c

com.microsoft.azure.synapse.ml.lightgbm

BasePartitionTask

abstract class BasePartitionTask extends Serializable with Logging

Class for handling the execution of Tasks on workers for each partition. Only runs on worker Tasks.

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Instance Constructors

  1. new BasePartitionTask()

Abstract Value Members

  1. abstract def generateFinalDatasetInternal(ctx: PartitionTaskContext, dataState: PartitionDataState, forValidation: Boolean, referenceDataset: Option[LightGBMDataset]): LightGBMDataset

    Generate the final dataset for this task.

    Generate the final dataset for this task. Internal implementation for specific execution modes.

    ctx

    The training context.

    dataState

    Any intermediate data state (used mainly by bulk execution mode).

    forValidation

    Whether to generate the final training dataset or the validation dataset.

    referenceDataset

    A reference dataset to start with (used mainly for validation dataset).

    returns

    LightGBM dataset Java wrapper.

    Attributes
    protected
  2. abstract def preparePartitionDataInternal(ctx: PartitionTaskContext, inputRows: Iterator[Row]): PartitionDataState

    Prepare any data objects for this particular partition.

    Prepare any data objects for this particular partition. Implement for specific execution modes.

    ctx

    The task context information.

    inputRows

    The Spark rows for a partition as an iterator.

    returns

    Any intermediate data state (used mainly by bulk execution mode) to pass to future stages.

    Attributes
    protected

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def cleanupInternal(ctx: PartitionTaskContext): Unit

    Cleanup the task

    Cleanup the task

    ctx

    The training context.

    Attributes
    protected
  6. def clone(): AnyRef
    Attributes
    protected[lang]
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    @throws( ... ) @native()
  7. def determineMatrixType(ctx: PartitionTaskContext, inputRows: Iterator[Row]): PeekingIterator[Row]
    Attributes
    protected
  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: Any): Boolean
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  10. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]
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  12. def getTaskContext(trainingCtx: TrainingContext, partitionId: Int, taskId: Long, measures: TaskInstrumentationMeasures, networkTopologyInfo: NetworkTopologyInfo, shouldExecuteTraining: Boolean, isEmptyPartition: Boolean, shouldReturnBooster: Boolean): PartitionTaskContext

    Initialize and customize the context for the task.

    Initialize and customize the context for the task.

    trainingCtx

    The training context information.

    partitionId

    The task context information.

    taskId

    The task context information.

    measures

    The task instrumentation measures.

    networkTopologyInfo

    Information about the network.

    shouldExecuteTraining

    Whether this task should participate in LightGBM training.

    isEmptyPartition

    Whether the partition has rows.

    shouldReturnBooster

    Whether the task should return a booster.

    returns

    The updated context information for the task.

    Attributes
    protected
  13. def hashCode(): Int
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    @native()
  14. def initializeInternal(ctx: TrainingContext, shouldExecuteTraining: Boolean, isEmptyPartition: Boolean): Unit

    Initialize and customize the context for the task.

    Initialize and customize the context for the task.

    ctx

    The task context information.

    returns

    The updated context information for the task.

    Attributes
    protected
  15. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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    protected
    Definition Classes
    Logging
  16. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  17. final def isInstanceOf[T0]: Boolean
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  18. def isTraceEnabled(): Boolean
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  19. def log: Logger
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  20. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
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  21. def logDebug(msg: ⇒ String): Unit
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  22. def logError(msg: ⇒ String, throwable: Throwable): Unit
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  23. def logError(msg: ⇒ String): Unit
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  24. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
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  25. def logInfo(msg: ⇒ String): Unit
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  26. def logName: String
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  27. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
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  28. def logTrace(msg: ⇒ String): Unit
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  29. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
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  30. def logWarning(msg: ⇒ String): Unit
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  31. def mapPartitionTask(ctx: TrainingContext)(inputRows: Iterator[Row]): Iterator[PartitionResult]

    This method will be passed to Spark's mapPartition method and handle execution of training on the workers.

    This method will be passed to Spark's mapPartition method and handle execution of training on the workers. Main stages: (and each execution mode has an "Internal" version to perform mode-specific operations) initialize() preparePartitionData() finalizeDatasetAndTrain() cleanup()

    ctx

    The training context.

    inputRows

    The Spark rows as an iterator.

    returns

    result iterator (to comply with Spark mapPartition API).

  32. final def ne(arg0: AnyRef): Boolean
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  33. final def notify(): Unit
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    @native()
  34. final def notifyAll(): Unit
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  35. final def synchronized[T0](arg0: ⇒ T0): T0
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  36. def toString(): String
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  37. final def wait(): Unit
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    @throws( ... )
  38. final def wait(arg0: Long, arg1: Int): Unit
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  39. final def wait(arg0: Long): Unit
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