Packages

class SampledData extends AnyRef

SampledData: Encapsulates the sampled data need to initialize a LightGBM dataset. . LightGBM expects sampled data to be an array of vectors, where each feature column has a sparse representation of non-zero values (i.e. indexes and data vector). It also needs a #features sized array of element count per feature to know how long each column is. . Since we create sampled data as a self-contained set with ONLY sampled data and nothing else, the indexes are trivial (0 until #elements). We don't need to maintain original raw indexes. LightGBM only uses this data to get distributions, and does not care about raw row indexes. . This class manages keeping all the indexing in sync so callers can just push rows of data into it and retrieve the resulting pointers at the end. . Note: sample data row count is not expected to exceed max(Int), so we index with Ints.

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Instance Constructors

  1. new SampledData(numRows: Int, numCols: Int)

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  10. final def getClass(): Class[_]
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  11. def getRowCounts: SWIGTYPE_p_int
  12. def getSampleData: SWIGTYPE_p_p_double
  13. def getSampleIndices: SWIGTYPE_p_p_int
  14. def hashCode(): Int
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  18. final def notifyAll(): Unit
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  19. val numCols: Int
  20. val numRows: Int
  21. def pushRow(rowData: SparseVector, index: Int): Unit
  22. def pushRow(rowData: Array[Double], index: Int): Unit
  23. def pushRow(rowData: DenseVector, index: Int): Unit
  24. def pushRow(rowData: Row, index: Int, featureColName: String): Unit
  25. def pushRowElementIfNotZero(col: Int, value: Double, index: Int): Unit
  26. val rowCounts: IntSwigArray
  27. val sampleData: DoublePointerSwigArray
  28. val sampleIndexes: IntPointerSwigArray
  29. final def synchronized[T0](arg0: ⇒ T0): T0
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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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