[
https://issues.apache.org/jira/browse/MAHOUT-1833?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Edmond Luo updated MAHOUT-1833:
-------------------------------
Description:
It will be nice to enhance the existing svec function in
org.apache.mahout.math.scalabindings
{code}
/**
* create a sparse vector out of list of tuple2's
* @param sdata cardinality
* @return
*/
def svec(sdata: TraversableOnce[(Int, AnyVal)], cardinality: Int = -1) = {
val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
var tmp = -1
if (cardinality < 0) {
tmp = required
} else if (cardinality < required) {
throw new IllegalArgumentException(s"Required cardinality %required but
got %cardinality")
} else {
tmp = cardinality
}
val initialCapacity = sdata.size
val sv = new RandomAccessSparseVector(tmp, initialCapacity)
sdata.foreach(t ⇒ sv.setQuick(t._1,
t._2.asInstanceOf[Number].doubleValue()))
sv
}
{code}
So user can specify the cardinality for the created sparse vector.
This is very useful and convenient if user wants to create a DRM with many
sparse vectors and the vectors are not with the same actual size(but with the
same logical size, e.g. rows of a sparse matrix).
Below code should demonstrate the case:
{code}
var cardinality = 20
val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line =>
(line(0).toInt, Array((line(1).toInt,1)))).reduceByKey((v1, v2) => v1 ++
v2).map(row => (row._1, svec(row._2，cardinality)))
val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))
// All below element wise opperations will fail for those DRM with not
cardinality-consistent SparseVector
val drm2 = drm + drm.t
val drm3 = drm - drm.t
val drm4 = drm * drm.t
val drm5 = drm / drm.t
{code}
Notice that in the last map, the svec acceptted one more cardinality parameter,
so the cardinality of those created sparse vectors can be consistent.
was:
It will be nice to enhance the existing svec function in
org.apache.mahout.math.scalabindings
{code}
/**
* create a sparse vector out of list of tuple2's with specific
cardinality(size),
* throws IllegalArgumentException if cardinality is not bigger than required
cardinality of sdata
* @param cardinality sdata
* @return
*/
def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
if (cardinality < required) {
throw new IllegalArgumentException(s"Cardinality[%cardinality] must be
bigger than required[%required]!")
}
val initialCapacity = sdata.size
val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
sdata.foreach(t ⇒ sv.setQuick(t._1, t._2.asInstanceOf[Number].doubleValue()))
sv
}
{code}
So user can specify the cardinality for the created sparse vector.
This is very useful and convenient if user wants to create a DRM with many
sparse vectors and the vectors are not with the same actual size(but with the
same logical size, e.g. rows of a sparse matrix).
Below code should demonstrate the case:
{code}
var cardinality = 20
val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line =>
(line(0).toInt, Array((line(1).toInt,1)))).reduceByKey((v1, v2) => v1 ++
v2).map(row => (row._1, svec(row._2，cardinality)))
val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))
// All element wise opperation will fail for those DRM with not
cardinality-consistent SparseVector
val drm2 = drm + drm.t
val drm3 = drm - drm.t
val drm4 = drm * drm.t
val drm5 = drm / drm.t
{code}
Notice that in the last map, the svec acceptted one more cardinality parameter,
so the cardinality of those created SparseVector can be consistent.
> Enhance svec function to accept cardinality as parameter
> ---------------------------------------------------------
>
> Key: MAHOUT-1833
> URL: https://issues.apache.org/jira/browse/MAHOUT-1833
> Project: Mahout
> Issue Type: Improvement
> Affects Versions: 0.12.0
> Environment: Mahout Spark Shell 0.12.0,
> Spark 1.6.0 Cluster on Hadoop Yarn 2.7.1,
> Centos7 64bit
> Reporter: Edmond Luo
>
> It will be nice to enhance the existing svec function in
> org.apache.mahout.math.scalabindings
> {code}
> /**
> * create a sparse vector out of list of tuple2's
> * @param sdata cardinality
> * @return
> */
> def svec(sdata: TraversableOnce[(Int, AnyVal)], cardinality: Int = -1) = {
> val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
> var tmp = -1
> if (cardinality < 0) {
> tmp = required
> } else if (cardinality < required) {
> throw new IllegalArgumentException(s"Required cardinality %required but
> got %cardinality")
> } else {
> tmp = cardinality
> }
> val initialCapacity = sdata.size
> val sv = new RandomAccessSparseVector(tmp, initialCapacity)
> sdata.foreach(t ⇒ sv.setQuick(t._1,
> t._2.asInstanceOf[Number].doubleValue()))
> sv
> }
> {code}
> So user can specify the cardinality for the created sparse vector.
> This is very useful and convenient if user wants to create a DRM with many
> sparse vectors and the vectors are not with the same actual size(but with the
> same logical size, e.g. rows of a sparse matrix).
> Below code should demonstrate the case:
> {code}
> var cardinality = 20
> val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line =>
> (line(0).toInt, Array((line(1).toInt,1)))).reduceByKey((v1, v2) => v1 ++
> v2).map(row => (row._1, svec(row._2，cardinality)))
> val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))
> // All below element wise opperations will fail for those DRM with not
> cardinality-consistent SparseVector
> val drm2 = drm + drm.t
> val drm3 = drm - drm.t
> val drm4 = drm * drm.t
> val drm5 = drm / drm.t
> {code}
> Notice that in the last map, the svec acceptted one more cardinality
> parameter, so the cardinality of those created sparse vectors can be
> consistent.
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