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case class SampledData(numRows: Int, numCols: Int) extends Product with Serializable

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)

Value Members

  1. def delete(): Unit
  2. def getRowCounts: SWIGTYPE_p_int
  3. def getSampleData: SWIGTYPE_p_p_double
  4. def getSampleIndices: SWIGTYPE_p_p_int
  5. val numCols: Int
  6. val numRows: Int
  7. def pushRow(rowData: SparseVector, index: Int): Unit
  8. def pushRow(rowData: Array[Double], index: Int): Unit
  9. def pushRow(rowData: DenseVector, index: Int): Unit
  10. def pushRow(rowData: Row, index: Int, featureColName: String): Unit
  11. val rowCounts: IntSwigArray
  12. val sampleData: DoublePointerSwigArray
  13. val sampleIndexes: IntPointerSwigArray