H2OSchemaUtils

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. def appendFlattenedStructsToDataFrame(df: DataFrame, prefixForNewColumns: String): DataFrame

   

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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7. def collectElemStartPositions(maxElemSizes: Array[Int]): Array[Int]

   

   Mapping from row in Spark frame to position where to start filling in H2OFrame

8. def collectMaxElementSizes(flatDataFrame: DataFrame): Array[Int]

   

   Collect max size of each element in DataFrame.

   Collect max size of each element in DataFrame. For array -> max array size For vectors -> max vector size For simple types -> 1

   returns

   array containing size of each element

9. def collectSparseInfo(flatDataFrame: DataFrame, elemMaxSizes: Array[Int]): Array[Boolean]

   

   Returns array for all expanded elements with values true/false based on the fact whether the value is sparse vector or not

   Returns array for all expanded elements with values true/false based on the fact whether the value is sparse vector or not

   • schema is represented as list of types
   • all arrays are expanded into columns based on the longest one
   • all vectors are expanded into columns based on the longest one

   flatDataFrame

   flat data frame

   elemMaxSizes

   max sizes of each element in the dataframe

   returns

   list of types with their positions

10. def collectVectorLikeTypes(flatSchema: StructType): Seq[Int]

    

11. def createSchema[T <: Frame](f: T, copyMetadata: Boolean): StructType

    

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

    

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13. def equals(arg0: Any): Boolean

    

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14. def expandedSchema(flatSchema: StructType, elemMaxSizes: Array[Int]): Seq[StructField]

    

    Returns expanded schema

    Returns expanded schema

    • schema is represented as list of types
    • all arrays are expanded into columns based on the longest one
    • all vectors are expanded into columns based on the longest one

    flatSchema

    flat schema of spark data frame

    returns

    list of types with their positions

15. def finalize(): Unit

    

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16. def flattenDataFrame(df: DataFrame, flatSchema: StructType): DataFrame

    

17. def flattenDataFrame(df: DataFrame): DataFrame

    

18. def flattenSchema(df: DataFrame): StructType

    

19. def flattenStructsInDataFrame(df: DataFrame): DataFrame

    

20. def flattenStructsInSchema(schema: StructType, sourceColPrefix: Option[String] = None, targetColPrefix: Option[String] = None, nullable: Boolean = false): Seq[(StructField, String)]

    

21. final def getClass(): Class[_]

    

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22. def getDatasetShape(schema: StructType): DatasetShape.DatasetShape

    

23. def hashCode(): Int

    

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24. final def isInstanceOf[T0]: Boolean

    

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25. final def ne(arg0: AnyRef): Boolean

    

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26. final def notify(): Unit

    

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27. final def notifyAll(): Unit

    

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28. def rowsToRowSchemas(df: DataFrame): Dataset[ArrayBuffer[FieldWithOrder]]

    

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

    

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30. def toString(): String

    

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31. final def wait(): Unit

    

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

    

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33. final def wait(arg0: Long): Unit

    

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