GitHub user iyounus opened a pull request: https://github.com/apache/spark/pull/11610
[SPARK-13777] [ML] Remove constant features from training in noraml solver (WLS) ## What changes were proposed in this pull request? "normal" solver in LinearRegression uses Cholesky decomposition to calculate the coefficients. If the data has features with identical values (zero variance), then (A^T A) matrix is not positive definite any more and the Cholesky decomposition fails. Since A^T.A and features variances are calculated in single pass, it's better to modify ATA instead to re-calculating it from the data after dropping constant columns. In this PR, I'm dropping columns and rows from ATA corresponding to features with zero variance. Then the cholesky decomposition can be performed without any problem. ## How was this patch tested? A unit test under LineatReagessionSuite is added which compares results from this change and l-bgfs solver to glmnet. All these are now onsistent. You can merge this pull request into a Git repository by running: $ git pull https://github.com/iyounus/spark SPARK-13777_WLS_fix_for_constant_features Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/11610.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #11610 ---- commit 9412ef439d523e58e5d6b8294628c2196f6f9019 Author: Imran Younus <iyou...@us.ibm.com> Date: 2016-03-09T19:38:12Z remove constant features from training in noraml solver ---- --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org