[jira] [Created] (SPARK-30329) add iterator/foreach methods for Vectors

["zhengruifeng (Jira)" <ji...@apache.org>]

zhengruifeng created SPARK-30329:
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             Summary: add iterator/foreach methods for Vectors
                 Key: SPARK-30329
                 URL: https://issues.apache.org/jira/browse/SPARK-30329
             Project: Spark
          Issue Type: Wish
          Components: ML
    Affects Versions: 3.0.0
            Reporter: zhengruifeng


1, foreach: there are a lot of places that we need to traversal all the elements, current we impl like this:
{code:java}

var i = 0; while (i < vec.size) { val v = vec(i); ...; i += 1 } {code}
This method is for both convenience and performace:

For a SparseVector, the total complexity is O(size * log(nnz)), since an apply call has log(nnz) complexity due to usage of binary search;

However, this can be optimized by operations of cursors.

 

2, foreachNonZero: the usage of foreachActive is mostly binded with filter value!=0, like
{code:java}
vec.foreachActive { case (i, v) =>
  if (v != 0.0) {
    ...
  }
}
 {code}
Here we can add this method for convenience.

 

3, iterator/activeIterator/nonZeroIterator: add those three iterators, so that we can futuremore add/change some impls based on those iterators for both ml and mllib sides, to avoid vector conversions.

For example, I want to optimize PCA by using ml.stat.Summarizer instead of

Statistics.colStats/mllib.MultivariateStatisticalSummary, to avoid computation of unused variables.

After having these iterators, I can do it without vector conversions.

 



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