FlinkML - Evaluate function should manage LabeledVector

[Thomas FOURNIER]

Hi,

I'd like to improve SVM evaluate function so that it can use LabeledVector
(and not only Vector).
Indeed, what is done in test is the following (data is a
DataSet[LabeledVector]):

val test = data.map(l => (l.vector, l.label))
svm.evaluate(test)

We would like to do:
sm.evaluate(data)


Adding this "new" code:

implicit def predictLabeledPoint[T <: LabeledVector] = {
 new PredictOperation  ...
}

gives me a predictOperation that should be used with
defaultEvaluateDataSetOperation
with the correct signature (ie with T <: LabeledVector and not T<: Vector).

Nonetheless, tests are failing:


it should "predict with LabeledDataPoint" in {

  val env = ExecutionEnvironment.getExecutionEnvironment

  val svm = SVM().
    setBlocks(env.getParallelism).
    setIterations(100).
    setLocalIterations(100).
    setRegularization(0.002).
    setStepsize(0.1).
    setSeed(0)

  val trainingDS = env.fromCollection(Classification.trainingData)
  svm.fit(trainingDS)
  val predictionPairs = svm.evaluate(trainingDS)

  ....
}

There is no PredictOperation defined for
org.apache.flink.ml.classification.SVM which takes a
DataSet[org.apache.flink.ml.common.LabeledVector] as input.
java.lang.RuntimeException: There is no PredictOperation defined for
org.apache.flink.ml.classification.SVM which takes a
DataSet[org.apache.flink.ml.common.LabeledVector] as input.



Thanks

Regards
Thomas