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在对一些稀疏向量进行聚类后，我需要在每个聚类中找到交集向量。为了实现这一点，我尝试减少 MLlib 向量，如下例所示：

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors

//For Sparse Vector
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.mllib.linalg.{Vector, Vectors}

object Recommend {

  def main(args: Array[String]) {
    // set up environment
    val conf = new SparkConf()
      .setAppName("Test")
      .set("spark.executor.memory", "2g")
    val sc = new SparkContext(conf)

    // Some vectors
    val vLen = 1800
    val sv11: Vector = Vectors.sparse(vLen,Seq( (100,1.0), (110,1.0), (120,1.0), (130, 1.0) ))
    val sv12: Vector = Vectors.sparse(vLen,Seq( (100,1.0), (110,1.0), (120,1.0), (130, 1.0), (140, 1.0)  ))
    val sv13: Vector = Vectors.sparse(vLen,Seq( (100,1.0), (120,1.0), (130,1.0) ))
    val sv14: Vector = Vectors.sparse(vLen,Seq( (110,1.0), (130, 1.0) ))
    val sv15: Vector = Vectors.sparse(vLen,Seq( (140, 1.0) ))

    val sv21: Vector = Vectors.sparse(vLen,Seq( (200,1.0), (210,1.0), (220,1.0), (230, 1.0) ))
    val sv22: Vector = Vectors.sparse(vLen,Seq( (200,1.0), (210,1.0), (220,1.0), (230, 1.0), (240, 1.0)  ))
    val sv23: Vector = Vectors.sparse(vLen,Seq( (200,1.0), (220,1.0), (230,1.0) ))
    val sv24: Vector = Vectors.sparse(vLen,Seq( (210,1.0), (230, 1.0) ))
    val sv25: Vector = Vectors.sparse(vLen,Seq( (240, 1.0) ))

    val sv31: Vector = Vectors.sparse(vLen,Seq( (300,1.0), (310,1.0), (320,1.0), (330, 1.0) ))
    val sv32: Vector = Vectors.sparse(vLen,Seq( (300,1.0), (310,1.0), (320,1.0), (330, 1.0), (340, 1.0)  ))
    val sv33: Vector = Vectors.sparse(vLen,Seq( (300,1.0), (320,1.0), (330,1.0) ))
    val sv34: Vector = Vectors.sparse(vLen,Seq( (310,1.0), (330, 1.0) ))
    val sv35: Vector = Vectors.sparse(vLen,Seq( (340, 1.0) ))

    val sparseData = sc.parallelize(Seq(
        sv11, sv12, sv13, sv14, sv15,
        sv21, sv22, sv23, sv24, sv25,
        sv31, sv32, sv33, sv34, sv35
        ))

    // Cluster the data into two classes using KMeans
    val numClusters = 3
    val numIterations = 20

    test(numClusters, numIterations, sparseData)
  }

  def test(numClusters:Int, numIterations:Int,
      data: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector]) = {

    val clusters = KMeans.train(data, numClusters, numIterations)

    val predictions = data.map(v => (clusters.predict(v), v) )

    predictions.reduceByKey((v1, v2) => v1)

  }
}

该行predictions.reduceByKey((v1, v2) => v1)导致错误：

value reduceByKey is not a member of org.apache.spark.rdd.RDD[(Int, org.apache.spark.mllib.linalg.Vector)]

这是什么原因？

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1 回答 1

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正如您已经猜到的那样，您的代码应该具有此导入添加：

import org.apache.spark.SparkContext._

为什么 ？因为随之而来的是一些隐式转换，主要重要的（对于您的情况）是PairRDD隐式转换。Spark 会猜测你什么时候有一个RDD左侧Tuple可以被认为是一个键，因此会给你一些方便的转换或操作，如reduceByKey.

问候，

于 2015-03-03T18:12:59.133 回答