如何将地图转换为Spark的RDD [英] How to convert a map to Spark's RDD

问题描述

我有一些嵌套地图形式的数据集，其Scala类型为:

I have a data set which is in the form of some nested maps, and its Scala type is:

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

第一个String键是每个样本的唯一标识符，其值是一个包含标签(为-1或1)和一个嵌套映射(为非零的稀疏表示)的元组.与样本相关的元素.

The first String key is a unique identifier for each sample, and the value is a tuple that contains the label (which is -1 or 1), and a nested map which is the sparse representation of the non-zero elements which are associated with the sample.

我想将此数据加载到Spark(使用MUtil)中，并训练和测试一些机器学习算法.

I would like to load this data into Spark (using MUtil) and train and test some machine learning algorithms.

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

It's easy to write this data into a file with LibSVM's sparse encoding, and then load it in Spark:

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.

我知道直接从data_map加载data变量应该很容易，但是我不知道如何.

I know it should be as easy to directly load the data variable from data_map, but I don't know how.

感谢您的帮助！

推荐答案

我想您想要这样的东西

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))
 }}

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