如何将地图转换为Spark的RDD

Question

我有一个数据集,它是一些嵌套映射的形式,其Scala类型是:

Map[String, (LabelType,Map[Int, Double])]

第一个String键是每个样本的唯一标识符,值是包含标签(-1或1)的元组,以及嵌套映射,它是与之关联的非零元素的稀疏表示.样品.

我想将这些数据加载到Spark(使用MUtil)并训练和测试一些机器学习算法.

使用LibSVM的稀疏编码将此数据写入文件很容易,然后将其加载到Spark中:

writeMapToLibSVMFile(data_map,"libsvm_data.txt") // Implemeneted some where else
val conf = new SparkConf().setAppName("DecisionTree").setMaster("local[4]")
val sc = new SparkContext(conf)

// Load and parse the data file.
val data = MLUtils.loadLibSVMFile(sc, "libsvm_data.txt")
// Split the data into training and test sets
val splits = data.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))

// Train a DecisionTree model.

我知道直接加载data变量应该很容易data_map,但我不知道如何.

任何帮助表示赞赏!

Answer 1

我想你想要这样的东西

import org.apache.spark.rdd.RDD
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint

// If you know this upfront, otherwise it can be computed
// using flatMap
// trainMap.values.flatMap(_._2.keys).max + 1
val nFeatures: Int = ??? 

val trainMap = Map(
  "x001" -> (-1, Map(0 -> 1.0, 3 -> 5.0)),
  "x002" -> (1, Map(2 -> 5.0, 3 -> 6.0)))

val trainRdd: RDD[(String, LabeledPoint)]  = sc
  // Convert Map to Seq so it can passed to parallelize
  .parallelize(trainMap.toSeq)
  .map{case (id, (labelInt, values)) => {

      // Convert nested map to Seq so it can be passed to Vector
      val features = Vectors.sparse(nFeatures, values.toSeq)

      // Convert label to Double so it can be used for LabeledPoint
      val label = labelInt.toDouble 

      (id, LabeledPoint(label, features))
 }}