Peng Meng created SPARK-21680:
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Summary: ML/MLLIB Vector compressed optimization
Key: SPARK-21680
URL: https://issues.apache.org/jira/browse/SPARK-21680
Project: Spark
Issue Type: Improvement
Components: ML, MLlib
Affects Versions: 2.3.0
Reporter: Peng Meng
When use Vector.compressed to change a Vector to SparseVector, the performance
is very low comparing with Vector.toSparse.
This is because you have to scan the value three times using Vector.compressed,
but you just need two times when use Vector.toSparse.
When the length of the vector is large, there is significant performance
difference between this two method.
Code of Vector compressed:
{code:java}
def compressed: Vector = {
val nnz = numNonzeros
// A dense vector needs 8 * size + 8 bytes, while a sparse vector needs 12
* nnz + 20 bytes.
if (1.5 * (nnz + 1.0) < size) {
toSparse
} else {
toDense
}
}
{code}
I propose to change it to:
{code:java}
// Some comments here
def compressed: Vector = {
val nnz = numNonzeros
// A dense vector needs 8 * size + 8 bytes, while a sparse vector needs 12
* nnz + 20 bytes.
if (1.5 * (nnz + 1.0) < size) {
val ii = new Array[Int](nnz)
val vv = new Array[Double](nnz)
var k = 0
foreachActive { (i, v) =>
if (v != 0) {
ii(k) = i
vv(k) = v
k += 1
}
}
new SparseVector(size, ii, vv)
} else {
toDense
}
}
{code}
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