Github user johnc1231 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17459#discussion_r111302391
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
---
@@ -108,8 +108,64 @@ class IndexedRowMatrix @Since("1.0.0") (
*/
@Since("1.3.0")
def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
- // TODO: This implementation may be optimized
- toCoordinateMatrix().toBlockMatrix(rowsPerBlock, colsPerBlock)
+ require(rowsPerBlock > 0,
+ s"rowsPerBlock needs to be greater than 0. rowsPerBlock:
$rowsPerBlock")
+ require(colsPerBlock > 0,
+ s"colsPerBlock needs to be greater than 0. colsPerBlock:
$colsPerBlock")
+
+ val m = numRows()
+ val n = numCols()
+ val lastRowBlockIndex = m / rowsPerBlock
+ val lastColBlockIndex = n / colsPerBlock
+ val lastRowBlockSize = (m % rowsPerBlock).toInt
+ val lastColBlockSize = (n % colsPerBlock).toInt
+ val numRowBlocks = math.ceil(m.toDouble / rowsPerBlock).toInt
+ val numColBlocks = math.ceil(n.toDouble / colsPerBlock).toInt
+
+ val blocks = rows.flatMap { ir: IndexedRow =>
+ val blockRow = ir.index / rowsPerBlock
+ val rowInBlock = ir.index % rowsPerBlock
+
+ ir.vector match {
+ case SparseVector(size, indices, values) =>
+ indices.zip(values).map { case (index, value) =>
+ val blockColumn = index / colsPerBlock
+ val columnInBlock = index % colsPerBlock
+ ((blockRow.toInt, blockColumn.toInt), (rowInBlock.toInt,
Array((value, columnInBlock))))
+ }
+ case DenseVector(values) =>
+ values.grouped(colsPerBlock)
+ .zipWithIndex
+ .map { case (values, blockColumn) =>
+ ((blockRow.toInt, blockColumn), (rowInBlock.toInt,
values.zipWithIndex))
+ }
+ }
+ }.groupByKey(GridPartitioner(numRowBlocks, numColBlocks,
rows.getNumPartitions)).map {
+ case ((blockRow, blockColumn), itr) =>
+ val actualNumRows =
+ if (blockRow == lastRowBlockIndex) lastRowBlockSize else
rowsPerBlock
+ val actualNumColumns =
+ if (blockColumn == lastColBlockIndex) lastColBlockSize else
colsPerBlock
+
+ val arraySize = actualNumRows * actualNumColumns
+ val matrixAsArray = new Array[Double](arraySize)
+ var countForValues = 0
+ itr.foreach { case (rowWithinBlock, valuesWithColumns) =>
+ valuesWithColumns.foreach { case (value, columnWithinBlock) =>
+ matrixAsArray.update(columnWithinBlock * actualNumRows +
rowWithinBlock, value)
+ countForValues += 1
+ }
+ }
+ val denseMatrix = new DenseMatrix(actualNumRows, actualNumColumns,
matrixAsArray)
+ val finalMatrix = if (countForValues / arraySize.toDouble >= 0.5) {
--- End diff --
Well, our main operations on block matrix are add and multiply. I guess a
reasonable thing to do would be to profile those operations with different
fractions besides .5 and see what's most performant
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