They have been merged into the master branch. However, the improvements are for implicit ALS computation. I don't think they can speed up normal ALS computation. Could you share more details about the variable projection?
JIRAs: https://spark-project.atlassian.net/browse/SPARK-1266 https://spark-project.atlassian.net/browse/SPARK-1238 https://spark-project.atlassian.net/browse/MLLIB-25 Best, Xiangrui On Wed, Mar 19, 2014 at 3:17 PM, Debasish Das <debasish.da...@gmail.com> wrote: > Nope...with the cleaner dataset I am not noticing issues with the dposv and > this dataset is even bigger...20 M users and 1 M products...I don't think > other than cholesky anything else will get us the efficiency we need... > > For my usecase we also need to see the effectiveness of positive factors > and I am doing variable projection as a start.. > > If possible could you please point me to the PRs related to ALS > improvements ? Are they all added to the master ? There are at least 3 PRs > that Sean and you contributed recently related to ALS efficiency. > > A JIRA or gist will definitely help a lot. > > Thanks. > Deb > > > > On Wed, Mar 19, 2014 at 10:11 AM, Xiangrui Meng <men...@gmail.com> wrote: > >> Another question: do you have negative or out-of-range user or product >> ids or? -Xiangrui >> >> On Tue, Mar 11, 2014 at 8:00 PM, Debasish Das <debasish.da...@gmail.com> >> wrote: >> > Nope..I did not test implicit feedback yet...will get into more detailed >> > debug and generate the testcase hopefully next week... >> > On Mar 11, 2014 7:02 PM, "Xiangrui Meng" <men...@gmail.com> wrote: >> > >> >> Hi Deb, did you use ALS with implicit feedback? -Xiangrui >> >> >> >> On Mon, Mar 10, 2014 at 1:17 PM, Xiangrui Meng <men...@gmail.com> >> wrote: >> >> > Choosing lambda = 0.1 shouldn't lead to the error you got. This is >> >> > probably a bug. Do you mind sharing a small amount of data that can >> >> > re-produce the error? -Xiangrui >> >> > >> >> > On Fri, Mar 7, 2014 at 8:24 AM, Debasish Das < >> debasish.da...@gmail.com> >> >> wrote: >> >> >> Hi Xiangrui, >> >> >> >> >> >> I used lambda = 0.1...It is possible that 2 users ranked in movies >> in a >> >> >> very similar way... >> >> >> >> >> >> I agree that increasing lambda will solve the problem but you agree >> >> this is >> >> >> not a solution...lambda should be tuned based on sparsity / other >> >> criteria >> >> >> and not to make a linearly dependent hessian matrix linearly >> >> >> independent... >> >> >> >> >> >> Thanks. >> >> >> Deb >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> On Thu, Mar 6, 2014 at 7:20 PM, Xiangrui Meng <men...@gmail.com> >> wrote: >> >> >> >> >> >>> If the matrix is very ill-conditioned, then A^T A becomes >> numerically >> >> >>> rank deficient. However, if you use a reasonably large positive >> >> >>> regularization constant (lambda), "A^T A + lambda I" should be still >> >> >>> positive definite. What was the regularization constant (lambda) you >> >> >>> set? Could you test whether the error still happens when you use a >> >> >>> large lambda? >> >> >>> >> >> >>> Best, >> >> >>> Xiangrui >> >> >>> >> >> >>
