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I have pageranks result from ParallelPersonalizedPageRank in Graphframes, which is a DataFrame with each element as sparseVector as following:

+---------------------------------------+
|           pageranks                   |
+---------------------------------------+
|(1887,[0, 1, 2,...][0.1, 0.2, 0.3, ...]|
|(1887,[0, 1, 2,...][0.2, 0.3, 0.4, ...]|
|(1887,[0, 1, 2,...][0.3, 0.4, 0.5, ...]|
|(1887,[0, 1, 2,...][0.4, 0.5, 0.6, ...]|
|(1887,[0, 1, 2,...][0.5, 0.6, 0.7, ...]|

What is the best way to add all the element of the sparseVector and generatre a sum or average? I suppose we can converter each sparseVector to denseVector with toArray and traverse each array to get the result with two nested loop, and get some thing like this:

+-----------+
|pageranks  |
+-----------+
|avg1|
|avg2|
|avg3|
|avg4|
|avg5|
|... |

I am sure there should be better way, but I could not find much on the API docs about sparseVector operation. Thanks!

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1 回答 1

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我想我找到了一个没有收集（实现）结果并在 Scala 中执行嵌套循环的解决方案。只是在这里发帖以防对其他人有帮助。

// convert Dataset element from SparseVector to Array
val ranksNursingArray = ranksNursing.vertices
  .orderBy("id")
  .select("pageranks")
  .map(r => 
  r(0).asInstanceOf[org.apache.spark.ml.linalg.SparseVector].toArray)
// Find average value of pageranks and add a column to DataFrame
val ranksNursingAvg = ranksNursingArray
  .map{case (value) => (value, value.sum/value.length)}
  .toDF("pageranks", "pr-avg")

最终结果如下所示：

+--------------------+--------------------+                                     
|           pageranks|              pr-avg|
+--------------------+--------------------+
|[1.52034575371428...|2.970332668789975E-5|
|[0.0, 0.0, 0.0, 0...|5.160299770346173E-6|
|[0.0, 0.0, 0.0, 0...|4.400537827779479E-6|
|[0.0, 0.0, 0.0, 0...|3.010621958524792...|
|[0.0, 0.0, 4.8987...|2.342424435412115E-5|
|[0.0, 0.0, 1.6895...|6.955151139681538E-6|
|[0.0, 0.0, 1.5669...| 5.47016001804886E-6|
|[0.0, 0.0, 0.0, 2...|2.303811469709906E-5|
|[0.0, 0.0, 0.0, 3...|1.985155979369427E-5|
|[0.0, 0.0, 0.0, 0...|1.411993797780601...|
+--------------------+--------------------+
于 2018-04-05T01:11:11.700 回答