I also modified the example to try 1, 5, 9, ... iterations as you did, and also ran with the same default parameters. I used the sample_movielens_data.txt file. Is that what you're using?
My result is: Iteration 1 Test RMSE = 1.426079653593016 Train RMSE = 1.5013155094216357 Iteration 5 Test RMSE = 1.405598012724468 Train RMSE = 1.4847078708333596 Iteration 9 Test RMSE = 1.4055990901261632 Train RMSE = 1.484713206769993 Iteration 13 Test RMSE = 1.4055990999738366 Train RMSE = 1.4847132332994588 Iteration 17 Test RMSE = 1.40559910003368 Train RMSE = 1.48471323345531 Iteration 21 Test RMSE = 1.4055991000342158 Train RMSE = 1.4847132334567061 Iteration 25 Test RMSE = 1.4055991000342174 Train RMSE = 1.4847132334567108 Train error is higher than test error, consistently, which could be underfitting. A higher rank=50 gets a reasonable result: Iteration 1 Test RMSE = 1.5981883186995312 Train RMSE = 1.4841671360432005 Iteration 5 Test RMSE = 1.5745145659678204 Train RMSE = 1.4672341345080382 Iteration 9 Test RMSE = 1.5745147110505406 Train RMSE = 1.4672385714907996 Iteration 13 Test RMSE = 1.5745147108258577 Train RMSE = 1.4672385929631868 Iteration 17 Test RMSE = 1.5745147108246424 Train RMSE = 1.4672385930428344 Iteration 21 Test RMSE = 1.5745147108246367 Train RMSE = 1.4672385930431973 Iteration 25 Test RMSE = 1.5745147108246367 Train RMSE = 1.467238593043199 I'm not sure what the difference is. I looked at your modifications and they seem very similar. Is it the data you're using? On Wed, Nov 26, 2014 at 3:34 PM, Kostas Kloudas <kklou...@gmail.com> wrote: > For the training I am using the code in the MovieLensALS example with > trainImplicit set to false > and for the training RMSE I use the > > val rmseTr = computeRmse(model, training, params.implicitPrefs). > > The computeRmse() method is provided in the MovieLensALS class. > > > Thanks a lot, > Kostas > > >> On Nov 26, 2014, at 2:41 PM, Sean Owen <so...@cloudera.com> wrote: >> >> How are you computing RMSE? >> and how are you training the model -- not with trainImplicit right? >> I wonder if you are somehow optimizing something besides RMSE. >> >> On Wed, Nov 26, 2014 at 2:36 PM, Kostas Kloudas <kklou...@gmail.com> wrote: >>> Once again, the error even with the training dataset increases. The results >>> are: >>> >>> Running 1 iterations >>> For 1 iter.: Test RMSE = 1.2447121194304893 Training RMSE = >>> 1.2394166987104076 (34.751317636 s). >>> Running 5 iterations >>> For 5 iter.: Test RMSE = 1.3253957117600659 Training RMSE = >>> 1.3206317416138509 (37.693118023000004 s). >>> Running 9 iterations >>> For 9 iter.: Test RMSE = 1.3255293380139364 Training RMSE = >>> 1.3207661218210436 (41.046175661 s). >>> Running 13 iterations >>> For 13 iter.: Test RMSE = 1.3255295352665748 Training RMSE = >>> 1.3207663201865092 (47.763619515 s). >>> Running 17 iterations >>> For 17 iter.: Test RMSE = 1.32552953555787 Training RMSE = >>> 1.3207663204794406 (59.682361103000005 s). >>> Running 21 iterations >>> For 21 iter.: Test RMSE = 1.3255295355583026 Training RMSE = >>> 1.3207663204798756 (57.210578232 s). >>> Running 25 iterations >>> For 25 iter.: Test RMSE = 1.325529535558303 Training RMSE = >>> 1.3207663204798765 (65.785485882 s). >>> >>> Thanks a lot, >>> Kostas >>> >>> On Nov 26, 2014, at 12:04 PM, Nick Pentreath <nick.pentre...@gmail.com> >>> wrote: >>> >>> copying user group - I keep replying directly vs reply all :) >>> >>> On Wed, Nov 26, 2014 at 2:03 PM, Nick Pentreath <nick.pentre...@gmail.com> >>> wrote: >>>> >>>> ALS will be guaranteed to decrease the squared error (therefore RMSE) in >>>> each iteration, on the training set. >>>> >>>> This does not hold for the test set / cross validation. You would expect >>>> the test set RMSE to stabilise as iterations increase, since the algorithm >>>> converges - but not necessarily to decrease. >>>> >>>> On Wed, Nov 26, 2014 at 1:57 PM, Kostas Kloudas <kklou...@gmail.com> >>>> wrote: >>>>> >>>>> Hi all, >>>>> >>>>> I am getting familiarized with Mllib and a thing I noticed is that >>>>> running the MovieLensALS >>>>> example on the movieLens dataset for increasing number of iterations does >>>>> not decrease the >>>>> rmse. >>>>> >>>>> The results for 0.6% training set and 0.4% test are below. For training >>>>> set to 0.8%, the results >>>>> are almost identical. Shouldn’t it be normal to see a decreasing error? >>>>> Especially going from 1 to 5 iterations. >>>>> >>>>> Running 1 iterations >>>>> Test RMSE for 1 iter. = 1.2452964343277886 (52.757125927000004 s). >>>>> Running 5 iterations >>>>> Test RMSE for 5 iter. = 1.3258973764470259 (61.183927666 s). >>>>> Running 9 iterations >>>>> Test RMSE for 9 iter. = 1.3260308117704385 (61.84948875800001 s). >>>>> Running 13 iterations >>>>> Test RMSE for 13 iter. = 1.3260310099809915 (73.799510125 s). >>>>> Running 17 iterations >>>>> Test RMSE for 17 iter. = 1.3260310102735398 (77.56512185300001 s). >>>>> Running 21 iterations >>>>> Test RMSE for 21 iter. = 1.3260310102739703 (79.607495074 s). >>>>> Running 25 iterations >>>>> Test RMSE for 25 iter. = 1.326031010273971 (88.631776301 s). >>>>> Running 29 iterations >>>>> Test RMSE for 29 iter. = 1.3260310102739712 (101.178383079 s). >>>>> >>>>> Thanks a lot, >>>>> Kostas >>>>> --------------------------------------------------------------------- >>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>>>> For additional commands, e-mail: user-h...@spark.apache.org >>>>> >>>> >>> >>> > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
