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-
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
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 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
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" wrote:
> Hi Deb, did you use ALS with implicit feedback? -Xiangrui
>
> On Mon, Mar 10, 2014 at 1:17 PM, Xiangrui Meng wrote:
>
Hi Deb, did you use ALS with implicit feedback? -Xiangrui
On Mon, Mar 10, 2014 at 1:17 PM, Xiangrui Meng 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,
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 wrote:
> Hi Xiangrui,
>
> I used lambda = 0.1...It is possible that 2 users ranked in
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 hess
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
I'm not sure about the mathematical details, but I found in some
experiments with Mahout that the matrix there was also not positive
definite. Therefore, we chose QR decomposition to solve the linear system.
--sebastian
On 03/06/2014 03:44 PM, Debasish Das wrote:
Hi,
I am running ALS on a s
Hi,
I am running ALS on a sparse problem (10M x 1M) and I am getting the
following error:
org.jblas.exceptions.LapackArgumentException: LAPACK DPOSV: Leading minor
of order i of A is not positive definite.
at org.jblas.SimpleBlas.posv(SimpleBlas.java:373)
at org.jblas.Solve.solvePositive(Solve.ja
Hi,
I am running ALS on a sparse problem (10M x 1M) and I am getting the
following error:
org.jblas.exceptions.LapackArgumentException: LAPACK DPOSV: Leading minor
of order i of A is not positive definite.
at org.jblas.SimpleBlas.posv(SimpleBlas.java:373)
at org.jblas.Solve.solvePositive(Solve.ja
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