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https://issues.apache.org/jira/browse/SPARK-17588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15502574#comment-15502574
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Sean Owen commented on SPARK-17588:
-----------------------------------
You can ignore the warnings, they're just about netlib falling back to Java
code.
The error means the input to a Cholesky decomposition wasn't positive definite.
What's your input like?
[~mengxr] is the system is solves in WeightedLeastSquares going to be positive
definite? it didn't seem like it would, necessarily, but I haven't thought it
through.
> java.lang.AssertionError: assertion failed: lapack.dppsv returned 105. when
> running glm using gaussian link function.
> ---------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-17588
> URL: https://issues.apache.org/jira/browse/SPARK-17588
> Project: Spark
> Issue Type: Question
> Components: SparkR
> Affects Versions: 2.0.0
> Reporter: sai pavan kumar chitti
> Labels: newbie
>
> hi,
> i am getting java.lang.AssertionError error when running glm, using gaussian
> link function, on a dataset with 109 columns and 81318461 rows
> Below is the call trace. Can someone please tell me what the issues is
> related to and how to go about resolving it. Is it because native
> acceleration is not working as i am also seeing following warning messages.
> WARN netlib.BLAS: Failed to load implementation from:
> com.github.fommil.netlib.NativeRefBLAS
> WARN netlib.LAPACK: Failed to load implementation from:
> com.github.fommil.netlib.NativeSystemLAPACK
> WARN netlib.LAPACK: Failed to load implementation from:
> com.github.fommil.netlib.NativeRefLAPACK
> 16/09/17 13:08:13 ERROR r.RBackendHandler: fit on
> org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper failed
> Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
> java.lang.AssertionError: assertion failed: lapack.dppsv returned 105.
> at scala.Predef$.assert(Predef.scala:170)
> at
> org.apache.spark.mllib.linalg.CholeskyDecomposition$.solve(CholeskyDecomposition.scala:40)
> at
> org.apache.spark.ml.optim.WeightedLeastSquares.fit(WeightedLeastSquares.scala:140)
> at
> org.apache.spark.ml.regression.GeneralizedLinearRegression.train(GeneralizedLinearRegression.scala:265)
> at
> org.apache.spark.ml.regression.GeneralizedLinearRegression.train(GeneralizedLinearRegression.scala:139)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:71)
> at
> org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:149)
> at
> org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:145)
> at scala.collection.Iterator$class.foreach(Iterator.scala:893)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
> at
> scala.collection.IterableViewLike$Transformed$class.foreach(IterableViewLike.sc
> thanks,
> pavan.
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