Hello Morten ok. afaik there is a tiny bit of randomness in these ML algorithms (pls anyone correct me if i m wrong). In fact if you run your RDF code multiple times, it will not give you EXACTLY the same results (though accuracy and errors should me more or less similar)..at least this is what i found when playing around with RDF and decision trees and other ML algorithms
If RDF is not a must for your usecase, could you try 'scale back' to Decision Trees and see if you still get intermittent failures? this at least to exclude issues with the data hth marco On Sat, Dec 10, 2016 at 5:20 PM, Morten Hornbech <mor...@datasolvr.com> wrote: > Already did. There are no issues with smaller samples. I am running this > in a cluster of three t2.large instances on aws. > > I have tried to find the threshold where the error occurs, but it is not a > single factor causing it. Input size and subsampling rate seems to be most > significant, and number of trees the least. > > I have also tried running on a test frame of randomized numbers with the > same number of rows, and could not reproduce the problem here. > > By the way maxDepth is 5 and maxBins is 32. > > I will probably need to leave this for a few weeks to focus on more > short-term stuff, but I will write here if I solve it or reproduce it more > consistently. > > Morten > > Den 10. dec. 2016 kl. 17.29 skrev Marco Mistroni <mmistr...@gmail.com>: > > Hi > Bring back samples to 1k range to debug....or as suggested reduce tree > and bins.... had rdd running on same size data with no issues.....or send > me some sample code and data and I try it out on my ec2 instance ... > Kr > > On 10 Dec 2016 3:16 am, "Md. Rezaul Karim" <rezaul.karim@insight-centre. > org> wrote: > >> I had similar experience last week. Even I could not find any error >> trace. >> >> Later on, I did the following to get rid of the problem: >> i) I downgraded to Spark 2.0.0 >> ii) Decreased the value of maxBins and maxDepth >> >> Additionally, make sure that you set the featureSubsetStrategy as "auto" to >> let the algorithm choose the best feature subset strategy for your data. >> Finally, set the impurity as "gini" for the information gain. >> >> However, setting the value of no. of trees to just 1 does not give you >> either real advantage of the forest neither better predictive performance. >> >> >> >> Best, >> Karim >> >> >> On Dec 9, 2016 11:29 PM, "mhornbech" <mor...@datasolvr.com> wrote: >> >>> Hi >>> >>> I have spent quite some time trying to debug an issue with the Random >>> Forest >>> algorithm on Spark 2.0.2. The input dataset is relatively large at around >>> 600k rows and 200MB, but I use subsampling to make each tree manageable. >>> However even with only 1 tree and a low sample rate of 0.05 the job >>> hangs at >>> one of the final stages (see attached). I have checked the logs on all >>> executors and the driver and find no traces of error. Could it be a >>> memory >>> issue even though no error appears? The error does seem sporadic to some >>> extent so I also wondered whether it could be a data issue, that only >>> occurs >>> if the subsample includes the bad data rows. >>> >>> Please comment if you have a clue. >>> >>> Morten >>> >>> <http://apache-spark-user-list.1001560.n3.nabble.com/file/n2 >>> 8192/Sk%C3%A6rmbillede_2016-12-10_kl.png> >>> >>> >>> >>> -- >>> View this message in context: http://apache-spark-user-list. >>> 1001560.n3.nabble.com/Random-Forest-hangs-without-trace-of-e >>> rror-tp28192.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com >>> <http://nabble.com>. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>> >>> >
