Github user mdagost commented on a diff in the pull request:
https://github.com/apache/spark/pull/3095#discussion_r19917467
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala ---
@@ -278,8 +278,28 @@ class PythonMLLibAPI extends Serializable {
rank: Int,
iterations: Int,
lambda: Double,
- blocks: Int): MatrixFactorizationModel = {
- new MatrixFactorizationModelWrapper(ALS.train(ratings.rdd, rank,
iterations, lambda, blocks))
+ blocks: Int,
+ seed: Long,
--- End diff --
I'll move it to the end.
The issue is that it would be nice to preserve the nanosecond level time
seed that scala uses, but I couldn't find a way to do that directly in python
and just pass it through. So I created the extra stub to let scala do it
itself.
> On Nov 5, 2014, at 6:06 PM, Davies Liu <[email protected]> wrote:
>
> In
mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala:
>
> > @@ -278,8 +278,28 @@ class PythonMLLibAPI extends Serializable {
> > rank: Int,
> > iterations: Int,
> > lambda: Double,
> > - blocks: Int): MatrixFactorizationModel = {
> > - new MatrixFactorizationModelWrapper(ALS.train(ratings.rdd, rank,
iterations, lambda, blocks))
> > + blocks: Int,
> > + seed: Long,
> We could use java.lang.Long for seed, then seed can be null (means not
specified), then we do not need another stub.
>
> Also, putting seed in the end may be better (seed is optional).
>
> â
> Reply to this email directly or view it on GitHub.
>
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