Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/15628#discussion_r107306663 --- Diff: mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala --- @@ -161,6 +162,109 @@ sealed trait Matrix extends Serializable { */ @Since("2.0.0") def numActives: Int + + /** + * Converts this matrix to a sparse matrix. + * + * @param colMajor Whether the values of the resulting sparse matrix should be in column major + * or row major order. If `false`, resulting matrix will be row major. + */ + private[ml] def toSparseMatrix(colMajor: Boolean): SparseMatrix + + /** + * Converts this matrix to a sparse matrix in column major order. + */ + @Since("2.2.0") + def toCSC: SparseMatrix = toSparseMatrix(colMajor = true) --- End diff -- I'm not good at naming, but since we use `toDenseRowMajor` for dense vector, should we use `toSparseColumnMajor`? Almost many packages are using `toCSC`, but I think we can make them consistent. Just my 2 cents.
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