Thanks a lot for your time guys and your quick replies! > On Nov 26, 2014, at 7:53 PM, Xiangrui Meng <men...@gmail.com> wrote: > > The training RMSE may increase due to regularization. Squared loss > only represents part of the global loss. If you watch the sum of the > squared loss and the regularization, it should be non-increasing. > -Xiangrui > > On Wed, Nov 26, 2014 at 9:53 AM, Sean Owen <so...@cloudera.com> wrote: >> 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 >>
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