCombinationModel → MultiClassCombinationModelBase → Identifiable
def validateParams(): Unit

Definition Classes
Params
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: WithSummaryWriter[LinearCombinationModel[N]]

Definition Classes
CombinedModel → ModelWithSummary → MLWritable
Annotations
@Since( "1.6.0" )

class LinearCombinationModel[N <: ModelWithSummary[N]] extends MultiClassCombinationModelBase[N, LinearCombinationModel[N]]

Instance Constructors

new LinearCombinationModel(nested: Map[String, N])

new LinearCombinationModel(uid: String)

new LinearCombinationModel(nestedModels: Map[String, N], uid: String)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final val classes: StringArrayParam

final val classesColumn: Param[String]

val classesWeights: JacksonParam[Map[String, Double]]

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

def clone(): AnyRef

def copy(extra: ParamMap): LinearCombinationModel[N]

def copy(summary: ModelSummary, params: ParamMap): LinearCombinationModel[N]

def copy(blocks: Map[Block, DataFrame], params: ParamMap = ParamMap()): LinearCombinationModel[N]

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

def create(): LinearCombinationModel[N]

def createPredictionMetadata(): Metadata

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

def descriminantColumn: Param[String]

def directTransform(data: DataFrame): Option[Column]

def disableSaveSummary(): ModelWithSummary[LinearCombinationModel[N]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def escalateBlocks(): Unit

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

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val featuresCol: 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 getDescriminantColumn: String

final def getFeaturesCol: String

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

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

final def getPredictionCol: String

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

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

def indirectTransform(dataset: DataFrame): DataFrame

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

final def isInstanceOf[T0]: Boolean

def isSaveSummaryEnabled: Boolean

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

def isTraceEnabled(): Boolean

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

def mayBePropagateKey(data: DataFrame, key: Any): DataFrame

final def ne(arg0: AnyRef): Boolean

def nested: Map[String, N]

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

var parent: Estimator[LinearCombinationModel[N]]

val predictCombinations: JacksonParam[Array[Map[String, Double]]]

final val predictionCol: Param[String]

final val propagatedKeyColumn: Param[String]

def save(path: String): Unit

final def set(paramPair: ParamPair[_]): LinearCombinationModel.this.type