在scala中将Spark Dataframe（带有WrappedArray）转换为RDD [labelPoint]

我是 Scala 新手，我想将 dataframe 转换为 rdd。让标签、特点转换成RDD[labelPoint]用于MLlib的输入。但我找不到处理的方法WrappedArray.

scala> test.printSchema
root
 |-- user_id: long (nullable = true)
 |-- brand_store_sn: string (nullable = true)
 |-- label: integer (nullable = true)
 |-- money_score: double (nullable = true)
 |-- normal_score: double (nullable = true)
 |-- action_score: double (nullable = true)
 |-- features: array (nullable = true)
 |    |-- element: string (containsNull = true)
 |-- flag: string (nullable = true)
 |-- dt: string (nullable = true)


scala> test.head
res21: org.apache.spark.sql.Row = [2533,10005072,1,2.0,1.0,1.0,WrappedArray(["d90_pv_1sec:1.4471580313422192", "d3_pv_1sec:0.9030899869919435", "d7_pv_1sec:0.9030899869919435", "d30_pv_1sec:1.414973347970818", "d90_pv_week_decay:1.4235871662780681", "d1_pv_1sec:0.9030899869919435", "d120_pv_1sec:1.4471580313422192"]),user_positive,20161130]

第一次-自从LabeledPoint期望向量为Doubles，我假设您还想拆分每个中的每个元素features按冒号排列的数组 (:)，并将其右侧视为双精度，例如：

 "d90_pv_1sec:1.4471580313422192" --> 1.4471580313422192

如果是这样 - 这是转换：

import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.regression.LabeledPoint

// sample data - DataFrame with label, features and other columns
val df = Seq(
  (1, Array("d90_pv_1sec:1.4471580313422192", "d3_pv_1sec:0.9030899869919435"), 4.0),
  (2, Array("d7_pv_1sec:0.9030899869919435", "d30_pv_1sec:1.414973347970818"), 5.0)
).toDF("label", "features", "ignored")

// extract relevant fields from Row and convert WrappedArray[String] into Vector:
val result = df.rdd.map(r => {
  val label = r.getAs[Int]("label")
  val featuresArray = r.getAs[mutable.WrappedArray[String]]("features")
  val features: Vector = Vectors.dense(
    featuresArray.map(_.split(":")(1).toDouble).toArray
  )
  LabeledPoint(label, features)
})

result.foreach(println)
// (1.0,[1.4471580313422192,0.9030899869919435])
// (2.0,[0.9030899869919435,1.414973347970818])

EDIT：根据澄清，现在假设输入数组中的每个项目都包含预期的index在由此产生的sparse vector:

"d90_pv_1sec:1.4471580313422192" --> index = 90; value = 1.4471580313422192

修改后的代码将是：

val vectorSize: Int = 100 // just a guess - should be the maximum index + 1

val result = df.rdd.map(r => {
  val label = r.getAs[Int]("label")
  val arr = r.getAs[mutable.WrappedArray[String]]("features").toArray
  // parse each item into (index, value) tuple to use in sparse vector
  val elements = arr.map(_.split(":")).map {
    case Array(s, d) => (s.replaceAll("d|_pv_1sec","").toInt, d.toDouble)
  }
  LabeledPoint(label, Vectors.sparse(vectorSize, elements))
})

result.foreach(println)
// (1.0,(100,[3,90],[0.9030899869919435,1.4471580313422192]))
// (2.0,(100,[7,30],[0.9030899869919435,1.414973347970818]))

NOTE: Using s.replaceAll("d|_pv_1sec","")可能有点慢，因为它分别为每个项目编译正则表达式。如果是这样的话，它可以被更快（但更丑）的替代s.replace("d", "").replace("_pv_1sec", "")它不使用正则表达式。