Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/15628#discussion_r105068149
--- Diff:
mllib-local/src/main/scala/org/apache/spark/ml/linalg/Matrices.scala ---
@@ -153,6 +153,86 @@ sealed trait Matrix extends Serializable {
*/
@Since("2.0.0")
def numActives: Int
+
+ /**
+ * Converts this matrix to a sparse matrix.
+ *
+ * @param columnMajor 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(columnMajor: Boolean): SparseMatrix
--- End diff --
Yeah, this is very hacky in my opinion too!
The problem is that when one overloads a function without parenthesis, the
ambiguity happens because when that function is invoked without parenthesis,
this can be calling the actual function without parenthesis, or getting the
function with parenthesis. The following is an example demonstrating the issue.
In my opinion, I would like to call it `toSparse(columnMajor: Boolean)` and
`toSparse() = toSparse(true)`, but in the vector api, we already use the one
without parenthesis, so it will result inconsistency in the api design.
I think exposing the ability of converting it to `columnMajor` or
`rowMajor` is very useful, as a result, we can expose it as `toCSRMatrix`,
`toCSCMatrix`, and `toSparse` which converts the matrix to the one with
smallest storage.
```scala
scala> trait A {
| def foo(b: Boolean): String
| def foo: String = foo(true)
| }
defined trait A
scala> class B extends A {
| def foo(b: Boolean): String = b.toString
| }
defined class B
scala> val b = new B
b: B = B@67b6d4ae
scala> b.foo
<console>:18: error: ambiguous reference to overloaded definition,
both method foo in class B of type (b: Boolean)String
and method foo in trait A of type => String
match expected type ?
b.foo
^
scala> val x: String = b.foo
x: String = true
scala> val y: Boolean=> String = b.foo
y: Boolean => String = <function1>
```
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