Class

com.salesforce.op.readers

ConditionalParams

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case class ConditionalParams[T](timeStampFn: (T) ⇒ Long, targetCondition: (T) ⇒ Boolean, responseWindow: Option[Duration] = ..., predictorWindow: Option[Duration] = ..., timeStampToKeep: TimeStampToKeep = TimeStampToKeep.Random, cutOffTimeFn: Option[(String, Seq[T]) ⇒ CutOffTime] = None, dropIfTargetConditionNotMet: Boolean = false) extends Product with Serializable

Conditional data reader params

timeStampFn

function for extracting the timestamp from an event

targetCondition

function for identifying if the condition is met

responseWindow

optional size of time window over which the response variable is to be aggregated

predictorWindow

optional size of time window over which the predictor variables are to be aggregated

timeStampToKeep

if a particular key met the condition multiple times, which of the times would you like to use in the training set

cutOffTimeFn

optional function to compute the cutoff value based on key and aggregated sequence of events for that key

dropIfTargetConditionNotMet

do not generate feature vectors for keys in training set where the target condition is not met. If set to false, and condition is not met, features for those

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Instance Constructors

  1. new ConditionalParams(timeStampFn: (T) ⇒ Long, targetCondition: (T) ⇒ Boolean, responseWindow: Option[Duration] = ..., predictorWindow: Option[Duration] = ..., timeStampToKeep: TimeStampToKeep = TimeStampToKeep.Random, cutOffTimeFn: Option[(String, Seq[T]) ⇒ CutOffTime] = None, dropIfTargetConditionNotMet: Boolean = false)

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    timeStampFn

    function for extracting the timestamp from an event

    targetCondition

    function for identifying if the condition is met

    responseWindow

    optional size of time window over which the response variable is to be aggregated

    predictorWindow

    optional size of time window over which the predictor variables are to be aggregated

    timeStampToKeep

    if a particular key met the condition multiple times, which of the times would you like to use in the training set

    cutOffTimeFn

    optional function to compute the cutoff value based on key and aggregated sequence of events for that key

    dropIfTargetConditionNotMet

    do not generate feature vectors for keys in training set where the target condition is not met. If set to false, and condition is not met, features for those

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  6. val cutOffTimeFn: Option[(String, Seq[T]) ⇒ CutOffTime]

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    optional function to compute the cutoff value based on key and aggregated sequence of events for that key

  7. val dropIfTargetConditionNotMet: Boolean

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    do not generate feature vectors for keys in training set where the target condition is not met.

    do not generate feature vectors for keys in training set where the target condition is not met. If set to false, and condition is not met, features for those

  8. final def eq(arg0: AnyRef): Boolean

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  15. val predictorWindow: Option[Duration]

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    optional size of time window over which the predictor variables are to be aggregated

  16. val responseWindow: Option[Duration]

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    optional size of time window over which the response variable is to be aggregated

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

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  18. val targetCondition: (T) ⇒ Boolean

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    function for identifying if the condition is met

  19. val timeStampFn: (T) ⇒ Long

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    function for extracting the timestamp from an event

  20. val timeStampToKeep: TimeStampToKeep

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    if a particular key met the condition multiple times, which of the times would you like to use in the training set

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