How do I change the schema on a Spark Dataset

When I retrieve a dataset in Spark 2, using a select statement the underlying columns inherit the data types of the queried columns.

val ds1 = spark.sql("select 1 as a, 2 as b, 'abd' as c")

ds1.printSchema()
root
 |-- a: integer (nullable = false)
 |-- b: integer (nullable = false)
 |-- c: string (nullable = false)

Now if I convert this into a case class, it will correctly convert the values, but the underlying schema is still wrong.

case class abc(a: Double, b: Double, c: String)
val ds2 = ds1.as[abc]
ds2.printSchema()
root
 |-- a: integer (nullable = false)
 |-- b: integer (nullable = false)
 |-- c: string (nullable = false)

ds2.collect
res18: Array[abc] = Array(abc(1.0,2.0,abd))

I "SHOULD" be able to specify the encoder to use when I create the second dataset, but scala seems to ignore this parameter (Is this a BUG?):

val abc_enc = org.apache.spark.sql.Encoders.product[abc]

val ds2 = ds1.as[abc](abc_enc)

ds2.printSchema
root
 |-- a: integer (nullable = false)
 |-- b: integer (nullable = false)
 |-- c: string (nullable = false)

So the only way I can see to do this simply, without very complex mapping is to use createDataset, but this requires a collect on the underlying object, so it's not ideal.

val ds2 = spark.createDataset(ds1.as[abc].collect)