PerClassModelLearner

abstract def copy(extra: ParamMap): SummarizableEstimator[M]

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

Given models trained for each fork create a combined model.

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 val classesColumn: Param[String]

final val classesMap: JacksonParam[Map[String, String]]

final val classesToIgnore: JacksonParam[Set[String]]

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

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

def clone(): AnyRef

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

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

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

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

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

def diveToReproContext(partialData: (String, DataFrame), estimator: SummarizableEstimator[N]): 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[N]): Try[N]

def finalize(): Unit

def finilizeReproContext: ReproContext

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

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

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

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

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

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

final def getClass(): Class[_]

def getClassesWeights: Map[String, Double]

def getCurrentContext: Seq[String]

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

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

final def getLabelCol: 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

final val labelCol: Param[String]

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: N): 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

val nested: SummarizableEstimator[N]

Nested estimator to call for each fork.

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[_]): PerClassModelLearner.this.type

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

final def set[T](param: Param[T], value: T): PerClassModelLearner.this.type

def setClassesColumn(value: String): PerClassModelLearner.this.type

def setClassesMap(values: (String, String)*): PerClassModelLearner.this.type

def setClassesToIgnore(values: String*): PerClassModelLearner.this.type

def setClassesWeights(weights: (String, Double)*): PerClassModelLearner.this.type

final def setDefault(paramPairs: ParamPair[_]*): PerClassModelLearner.this.type

final def setDefault[T](param: Param[T], value: T): PerClassModelLearner.this.type

def setEnableDive(value: Boolean): PerClassModelLearner.this.type

def setNumThreads(value: Int): PerClassModelLearner.this.type

def setOverwriteModels(value: Boolean): PerClassModelLearner.this.type

def setPathForTempModels(value: String): PerClassModelLearner.this.type

def setPredictionCol(value: String): PerClassModelLearner.this.type

def setPropagatedKeyColumn(value: String): PerClassModelLearner.this.type

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def transformSchema(schema: StructType): StructType

def transformSchema(schema: StructType, logging: Boolean): StructType

val uid: String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit