如何定义自定义聚合函数来对一列Vectors求和

我有两列的DataFrame，类型为Int的ID和类型为Vector（org.apache.spark.mllib.linalg.Vector）的Vec。

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]
....

我想做一个groupBy($"ID")，然后通过对向量求和来对每组内的行应用聚合。

上述示例的期望输出为：

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。

如何实现一个自定义聚合函数，允许我进行向量或数组的求和或任何其他自定义操作？

Spark>=3.0

您可以将Summarizer与sum 一起使用

import org.apache.spark.ml.stat.Summarizer
df
  .groupBy($"id")
  .agg(Summarizer.sum($"vec").alias("vec"))

火花<=3.0

就我个人而言，我不会为UDAF而烦恼。这里有很多冗长而不完全快速的问题（使用ArrayType作为缓冲模式的Spark UDAF性能问题）。相反，我会简单地使用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]|
// +---+--------------+

只是为了进行比较，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

类别定义：

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)
}

还有一个用法示例：

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中找到分组Vector列的平均值？。

我建议如下（适用于Spark 2.0.2以上版本），它可能会被优化，但非常好，你必须提前知道的一件事是创建UDAF实例时的矢量大小

import org.apache.spark.ml.linalg._
import org.apache.spark.mllib.linalg.WeightedSparseVector
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types._
class VectorAggregate(val numFeatures: Int)
   extends UserDefinedAggregateFunction {
private type B = Map[Int, Double]
def inputSchema: StructType = StructType(StructField("vec", new VectorUDT()) :: Nil)
def bufferSchema: StructType =
StructType(StructField("agg", MapType(IntegerType, DoubleType)) :: Nil)
def initialize(buffer: MutableAggregationBuffer): Unit =
buffer.update(0, Map.empty[Int, Double])
def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
    val zero = buffer.getAs[B](0)
    input match {
        case Row(DenseVector(values)) => buffer.update(0, values.zipWithIndex.foldLeft(zero){case (acc,(v,i)) => acc.updated(i, v + acc.getOrElse(i,0d))})
        case Row(SparseVector(_, indices, values)) => buffer.update(0, values.zip(indices).foldLeft(zero){case (acc,(v,i)) => acc.updated(i, v + acc.getOrElse(i,0d))}) }}
def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
val zero = buffer1.getAs[B](0)
buffer1.update(0, buffer2.getAs[B](0).foldLeft(zero){case (acc,(i,v)) => acc.updated(i, v + acc.getOrElse(i,0d))})}
def deterministic: Boolean = true
def evaluate(buffer: Row): Any = {
    val Row(agg: B) = buffer
    val indices = agg.keys.toArray.sorted
    Vectors.sparse(numFeatures,indices,indices.map(agg)).compressed
}
def dataType: DataType = new VectorUDT()
}

使用我的版本pyspark3.0.0，您可以使用摘要生成器轻松完成此操作。您的col需要是DenseVector类型

from pyspark.ml.stat import Summarizer
sdf.groupBy("ID").agg(Summarizer.mean(sdf.Vec)).show()

注意：pyspark中没有avg函数，但您可以使用mean方法

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