[ https://issues.apache.org/jira/browse/SPARK-4547?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng updated SPARK-4547: --------------------------------- Target Version/s: 1.3.0 > OOM when making bins in BinaryClassificationMetrics > --------------------------------------------------- > > Key: SPARK-4547 > URL: https://issues.apache.org/jira/browse/SPARK-4547 > Project: Spark > Issue Type: Bug > Components: MLlib > Affects Versions: 1.1.0 > Reporter: Sean Owen > Priority: Minor > > Also following up on > http://mail-archives.apache.org/mod_mbox/spark-dev/201411.mbox/%3CCAMAsSdK4s4TNkf3_ecLC6yD-pLpys_PpT3WB7Tp6=yoxuxf...@mail.gmail.com%3E > -- this one I intend to make a PR for a bit later. The conversation was > basically: > {quote} > Recently I was using BinaryClassificationMetrics to build an AUC curve for a > classifier over a reasonably large number of points (~12M). The scores were > all probabilities, so tended to be almost entirely unique. > The computation does some operations by key, and this ran out of memory. It's > something you can solve with more than the default amount of memory, but in > this case, it seemed unuseful to create an AUC curve with such fine-grained > resolution. > I ended up just binning the scores so there were ~1000 unique values > and then it was fine. > {quote} > and: > {quote} > Yes, if there are many distinct values, we need binning to compute the AUC > curve. Usually, the scores are not evenly distribution, we cannot simply > truncate the digits. Estimating the quantiles for binning is necessary, > similar to RangePartitioner: > https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/Partitioner.scala#L104 > Limiting the number of bins is definitely useful. > {quote} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org