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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-11918) WLS can not resolve some kinds of equation
Date Tue, 20 Sep 2016 21:53:20 GMT

    [ 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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