resolving error with LabeledPoint.parse - scala

Question

I am new in spark and I am trying this example:
import org.apache.spark.SparkConf
import org.apache.spark.mllib.clustering.StreamingKMeans
import org.apache.spark.mllib.linalg.{Vectors,Vector}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.streaming.{Seconds, StreamingContext}
object App {
def main(args: Array[String]) {
if (args.length != 5) {
System.err.println(
"Usage: StreamingKMeansExample " +
"<trainingDir> <testDir> <batchDuration> <numClusters> <numDimensions>")
System.exit(1)
}
// $example on$
val conf = new SparkConf().setAppName("StreamingKMeansExample")
val ssc = new StreamingContext(conf, Seconds(args(2).toLong))
val trainingData = ssc.textFileStream(args(0)).map(Vectors.parse)
val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse)
val model = new StreamingKMeans()
.setK(args(3).toInt)
.setDecayFactor(1.0)
.setRandomCenters(args(4).toInt, 0.0)
model.trainOn(trainingData)
model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
ssc.start()
ssc.awaitTermination()
// $example off$
}
}
but it cannot resolve LabeledPoint.parse it only has apply and unapply methods available not parse.
It's probably the version I am using. So this is my sbt
name := "myApp"
version := "0.1"
scalaVersion := "2.11.0"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % "2.2.0" % "provided",
"org.apache.spark" %% "spark-streaming" % "2.2.0",
"org.apache.spark" %% "spark-mllib" % "2.3.1"
)
EDIT so I made a custom made labelPoint class since nothing else worker that did solved the compile problem. But, I tried to run it and the predicted values are always zero.
the input txt for train is
[36.72, 67.44]
[92.20, 11.81]
[90.85, 48.07]
.....
and the test txt is
(2, [9.26,68.19])
(1, [3.27,9.14])
(9, [66.66,13.85])
....
So why the result values are 2,0 1,0 9,0 ? Is there a problem with labeledPoint?