Thank you very much Kevin.
On 29 February 2016 at 16:20, Kevin Mellott <kevin.r.mell...@gmail.com> wrote: > I found a helper class that I think should do the trick. Take a look at > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala > > When passing the Loss, you should be able to do something like: > > Losses.fromString("leastSquaresError") > > On Mon, Feb 29, 2016 at 10:03 AM, diplomatic Guru < > diplomaticg...@gmail.com> wrote: > >> It's strange as you are correct the doc does state it. But it's >> complaining about the constructor. >> >> When I clicked on the org.apache.spark.mllib.tree.loss.AbsoluteError >> class, this is what I see: >> >> >> @Since("1.2.0") >> @DeveloperApi >> object AbsoluteError extends Loss { >> >> /** >> * Method to calculate the gradients for the gradient boosting >> calculation for least >> * absolute error calculation. >> * The gradient with respect to F(x) is: sign(F(x) - y) >> * @param prediction Predicted label. >> * @param label True label. >> * @return Loss gradient >> */ >> @Since("1.2.0") >> override def gradient(prediction: Double, label: Double): Double = { >> if (label - prediction < 0) 1.0 else -1.0 >> } >> >> override private[mllib] def computeError(prediction: Double, label: >> Double): Double = { >> val err = label - prediction >> math.abs(err) >> } >> } >> >> >> On 29 February 2016 at 15:49, Kevin Mellott <kevin.r.mell...@gmail.com> >> wrote: >> >>> Looks like it should be present in 1.3 at >>> org.apache.spark.mllib.tree.loss.AbsoluteError >>> >>> >>> spark.apache.org/docs/1.3.0/api/java/org/apache/spark/mllib/tree/loss/AbsoluteError.html >>> >>> On Mon, Feb 29, 2016 at 9:46 AM, diplomatic Guru < >>> diplomaticg...@gmail.com> wrote: >>> >>>> AbsoluteError() constructor is undefined. >>>> >>>> I'm using Spark 1.3.0, maybe it is not ready for this version? >>>> >>>> >>>> >>>> On 29 February 2016 at 15:38, Kevin Mellott <kevin.r.mell...@gmail.com> >>>> wrote: >>>> >>>>> I believe that you can instantiate an instance of the AbsoluteError >>>>> class for the *Loss* object, since that object implements the Loss >>>>> interface. For example. >>>>> >>>>> val loss = new AbsoluteError() >>>>> boostingStrategy.setLoss(loss) >>>>> >>>>> On Mon, Feb 29, 2016 at 9:33 AM, diplomatic Guru < >>>>> diplomaticg...@gmail.com> wrote: >>>>> >>>>>> Hi Kevin, >>>>>> >>>>>> Yes, I've set the bootingStrategy like that using the example. But >>>>>> I'm not sure how to create and pass the Loss object. >>>>>> >>>>>> e.g >>>>>> >>>>>> boostingStrategy.setLoss(......); >>>>>> >>>>>> Not sure how to pass the selected Loss. >>>>>> >>>>>> How do I set the Absolute Error in setLoss() function? >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> On 29 February 2016 at 15:26, Kevin Mellott < >>>>>> kevin.r.mell...@gmail.com> wrote: >>>>>> >>>>>>> You can use the constructor that accepts a BoostingStrategy object, >>>>>>> which will allow you to set the tree strategy (and other >>>>>>> hyperparameters as >>>>>>> well). >>>>>>> >>>>>>> *GradientBoostedTrees >>>>>>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/tree/GradientBoostedTrees.html#GradientBoostedTrees(org.apache.spark.mllib.tree.configuration.BoostingStrategy)>* >>>>>>> (BoostingStrategy >>>>>>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/tree/configuration/BoostingStrategy.html> >>>>>>> boostingStrategy) >>>>>>> >>>>>>> On Mon, Feb 29, 2016 at 9:21 AM, diplomatic Guru < >>>>>>> diplomaticg...@gmail.com> wrote: >>>>>>> >>>>>>>> Hello guys, >>>>>>>> >>>>>>>> I think the default Loss algorithm is Squared Error for regression, >>>>>>>> but how do I change that to Absolute Error in Java. >>>>>>>> >>>>>>>> Could you please show me an example? >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
