spark scala用map和过滤器阅读文本文件

我有一个文本文件，格式如下（id,f1,f2,f3,...,fn）：

12345,0,0,1,2,...,3
23456,0,0,1,2,...,0
33333,0,1,1,0,...,0
56789,1,0,0,0,...,4
a_123,0,0,0,6,...,3
我想要读取文件（忽略线条a_123,0,0,0,6,...,3）来创建一个RDD[(Long, Vector)。这是我的解决方案：

def readDataset(path: String, sparkSession: SparkSession): RDD[(ItemId, Vector)] = {

val sc = sparkSession.sparkContext
sc.textFile(path)
  .map({ line => val values=line.split(",")
    (
      values(0).toLong,
      //util.Try(values(0).toLong).getOrElse(0L),
      Vectors.dense(values.slice(1, values.length).map {x => x.toDouble }).toSparse
    )})
  .filter(x => x._1 > 0)

}
但是这段代码无法编译：

[ERROR] found : org.apache.spark.rdd.RDD[(Long, org.apache.spark.ml.linalg.SparseVector)]
[ERROR] required: org.apache.spark.rdd.RDD[(Long, org.apache.spark.ml.linalg.Vector)]
[ERROR] (which expands to) org.apache.spark.rdd.RDD[(Long, org.apache.spark.ml.linalg.Vector)]
[ERROR] Note: (Long, org.apache.spark.ml.linalg.SparseVector) <: (Long, org.apache.spark.ml.linalg.Vector), but class RDD is invariant in type T.
[ERROR] You may wish to define T as +T instead. (SLS 4.5)
[ERROR] .filter(x => x._1 > 0)
[ERROR] ^
[ERROR] one error found
但是，如果我删除. toSparse或.filter(x => x._1 > 0)此代码可以成功编译。

有人知道为什么以及我该怎么做才能解决它？

还有没有更好的方法来读取文件到RDD忽略非数字id行？