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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/09/21 08:38:20 UTC
[jira] [Assigned] (SPARK-11918) Better error from WLS for cases
like singular input
[ https://issues.apache.org/jira/browse/SPARK-11918?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-11918:
------------------------------------
Assignee: Apache Spark (was: Sean Owen)
> Better error from WLS for cases like singular input
> ---------------------------------------------------
>
> Key: SPARK-11918
> URL: https://issues.apache.org/jira/browse/SPARK-11918
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Yanbo Liang
> Assignee: Apache Spark
> Priority: Minor
> 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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