计算余弦相似度 Spark Dataframe

我正在使用 Spark Scala 来计算 Dataframe 行之间的余弦相似度。

数据帧格式如下

root
    |-- SKU: double (nullable = true)
    |-- Features: vector (nullable = true)

下面的数据框示例

    +-------+--------------------+
    |    SKU|            Features|
    +-------+--------------------+
    | 9970.0|[4.7143,0.0,5.785...|
    |19676.0|[5.5,0.0,6.4286,4...|
    | 3296.0|[4.7143,1.4286,6....|
    |13658.0|[6.2857,0.7143,4....|
    |    1.0|[4.2308,0.7692,5....|
    |  513.0|[3.0,0.0,4.9091,5...|
    | 3753.0|[5.9231,0.0,4.846...|
    |14967.0|[4.5833,0.8333,5....|
    | 2803.0|[4.2308,0.0,4.846...|
    |11879.0|[3.1429,0.0,4.5,4...|
    +-------+--------------------+

我尝试转置矩阵并检查以下提到的链接。Apache Spark Python DataFrame 上的余弦相似度 https://stackoverflow.com/questions/43921636/apache-spark-python-cosine-similarity-over-dataframes, 使用 tf-idf 将文本特征化为向量来计算余弦相似度 https://stackoverflow.com/questions/32645231/calculating-cosine-similarity-by-featurizing-the-text-into-vector-using-tf-idf但我相信有更好的解决方案

我尝试了下面的示例代码

val irm = new IndexedRowMatrix(inClusters.rdd.map {
  case (v,i:Vector) => IndexedRow(v, i)


}).toCoordinateMatrix.transpose.toRowMatrix.columnSimilarities

但我收到以下错误

Error:(80, 12) constructor cannot be instantiated to expected type;
 found   : (T1, T2)
 required: org.apache.spark.sql.Row
      case (v,i:Vector) => IndexedRow(v, i)

我检查了以下链接Apache Spark：如何从 DataFrame 创建矩阵？ https://stackoverflow.com/questions/31567989/apache-spark-how-to-create-a-matrix-from-a-dataframe但无法使用 Scala 做到这一点

• DataFrame.rdd回报RDD[Row] not RDD[(T, U)]。你必须模式匹配Row或者直接提取感兴趣的部分。
• ml Vector与...一起使用Datasets因为 Spark 2.0 与mllib Vector由旧 API 使用。你必须将它转换为使用IndexedRowMatrix.
• 索引必须是Long不是字符串。

import org.apache.spark.sql.Row

val irm = new IndexedRowMatrix(inClusters.rdd.map {
  Row(_, v: org.apache.spark.ml.linalg.Vector) => 
    org.apache.spark.mllib.linalg.Vectors.fromML(v)
}.zipWithIndex.map { case (v, i) => IndexedRow(i, v) })