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https://issues.apache.org/jira/browse/SPARK-11918?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15021787#comment-15021787
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Sean Owen edited comment on SPARK-11918 at 9/20/16 9:54 PM:
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[~yanboliang] yes this is true in general of ill-conditioned problems. What are
you proposing? to propagate the error from lapack in a different way? check the
condition number? it's roughly speaking the correct behavior in that there's no
real answer here.
EDIT to my old comment: I don't think that's accurate. It's possible to return
a 'best' answer in at least some cases that would trigger this problem, like
two identical features.
was (Author: srowen):
[~yanboliang] yes this is true in general of ill-conditioned problems. What are
you proposing? to propagate the error from lapack in a different way? check the
condition number? it's roughly speaking the correct behavior in that there's no
real answer here.
> WLS can not resolve some kinds of equation
> ------------------------------------------
>
> Key: SPARK-11918
> URL: https://issues.apache.org/jira/browse/SPARK-11918
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Yanbo Liang
> Priority: Minor
> Labels: starter
> Attachments: R_GLM_output
>
>
> Weighted Least Squares (WLS) is one of the optimization method for solve
> Linear Regression (when #feature < 4096). But if the dataset is very ill
> condition (such as 0-1 based label used for classification and the equation
> is underdetermined), the WLS failed (But "l-bfgs" can train and get the
> model). The failure is caused by the underneath lapack library return error
> value when Cholesky decomposition.
> This issue is easy to reproduce, you can train a LinearRegressionModel by
> "normal" solver with the example
> dataset(https://github.com/apache/spark/blob/master/data/mllib/sample_libsvm_data.txt).
> The following is the exception:
> {code}
> assertion failed: lapack.dpotrs returned 1.
> java.lang.AssertionError: assertion failed: lapack.dpotrs returned 1.
> at scala.Predef$.assert(Predef.scala:179)
> at
> org.apache.spark.mllib.linalg.CholeskyDecomposition$.solve(CholeskyDecomposition.scala:42)
> at
> org.apache.spark.ml.optim.WeightedLeastSquares.fit(WeightedLeastSquares.scala:117)
> at
> org.apache.spark.ml.regression.LinearRegression.train(LinearRegression.scala:180)
> at
> org.apache.spark.ml.regression.LinearRegression.train(LinearRegression.scala:67)
> at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
> {code}
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