「报错」Spark: scala.MatchError (of class org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema

场景：

  多分类

出错代码：

/** 词向量映射*/
val hashingTF = new HashingTF().setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(500).
  transform(DF_classAndDoc)
/** 计算逆向文本频率 */
val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
val rescaled = idf.fit(hashingTF).//对每个单词计算逆文本频率
  transform(hashingTF)//转换词频向量为TF-IDF向量

/** 转化DF为训练模型RDDArray[Double]*/
val labelAndFeaturesRDD = rescaled.select($"label", $"features").rdd.map{
  case Row(label: String, features: Vector) =>
    LabeledPoint(label.toDouble, features) // features.toDense
}
labelAndFeaturesRDD

说明：

LabeledPoint（） 是 mllib 中的方法，如上使用的是spark-2.1.0的 ML 包，IDF计算所得为：org.apache.spark.ml.linalg.Vector类型  。 所以会报类型不匹配错误。

spark2 与 spark1 不兼容， 测试spark-1.6.3 如上代码可行，无错， 

解决：

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.linalg.{Vector => mlV}
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
import org.apache.spark.sql.Row

// Prepare training data from a list of (id, text, label) tuples.
val training = spark.createDataFrame(Seq(
  (0L, "a b c d e spark", 1.0),
  (1L, "b d", 0.0),
  (2L, "spark f g h", 1.0),
  (3L, "hadoop mapreduce", 0.0),
  (4L, "b spark who", 1.0),
  (5L, "g d a y", 0.0),
  (6L, "spark fly", 1.0),
  (7L, "was mapreduce", 0.0),
  (8L, "e spark program", 1.0),
  (9L, "a e c l", 0.0),
  (10L, "spark compile", 1.0),
  (11L, "hadoop software", 0.0)
)).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().setInputCol(tokenizer.getOutputCol).setOutputCol("features")
val lr = new LogisticRegression().setFamily("multinomial")//.LogisticRegressionWithLBFGS().setNumClasses(5)//.setMaxIter(10)
val pipeline = new Pipeline().setStages(Array(tokenizer, hashingTF, lr))


val paramGrid = new ParamGridBuilder().addGrid(hashingTF.numFeatures, Array(10, 100, 1000)).addGrid(lr.regParam, Array(0.1, 0.01)).build()


val cv = new CrossValidator().setEstimator(pipeline).setEvaluator(new MulticlassClassificationEvaluator).setEstimatorParamMaps(paramGrid).setNumFolds(2)  // Use 3+ in practice

val cvModel = cv.fit(training)

val test = spark.createDataFrame(Seq(
  (4L, "spark i j k"),
  (5L, "l m n"),
  (6L, "mapreduce spark"),
  (3L, "hadoop mapreduce"),
  (7L, "apache hadoop")
)).toDF("id", "text").select("text")


cvModel.transform(test).select("id", "text", "probability", "prediction").
  collect().foreach { case Row(id: Long, text: String, prob: mlV, prediction: Double) =>
    println(s"($id, $text) --> prob=$prob, prediction=$prediction")
  }