Hello Thomas,
since you are calling evaluate here, you should be creating an
EvaluateDataSet operation that works with LabeledVector, I see you are
creating a new PredictOperation.
On Wed, Oct 19, 2016 at 3:05 PM, Thomas FOURNIER <
thomasfournier...@gmail.com> wrote:
> 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
>
