scala – 为什么Spark ML NaiveBayes输出的标签与训练数据不同？

这是一个可以粘贴到例如Zeppelin笔记本：

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.NaiveBayes
import org.apache.spark.ml.feature.{HashingTF,Tokenizer}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.sql.Row

// Prepare training documents from a list of (id,text,label) tuples.
val training = sqlContext.createDataFrame(Seq(
  (0L,"X totally sucks :-(",100.0),(1L,"Today was kind of meh",200.0),(2L,"I'm so happy :-)",300.0)
)).toDF("id","text","label")

// Configure an ML pipeline,which consists of three stages: tokenizer,hashingTF,and lr.
val tokenizer = new Tokenizer()
  .setInputCol("text")
  .setOutputCol("words")
val hashingTF = new HashingTF()
  .setNumFeatures(1000)
  .setInputCol(tokenizer.getOutputCol)
  .setOutputCol("features")
val nb = new NaiveBayes()

val pipeline = new Pipeline()
  .setStages(Array(tokenizer,nb))

// Fit the pipeline to training documents.
val model = pipeline.fit(training)

// Prepare test documents,which are unlabeled (id,text) tuples.
val test = sqlContext.createDataFrame(Seq(
  (4L,"roller coasters are fun :-)"),(5L,"i burned my bacon :-("),(6L,"the movie is kind of meh")
)).toDF("id","text")

// Make predictions on test documents.
model.transform(test)
  .select("id","prediction")
  .collect()
  .foreach { case Row(id: Long,text: String,prediction: Double) =>
    println(s"($id,$text) --> prediction=$prediction")
  }

小程序的输出：

(4,roller coasters are fun :-)) --> prediction=2.0
(5,i burned my bacon :-() --> prediction=0.0
(6,the movie is kind of meh) --> prediction=1.0

预测标签{0.0,1.0,2.0}的集合与我的训练集标签{100.0,200.0,300.0}不相交.

问题：如何将这些预测标签映射回原始训练集标签？

奖金问题：为什么训练集标签必须是双打,当任何其他类型的标签和标签一样好？似乎没必要.