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Andrew MacKinlay edited comment on SPARK-19234 at 1/18/17 1:53 AM: ------------------------------------------------------------------- [~yanboliang] I presume that you are the author judging by Github commits? Do you have an opinion on this? was (Author: admackin): [~yanbo] I presume that you are the author judging by Github commits? Do you have an opinion on this? > AFTSurvivalRegression chokes silently or with confusing errors when any > labels are zero > --------------------------------------------------------------------------------------- > > Key: SPARK-19234 > URL: https://issues.apache.org/jira/browse/SPARK-19234 > Project: Spark > Issue Type: Bug > Components: ML > Affects Versions: 2.1.0 > Environment: spark-shell or pyspark > Reporter: Andrew MacKinlay > Priority: Minor > Attachments: spark-aft-failure.txt > > > If you try and use AFTSurvivalRegression and any label in your input data is > 0.0, you get coefficients of 0.0 returned, and in many cases, errors like > this: > {{17/01/16 15:10:50 ERROR StrongWolfeLineSearch: Encountered bad values in > function evaluation. Decreasing step size to NaN}} > Zero should, I think, be an allowed value for survival analysis. I don't know > if this is a pathological case for AFT specifically as I don't know enough > about it, but this behaviour is clearly undesirable. If you have any labels > of 0.0, you get either a) obscure error messages, with no knowledge of the > cause and coefficients which are all zero or b) no errors messages at all and > coefficients of zero (arguably worse, since you don't even have console > output to tell you something's gone awry). If AFT doesn't work with > zero-valued labels, Spark should fail fast and let the developer know why. If > it does, we should get results here. -- 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