Spark DataFrame CountVectorizedModel 错误数据类型字符串

Question

我有以下代码尝试执行一个简单的操作，我试图将稀疏向量转换为密集向量。 这是我到目前为止所拥有的：

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.{StringIndexer, OneHotEncoder}
import org.apache.spark.ml.feature.CountVectorizerModel
import org.apache.spark.mllib.linalg.Vector
import spark.implicits._

// Identify how many distinct values are in the OCEAN_PROXIMITY column
val distinctOceanProximities = dfRaw.select(col("ocean_proximity")).distinct().as[String].collect()

val cvmDF = new CountVectorizerModel(tags)
  .setInputCol("ocean_proximity")
  .setOutputCol("sparseFeatures")
  .transform(dfRaw)
  
val exprs = (0 until distinctOceanProximities.size).map(i => $"features".getItem(i).alias(s"$distinctOceanProximities(i)"))
val vecToSeq = udf((v: Vector) => v.toArray)

val df2 = cvmDF.withColumn("features", vecToSeq($"sparseFeatures")).select(exprs:_*)
df2.show()

当我运行此脚本时，我收到以下错误：

java.lang.IllegalArgumentException: requirement failed: Column ocean_proximity must be of type equal to one of the following types: [array<string>, array<string>] but was actually of type string.
  at scala.Predef$.require(Predef.scala:281)
  at org.apache.spark.ml.util.SchemaUtils$.checkColumnTypes(SchemaUtils.scala:63)
  at org.apache.spark.ml.feature.CountVectorizerParams.validateAndTransformSchema(CountVectorizer.scala:97)
  at org.apache.spark.ml.feature.CountVectorizerParams.validateAndTransformSchema$(CountVectorizer.scala:95)
  at org.apache.spark.ml.feature.CountVectorizerModel.validateAndTransformSchema(CountVectorizer.scala:272)
  at org.apache.spark.ml.feature.CountVectorizerModel.transformSchema(CountVectorizer.scala:338)
  at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:71)
  at org.apache.spark.ml.feature.CountVectorizerModel.transform(CountVectorizer.scala:306)
  ... 101 elided

我认为它期待数据类型的字符串序列，但我只有一个字符串。 任何想法如何解决这一问题？

Answer 1

这很简单。 我所要做的就是将列从字符串转换为字符串数组，如下所示：

val oceanProximityAsArrayDF = dfRaw.withColumn("ocean_proximity", array("ocean_proximity"))