PerTypeModelLearner

Instance Constructors

new PerTypeModelLearner(nested: SummarizableEstimator[M])
new PerTypeModelLearner(nested: SummarizableEstimator[M], uid: String)

nested
Nested estimator or fitting.

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 clear(param: Param[_]): PerTypeModelLearner.this.type

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
PerTypeModelLearner → SummarizableEstimator → Estimator → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
def createForkSource(dataset: Dataset[_]): ForkSource[M, String, SelectingModel[M]]

Attributes
protected
Definition Classes
ForkedEstimator
def createForks(dataset: Dataset[_]): Seq[(String, DataFrame)]

Override this method and create forks to train from the data.
Override this method and create forks to train from the data.

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

Attributes
protected
Definition Classes
Params
def diveToReproContext(partialData: (String, DataFrame), estimator: SummarizableEstimator[M]): Unit

Attributes
protected
Definition Classes
ForkedEstimator
final val enableDive: BooleanParam

Definition Classes
ForkedEstimator
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
def failFast(key: String, triedIn: Try[M]): Try[M]

Attributes
protected
Definition Classes
ForkedEstimator
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def finilizeReproContext: ReproContext

Attributes
protected
Definition Classes
ForkedEstimator
def fit(dataset: Dataset[_]): SelectingModel[M]

Definition Classes
ForkedEstimator → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[SelectingModel[M]]

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

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

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
def fitFork(estimator: SummarizableEstimator[M], wholeData: Dataset[_], partialData: (String, DataFrame)): (String, Try[M])

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

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

Definition Classes
AnyRef → Any
def getCurrentContext: Seq[String]

Attributes
protected
Definition Classes
ForkedEstimator
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getForkTags(partialData: (String, DataFrame)): Seq[(String, String)]

Attributes
protected
Definition Classes
ForkedEstimator
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 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
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 logMetricsToReproContext(model: M): Unit

Attributes
protected
Definition Classes
ForkedEstimator
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
def mergeModels(sqlContext: SQLContext, models: Seq[(String, Try[M])]): SelectingModel[M]

Given models trained for each fork create a combined model.
Given models trained for each fork create a combined model. This model is the result of the estimator.

Attributes
protected
Definition Classes
PerTypeModelLearner → ForkedEstimator
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val nested: SummarizableEstimator[M]

Nested estimator to call for each fork.
Nested estimator to call for each fork.

Definition Classes
ForkedEstimator
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final val numThreads: IntParam

Definition Classes
HasNumThreads
final val overwriteModels: BooleanParam

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

Definition Classes
Params
final val pathForTempModels: Param[String]

Definition Classes
ForkedEstimator
final val persistingKeyColumns: StringArrayParam

Definition Classes
ForkedEstimator
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
final val propagatedKeyColumn: Param[String]

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

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

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

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

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

Attributes
protected
Definition Classes
Params
def setEnableDive(value: Boolean): PerTypeModelLearner.this.type

Definition Classes
ForkedEstimator
def setNumThreads(value: Int): PerTypeModelLearner.this.type

Definition Classes
HasNumThreads
def setOverwriteModels(value: Boolean): PerTypeModelLearner.this.type

Definition Classes
ForkedEstimator
def setPathForTempModels(value: String): PerTypeModelLearner.this.type

Definition Classes
ForkedEstimator
def setPredictionCol(value: String): PerTypeModelLearner.this.type
def setPropagatedKeyColumn(value: String): PerTypeModelLearner.this.type

Definition Classes
ForkedModelParams
def setTypeColumn(value: String): PerTypeModelLearner.this.type

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transformSchema(schema: StructType): StructType

Definition Classes
ForkedEstimator → PipelineStage
Annotations
@DeveloperApi()
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
final val typeColumn: Param[String]

Definition Classes
HasTypeCol
val uid: String

Definition Classes
PerTypeModelLearner → ForkedEstimator → Identifiable
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( ... )

Related Doc: package CombinedModel

class PerTypeModelLearner[M <: ModelWithSummary[M]] extends ForkedEstimator[M, String, SelectingModel[M]] with HasTypeCol with HasPredictionCol

Instance Constructors

new PerTypeModelLearner(nested: SummarizableEstimator[M])

new PerTypeModelLearner(nested: SummarizableEstimator[M], 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

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

def clone(): AnyRef

def copy(extra: ParamMap): PerTypeModelLearner[M]

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

def createForkSource(dataset: Dataset[_]): ForkSource[M, String, SelectingModel[M]]

def createForks(dataset: Dataset[_]): Seq[(String, DataFrame)]

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

def diveToReproContext(partialData: (String, DataFrame), estimator: SummarizableEstimator[M]): Unit

final val enableDive: BooleanParam

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

def failFast(key: String, triedIn: Try[M]): Try[M]

def finalize(): Unit

def finilizeReproContext: ReproContext

def fit(dataset: Dataset[_]): SelectingModel[M]

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

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

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

def fitFork(estimator: SummarizableEstimator[M], wholeData: Dataset[_], partialData: (String, DataFrame)): (String, Try[M])

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

final def getClass(): Class[_]

def getCurrentContext: Seq[String]

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

def getForkTags(partialData: (String, DataFrame)): Seq[(String, 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 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

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 logMetricsToReproContext(model: M): 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

def mergeModels(sqlContext: SQLContext, models: Seq[(String, Try[M])]): SelectingModel[M]

final def ne(arg0: AnyRef): Boolean

val nested: SummarizableEstimator[M]

final def notify(): Unit

final def notifyAll(): Unit

final val numThreads: IntParam

final val overwriteModels: BooleanParam

lazy val params: Array[Param[_]]

final val pathForTempModels: Param[String]

final val persistingKeyColumns: StringArrayParam

final val predictionCol: Param[String]

final val propagatedKeyColumn: Param[String]

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

final def set(param: String, value: Any): PerTypeModelLearner.this.type