如何定义自定义聚合函数来对一列向量求和？

Question

我有一个两列的DataFrame,ID类型Int和Vec类型Vector(org.apache.spark.mllib.linalg.Vector).

DataFrame如下所示:

ID,Vec
1,[0,0,5]
1,[4,0,1]
1,[1,2,1]
2,[7,5,0]
2,[3,3,4]
3,[0,8,1]
3,[0,0,1]
3,[7,7,7]
....

我想groupBy($"ID")通过对向量求和来对每个组内的行应用聚合.

上述示例的所需输出将是:

ID,SumOfVectors
1,[5,2,7]
2,[10,8,4]
3,[7,15,9]
...

可用的聚合函数将不起作用,例如,df.groupBy($"ID").agg(sum($"Vec")将导致ClassCastException.

如何实现自定义聚合函数,允许我进行向量或数组的总和或任何其他自定义操作？

Answer 1

我个人不打扰UDAF.不仅仅是冗长而且不是很快(带有ArrayType的Spark UDAF作为bufferSchema性能问题)相反,我只想使用reduceByKey/ foldByKey:

import org.apache.spark.sql.Row
import breeze.linalg.{DenseVector => BDV}
import org.apache.spark.ml.linalg.{Vector, Vectors}

def dv(values: Double*): Vector = Vectors.dense(values.toArray)

val df = spark.createDataFrame(Seq(
    (1, dv(0,0,5)), (1, dv(4,0,1)), (1, dv(1,2,1)),
    (2, dv(7,5,0)), (2, dv(3,3,4)), 
    (3, dv(0,8,1)), (3, dv(0,0,1)), (3, dv(7,7,7)))
  ).toDF("id", "vec")

val aggregated = df
  .rdd
  .map{ case Row(k: Int, v: Vector) => (k, BDV(v.toDense.values)) }
  .foldByKey(BDV.zeros[Double](3))(_ += _)
  .mapValues(v => Vectors.dense(v.toArray))
  .toDF("id", "vec")

aggregated.show

// +---+--------------+
// | id|           vec|
// +---+--------------+
// |  1| [5.0,2.0,7.0]|
// |  2|[10.0,8.0,4.0]|
// |  3|[7.0,15.0,9.0]|
// +---+--------------+

仅仅为了比较一个"简单"的UDAF.所需进口:

import org.apache.spark.sql.expressions.{MutableAggregationBuffer,
  UserDefinedAggregateFunction}
import org.apache.spark.ml.linalg.{Vector, Vectors, SQLDataTypes}
import org.apache.spark.sql.types.{StructType, ArrayType, DoubleType}
import org.apache.spark.sql.Row
import scala.collection.mutable.WrappedArray

班级定义:

class VectorSum (n: Int) extends UserDefinedAggregateFunction {
    def inputSchema = new StructType().add("v", SQLDataTypes.VectorType)
    def bufferSchema = new StructType().add("buff", ArrayType(DoubleType))
    def dataType = SQLDataTypes.VectorType
    def deterministic = true 

    def initialize(buffer: MutableAggregationBuffer) = {
      buffer.update(0, Array.fill(n)(0.0))
    }

    def update(buffer: MutableAggregationBuffer, input: Row) = {
      if (!input.isNullAt(0)) {
        val buff = buffer.getAs[WrappedArray[Double]](0) 
        val v = input.getAs[Vector](0).toSparse
        for (i <- v.indices) {
          buff(i) += v(i)
        }
        buffer.update(0, buff)
      }
    }

    def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = {
      val buff1 = buffer1.getAs[WrappedArray[Double]](0) 
      val buff2 = buffer2.getAs[WrappedArray[Double]](0) 
      for ((x, i) <- buff2.zipWithIndex) {
        buff1(i) += x
      }
      buffer1.update(0, buff1)
    }

    def evaluate(buffer: Row) =  Vectors.dense(
      buffer.getAs[Seq[Double]](0).toArray)
} 

并举例说明:

df.groupBy($"id").agg(new VectorSum(3)($"vec") alias "vec").show

// +---+--------------+
// | id|           vec|
// +---+--------------+
// |  1| [5.0,2.0,7.0]|
// |  2|[10.0,8.0,4.0]|
// |  3|[7.0,15.0,9.0]|
// +---+--------------+

另请参阅:如何在Spark SQL中查找已分组的Vector列的平均值？.