scala – 从DataFrame到RDD [LabeledPoint]

import org.apache.spark.sql.{Row,SQLContext}
import org.apache.spark.sql.types.{StringType,StructField,StructType}
import org.apache.spark.ml.feature.Tokenizer
import org.apache.spark.ml.feature.HashingTF
import org.apache.spark.ml.feature.IDF

val sql = new SQLContext(sc)

// Load raw data from a TSV file
val raw = sc.textFile("data.tsv").map(_.split("t").toSeq)

// Convert the RDD to a dataframe
val schema = StructType(List(StructField("class",StringType),StructField("content",StringType)))
val dataframe = sql.createDataFrame(raw.map(row => Row(row(0),row(1))),schema)

// Tokenize
val tokenizer = new Tokenizer().setInputCol("content").setOutputCol("tokens")
val tokenized = tokenizer.transform(dataframe)

// TF-IDF
val htf = new HashingTF().setInputCol("tokens").setOutputCol("rawFeatures").setNumFeatures(500)
val tf = htf.transform(tokenized)
tf.cache
val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
val idfModel = idf.fit(tf)
val tfidf = idfModel.transform(tf)

// Create labeled points
val labeled = tfidf.map(row => LabeledPoint(row.getDouble(0),row.get(4)))

我需要使用数据帧生成令牌并创建TF-IDF功能.当我尝试将此数据帧转换为RDD [LabeledPoint]时,出现此问题.我映射数据帧行,但是Row的get方法返回一个Any类型,而不是在数据帧架构(Vector)上定义的类型.因此,我不能构建我需要训练ML模型的RDD.

计算TF-IDF后获得RDD [LabeledPoint]的最佳选择是什么？