KMeans ||用于Spark上的情绪分析

我正在尝试编写基于Spark的情绪分析程序。为此,我使用了word2vec和KMeans聚类。从word2Vec,我在100维空间中收集了20k个单词/向量,现在我正在尝试对这个向量空间进行聚类。当我用默认的并行实现运行KMeans时,算法工作了3个小时!但在随机初始化策略下,这就像是8分钟。 我做错了什么?我有4个内核处理器和16 GB RAM的mac book pro机器。

K~=4000 最大交互为20

var vectors: Iterable[org.apache.spark.mllib.linalg.Vector] =
      model.getVectors.map(entry => new VectorWithLabel(entry._1, entry._2.map(_.toDouble)))
    val data = sc.parallelize(vectors.toIndexedSeq).persist(StorageLevel.MEMORY_ONLY_2)
    log.info("Clustering data size {}",data.count())
    log.info("==================Train process started==================");
    val clusterSize = modelSize/5

    val kmeans = new KMeans()
    kmeans.setInitializationMode(KMeans.K_MEANS_PARALLEL)
    kmeans.setK(clusterSize)
    kmeans.setRuns(1)
    kmeans.setMaxIterations(50)
    kmeans.setEpsilon(1e-4)

    time = System.currentTimeMillis()
    val clusterModel: KMeansModel = kmeans.run(data)

spark上下文初始化如下:

val conf = new SparkConf()
      .setAppName("SparkPreProcessor")
      .setMaster("local[4]")
      .set("spark.default.parallelism", "8")
      .set("spark.executor.memory", "1g")
    val sc = SparkContext.getOrCreate(conf)

关于运行此程序的更新也很少。我在Intelij IDEA内部运行。我没有真正的Spark集群。但我想你的个人机器可以是Spark集群

我看到程序挂在Spark代码LocalKMeans.scala的循环中:

// Initialize centers by sampling using the k-means++ procedure.
    centers(0) = pickWeighted(rand, points, weights).toDense
    for (i <- 1 until k) {
      // Pick the next center with a probability proportional to cost under current centers
      val curCenters = centers.view.take(i)
      val sum = points.view.zip(weights).map { case (p, w) =>
        w * KMeans.pointCost(curCenters, p)
      }.sum
      val r = rand.nextDouble() * sum
      var cumulativeScore = 0.0
      var j = 0
      while (j < points.length && cumulativeScore < r) {
        cumulativeScore += weights(j) * KMeans.pointCost(curCenters, points(j))
        j += 1
      }
      if (j == 0) {
        logWarning("kMeansPlusPlus initialization ran out of distinct points for centers." +
          s" Using duplicate point for center k = $i.")
        centers(i) = points(0).toDense
      } else {
        centers(i) = points(j - 1).toDense
      }
    }