Then you only have to provide an implicit PredictOperation[SVM, (T, Int),
(LabeledVector, Int)] value with T <: Vector in the scope where you call
the predict operation.
On Jun 6, 2015 8:14 AM, "Felix Neutatz" <neut...@googlemail.com> wrote:
> That would be great. I like the special predict operation better because it
> is only in some cases necessary to return the id. The special predict
> Operation would save this overhead.
>
> Best regards,
> Felix
> Am 04.06.2015 7:56 nachm. schrieb "Till Rohrmann" <till.rohrm...@gmail.com
> >:
>
> > I see your problem. One way to solve the problem is to implement a
> special
> > PredictOperation which takes a tuple (id, vector) and returns a tuple
> (id,
> > labeledVector). You can take a look at the implementation for the vector
> > prediction operation.
> >
> > But we can also discuss about adding an ID field to the Vector type.
> >
> > Cheers,
> > Till
> > On Jun 4, 2015 7:30 PM, "Felix Neutatz" <neut...@googlemail.com> wrote:
> >
> > > Hi,
> > >
> > > I have the following use case: I want to to regression for a timeseries
> > > dataset like:
> > >
> > > id, x1, x2, ..., xn, y
> > >
> > > id = point in time
> > > x = features
> > > y = target value
> > >
> > > In the Flink frame work I would map this to a LabeledVector (y,
> > > DenseVector(x)). (I don't want to use the id as a feature)
> > >
> > > When I apply finally the predict() method I get a LabeledVector
> > > (y_predicted, DenseVector(x)).
> > >
> > > Now my problem is that I would like to plot the predicted target value
> > > according to its time.
> > >
> > > What I have to do now is:
> > >
> > > a = predictedDataSet.map ( LabeledVector => Tuple2(x,y_p))
> > > b = originalDataSet.map("id, x1, x2, ..., xn, y" => Tuple2(x,id))
> > >
> > > a.join(b).where("x").equalTo("x") { (a,b) => (id, y_p)
> > >
> > > This is really a cumbersome process for such an simple thing. Is there
> > any
> > > approach which makes this more simple. If not, can we extend the ML
> API.
> > to
> > > allow ids?
> > >
> > > Best regards,
> > > Felix
> > >
> >
>
