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ai.h2o.sparkling.backend.converters

SparkDataFrameConverter

object SparkDataFrameConverter extends Logging

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  1. SparkDataFrameConverter
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  1. final def !=(arg0: Any): Boolean
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  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  14. def isTraceEnabled(): Boolean
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  15. def log: Logger
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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
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  17. def logDebug(msg: ⇒ String): Unit
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  18. def logError(msg: ⇒ String, throwable: Throwable): Unit
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  19. def logError(msg: ⇒ String): Unit
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  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
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  21. def logInfo(msg: ⇒ String): Unit
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  22. def logName: String
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  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
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  24. def logTrace(msg: ⇒ String): Unit
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  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
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  26. def logWarning(msg: ⇒ String): Unit
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  27. final def ne(arg0: AnyRef): Boolean
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  28. final def notify(): Unit
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  29. final def notifyAll(): Unit
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  30. final def synchronized[T0](arg0: ⇒ T0): T0
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  31. def toDataFrame(hc: H2OContext, fr: H2OFrame, copyMetadata: Boolean): DataFrame

    Create a Spark DataFrame from a given REST-based H2O frame.

    Create a Spark DataFrame from a given REST-based H2O frame.

    hc

    an instance of H2O context

    fr

    an instance of H2O frame

    copyMetadata

    copy H2O metadata into Spark DataFrame

    returns

    a new DataFrame definition using given H2OFrame as data source

  32. def toH2OFrame(hc: H2OContext, dataFrame: DataFrame, frameKeyName: Option[String]): H2OFrame
  33. def toString(): String
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  34. final def wait(): Unit
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  35. final def wait(arg0: Long, arg1: Int): Unit
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  36. final def wait(arg0: Long): Unit
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