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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/09/20 21:53:20 UTC
[jira] [Commented] (SPARK-11918) WLS can not resolve some kinds of
equation
[ https://issues.apache.org/jira/browse/SPARK-11918?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15507914#comment-15507914 ]
Sean Owen commented on SPARK-11918:
-----------------------------------
Copying my comment from the other JIRA - yes we should have a better error.
But is Cholesky the right choice here? for this reason. AtA may not be positive definite.
> 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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