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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.  def apply(keyTags: Seq[Int], keyTagList: Seq[String], df: DataFrame, derivedFeature: DerivedFeature, derivationFunction: FeatureDerivationFunction, mvelContext: Option[FeathrExpressionExecutionContext]): DataFrame

   Calculate a Row-based derived features such as Mvel based derivations or UDFs.

   Calculate a Row-based derived features such as Mvel based derivations or UDFs. High-level approach: - DerivedFeature metadata is used to walkthrough the dependent features for a derived feature. - DataFrameColName utility is used to generate the column names for the dependent features. - CoercionUtils utility is used to convert the column values to FeatureValue. - DerivedFeatureEvaluator.evaluateFromFeatureValues utility method is used to apply the derivation function. - Use Spark Row library to build a DataFrame row and merge different rows.

   keyTags

   key tags of the current join stage

   keyTagList

   all key tags of the current feature join job

   df

   input context dataframe

   derivedFeature

   derived feature to calculate

   derivationFunction

   derivation function to be applied

   returns

   context dataframe with the derived feature column appended

   Definition Classes

   RowBasedDerivation → DerivationStrategy

5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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7.  final def eq(arg0: AnyRef): Boolean

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9.  def finalize(): Unit

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10.  final def getClass(): Class[_]

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11.  def hashCode(): Int

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12.  final def isInstanceOf[T0]: Boolean

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13.  val mvelContext: Option[FeathrExpressionExecutionContext]
14.  final def ne(arg0: AnyRef): Boolean

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16.  final def notifyAll(): Unit

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

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21.  final def wait(arg0: Long): Unit

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