How to read probability Vector in Spark Dataframe LogisticRegression output - scala

Question

I am trying to read the first probability on the logistic regression output so that I may perform decile binning on it.
Below is some test code that just emulates the outputs with a vector.
val r = sqlContext.createDataFrame(Seq(("jane", Vectors.dense(.98)),
("tom", Vectors.dense(.34)),
("nancy", Vectors.dense(.93)),
("tim", Vectors.dense(.02)),
("larry", Vectors.dense(.033)),
("lana", Vectors.dense(.85)),
("jack", Vectors.dense(.84)),
("john", Vectors.dense(.09)),
("jill", Vectors.dense(.12)),
("mike", Vectors.dense(.21)),
("jason", Vectors.dense(.31)),
("roger", Vectors.dense(.76)),
("ed", Vectors.dense(.77)),
("alan", Vectors.dense(.64)),
("ryan", Vectors.dense(.52)),
("ted", Vectors.dense(.66)),
("paul", Vectors.dense(.67)),
("brian", Vectors.dense(.68)),
("jeff", Vectors.dense(.05)))).toDF(CSMasterCustomerID, MLProbability)
var result = r.select(CSMasterCustomerID, MLProbability)
val schema = StructType(Seq(StructField(CSMasterCustomerID, StringType, false), StructField(MLProbability, DoubleType, true)))
result = sqlContext.createDataFrame(result.map((r: Row) => {
r match {
case Row(mcid: String, probability: Vector) =>
RowFactory.create(mcid, probability(0))
}
}), schema)
This fails to compile saying:
<console>:56: error: type mismatch;
found : Double
required: Object
Note: an implicit exists from scala.Double => java.lang.Double, but
methods inherited from Object are rendered ambiguous. This is to avoid
a blanket implicit which would convert any scala.Double to any AnyRef.
You may wish to use a type ascription: `x: java.lang.Double`.
RowFactory.create(mcid, probability(0))
Any suggestions to fix this or another approach?