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https://issues.apache.org/jira/browse/MAHOUT-1837?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15270089#comment-15270089
]
ASF GitHub Bot commented on MAHOUT-1837:
----------------------------------------
Github user andrewpalumbo commented on a diff in the pull request:
https://github.com/apache/mahout/pull/228#discussion_r61988305
--- Diff:
spark/src/main/scala/org/apache/mahout/sparkbindings/drm/package.scala ---
@@ -60,19 +60,27 @@ package object drm {
val keys = data.map(t => t._1).toArray[K]
val vectors = data.map(t => t._2).toArray
- val block = if (vectors(0).isDense) {
- val block = new DenseMatrix(vectors.length, blockncol)
- var row = 0
- while (row < vectors.length) {
- block(row, ::) := vectors(row)
- row += 1
- }
+ // create the block by default as sparse.
+ // would probably be better to sample a subset of these
+ // vectors first before creating the entire matrix.
+ // so that we don't have the overhead of creating a full second
matrix in
+ // the case that the matrix is not Spars
+ val block = new DenseMatrix(vectors.length, blockncol)
+ var row = 0
+ while (row < vectors.length) {
+ block(row, ::) := vectors(row)
+ row += 1
+ }
+
+ // Test the density of the data. If the matrix does not meet the
+ // requirements for sparsity, convert the Vectors to a dense
Matrix.
+ val resBlock = if (isMatrixDense(block)) {
block
--- End diff --
currently fails this test (when testing samples of the full matrix) with a
density threshold of .3 rows/matrix containing, .30% nonZeroElements/row and a
sample size or .25 (with a minimum of one row to test):
```scala
test("DRM blockify sparse -> SRM") {
val inCoreA = sparse(
(1, 2, 3),
0 -> 3 :: 2 -> 5 :: Nil
)
val drmA = drmParallelize(inCoreA, numPartitions = 2)
(inCoreA - drmA.mapBlock() {
case (keys, block) =>
if (!block.isInstanceOf[SparseRowMatrix])
throw new AssertionError("Block must be dense.")
keys -> block
}).norm should be < 1e-4
}
```
> Sparse/Dense Matrix analysis for Matrix Multiplication
> ------------------------------------------------------
>
> Key: MAHOUT-1837
> URL: https://issues.apache.org/jira/browse/MAHOUT-1837
> Project: Mahout
> Issue Type: Improvement
> Components: Math
> Affects Versions: 0.12.0
> Reporter: Andrew Palumbo
> Assignee: Andrew Palumbo
> Fix For: 0.12.1
>
>
> In matrix multiplication, Sparse Matrices can easily turn dense and bloat
> memory, one fully dense column and one fully dense row can cause a sparse
> %*% sparse operation have a dense result.
> There are two issues here one with a quick Fix and one a bit more involved:
> # in {{ABt.Scala}} use check the `MatrixFlavor` of the combiner and use
> the flavor of the Block as the resulting Sparse or Dense matrix type:
> {code}
> val comb = if (block.getFlavor == MatrixFlavor.SPARSELIKE) {
> new SparseMatrix(prodNCol, block.nrow).t
> } else {
> new DenseMatrix(prodNCol, block.nrow).t
> }
> {code}
> a simlar check needs to be made in the {{blockify}} transformation.
>
> # More importantly, and more involved is to do an actual analysis of the
> resulting matrix data in the in-core {{mmul}} class and use a matrix of the
> appropriate Structure as a result.
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