Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/11610#discussion_r55964546 --- Diff: mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala --- @@ -108,6 +101,57 @@ private[ml] class WeightedLeastSquares( "Consider setting fitIntercept=true.") } } + /* + * If more than one of the features in the data are constant (i.e. data matrix has constant + * columns), then A^T.A is no longer positive definite and Cholesky decomposition fails + * (because the normal equation does not have a solution). + * In order to find a solution, we need to drop constant columns from the data matrix. Or, + * we can drop corresponding column and row from A^T.A matrix. + * Once we drop rows/columns from A^T.A matrix, the Cholesky decomposition will produce + * correct coefficients. But, for the final result, we need to add zeros to the list of + * coefficients corresponding to the constant features. + */ + val aVarRaw = summary.aVar.values + // this will keep track of features to keep in the model, and remove + // features with zero variance. + val nzVarIndex = aVarRaw.zipWithIndex.filter(_._1 != 0).map(_._2) --- End diff -- Using `foreachActive` to build the non zero element index given the reason we just discussed.
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