How to convert Array[Long] to Vector type in scala dataframe?

Question

I have dataframe like this:

+------+-----+-------------------+--------------------+
|    Id|Label|          Timestamp|         Signal_list|
+------+-----+-------------------+--------------------+
|A05439|    1|2014-05-20 05:05:21|[-116, -123, -129...|
|A06392|    1|2013-12-27 04:12:33|[260, 314, 370, 4...|
|A08192|    1|2014-06-03 04:06:15|[334, 465, 628, 8...|
|A08219|    3|2013-12-31 03:12:41|[-114, -140, -157...|
|A02894|    2|2013-10-28 06:10:53|[109, 139, 170, 1...|

This dataframe signal list have 9k elements, I want to convert the signal list column into vector. I tried the below UDF :

import org.apache.spark.ml.linalg._

val convertUDF = udf((array : Seq[Long]) => {
  Vectors.dense(array.toArray)
})
val afWithVector = afLabel.select("*").withColumn("Signal_list", convertUDF($"Signal_list"))

But it gives error:

console>:39: error: overloaded method value dense with alternatives:
  (values: Array[Double])org.apache.spark.ml.linalg.Vector <and>
  (firstValue: Double,otherValues: Double*)org.apache.spark.ml.linalg.Vector
 cannot be applied to (Array[Long])
         Vectors.dense(array.toArray)

Dataframe schema:

|-- Id: string (nullable = true)
 |-- Label: integer (nullable = true)
 |-- Timestamp: string (nullable = true)
 |-- Signal_list: array (nullable = true)
 |    |-- element: long (containsNull = true)

I'm new at using scala, an answer using pyspark will be more helpful.

Answer 1

The UDF is nearly correct. The problem lies in that a vector in Spark can only use doubles, longs are not accepted. The change would look like this in Scala:

val convertUDF = udf((array : Seq[Long]) => {
  Vectors.dense(array.toArray.map(_.toDouble))
})

In Python I believe it would look like this:

udf(lambda vs: Vectors.dense([float(i) for i in vs]), VectorUDT())