Was "user" presented in training? We can put a check there and return NaN if the user is not included in the model. -Xiangrui
On Mon, Nov 3, 2014 at 5:25 PM, Debasish Das <debasish.da...@gmail.com> wrote: > Hi, > > I am testing MatrixFactorizationModel.predict(user: Int, product: Int) but > the code fails on userFeatures.lookup(user).head > > In computeRmse MatrixFactorizationModel.predict(RDD[(Int, Int)]) has been > called and in all the test-cases that API has been used... > > I can perhaps refactor my code to do the same but I was wondering whether > people test the lookup(user) version of the code.. > > Do I need to cache the model to make it work ? I think right now default is > STORAGE_AND_DISK... > > Thanks. > Deb --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
