On Sun, Mar 30, 2014 at 2:01 PM, Debasish Das <debasish.da...@gmail.com>wrote:
> Hi David,
>
> I have started to experiment with BFGS solvers for Spark GLM over large
> scale data...
>
> I am also looking to add a good QP solver in breeze that can be used in
> Spark ALS for constraint solves...More details on that soon...
>
> I could not load up breeze 0.7 code onto eclipse...There is a folder called
> natives in the master but there is no code in that....all the code is in
> src/main/scala...
>
> I added the eclipse plugin:
>
> addSbtPlugin("com.github.mpeltonen" % "sbt-idea" % "1.6.0")
>
> addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.2.0")
>
> But it seems the project is set to use idea...
>
> Could you please explain the dev methodology for breeze ? My idea is to do
> solver work in breeze as that's the right place and get it into Spark
> through Xiangrui's WIP on Sparse data and breeze support...
>
It would be great to have a QP Solver: I don't know if you know about this
library: http://www.joptimizer.com/
I'm not quite sure what you mean by dev methodology. If you just mean how
to get code into Breeze, just send a PR to scalanlp/breeze. Unit tests are
good for something nontrivial like this. Maybe some basic documentation.
>
> Thanks.
> Deb
>
>
>
> On Fri, Mar 7, 2014 at 12:46 AM, DB Tsai <dbt...@alpinenow.com> wrote:
>
> > Hi Xiangrui,
> >
> > I think it doesn't matter whether we use Fortran/Breeze/RISO for
> > optimizers since optimization only takes << 1% of time. Most of the
> > time is in gradientSum and lossSum parallel computation.
> >
> > Sincerely,
> >
> > DB Tsai
> > Machine Learning Engineer
> > Alpine Data Labs
> > --------------------------------------
> > Web: http://alpinenow.com/
> >
> >
> > On Thu, Mar 6, 2014 at 7:10 PM, Xiangrui Meng <men...@gmail.com> wrote:
> > > Hi DB,
> > >
> > > Thanks for doing the comparison! What were the running times for
> > > fortran/breeze/riso?
> > >
> > > Best,
> > > Xiangrui
> > >
> > > On Thu, Mar 6, 2014 at 4:21 PM, DB Tsai <dbt...@alpinenow.com> wrote:
> > >> Hi David,
> > >>
> > >> I can converge to the same result with your breeze LBFGS and Fortran
> > >> implementations now. Probably, I made some mistakes when I tried
> > >> breeze before. I apologize that I claimed it's not stable.
> > >>
> > >> See the test case in BreezeLBFGSSuite.scala
> > >> https://github.com/AlpineNow/spark/tree/dbtsai-breezeLBFGS
> > >>
> > >> This is training multinomial logistic regression against iris dataset,
> > >> and both optimizers can train the models with 98% training accuracy.
> > >>
> > >> There are two issues to use Breeze in Spark,
> > >>
> > >> 1) When the gradientSum and lossSum are computed distributively in
> > >> custom defined DiffFunction which will be passed into your optimizer,
> > >> Spark will complain LBFGS class is not serializable. In
> > >> BreezeLBFGS.scala, I've to convert RDD to array to make it work
> > >> locally. It should be easy to fix by just having LBFGS to implement
> > >> Serializable.
> > >>
> > >> 2) Breeze computes redundant gradient and loss. See the following log
> > >> from both Fortran and Breeze implementations.
> > >>
> > >> Thanks.
> > >>
> > >> Fortran:
> > >> Iteration -1: loss 1.3862943611198926, diff 1.0
> > >> Iteration 0: loss 1.5846343143210866, diff 0.14307193024217352
> > >> Iteration 1: loss 1.1242501524477688, diff 0.29053004039012126
> > >> Iteration 2: loss 1.0930151243303563, diff 0.027782962952189336
> > >> Iteration 3: loss 1.054036932835569, diff 0.03566113127440601
> > >> Iteration 4: loss 0.9907956302751622, diff 0.05999907649459571
> > >> Iteration 5: loss 0.9184205380342829, diff 0.07304737423337761
> > >> Iteration 6: loss 0.8259870936519937, diff 0.10064381175132982
> > >> Iteration 7: loss 0.6327447552109574, diff 0.23395293458364716
> > >> Iteration 8: loss 0.5534101162436359, diff 0.1253815427665277
> > >> Iteration 9: loss 0.4045020086612566, diff 0.26907321376758075
> > >> Iteration 10: loss 0.3078824990823728, diff 0.23885980452569627
> > >>
> > >> Breeze:
> > >> Iteration -1: loss 1.3862943611198926, diff 1.0
> > >> Mar 6, 2014 3:59:11 PM com.github.fommil.netlib.BLAS <clinit>
> > >> WARNING: Failed to load implementation from:
> > >> com.github.fommil.netlib.NativeSystemBLAS
> > >> Mar 6, 2014 3:59:11 PM com.github.fommil.netlib.BLAS <clinit>
> > >> WARNING: Failed to load implementation from:
> > >> com.github.fommil.netlib.NativeRefBLAS
> > >> Iteration 0: loss 1.3862943611198926, diff 0.0
> > >> Iteration 1: loss 1.5846343143210866, diff 0.14307193024217352
> > >> Iteration 2: loss 1.1242501524477688, diff 0.29053004039012126
> > >> Iteration 3: loss 1.1242501524477688, diff 0.0
> > >> Iteration 4: loss 1.1242501524477688, diff 0.0
> > >> Iteration 5: loss 1.0930151243303563, diff 0.027782962952189336
> > >> Iteration 6: loss 1.0930151243303563, diff 0.0
> > >> Iteration 7: loss 1.0930151243303563, diff 0.0
> > >> Iteration 8: loss 1.054036932835569, diff 0.03566113127440601
> > >> Iteration 9: loss 1.054036932835569, diff 0.0
> > >> Iteration 10: loss 1.054036932835569, diff 0.0
> > >> Iteration 11: loss 0.9907956302751622, diff 0.05999907649459571
> > >> Iteration 12: loss 0.9907956302751622, diff 0.0
> > >> Iteration 13: loss 0.9907956302751622, diff 0.0
> > >> Iteration 14: loss 0.9184205380342829, diff 0.07304737423337761
> > >> Iteration 15: loss 0.9184205380342829, diff 0.0
> > >> Iteration 16: loss 0.9184205380342829, diff 0.0
> > >> Iteration 17: loss 0.8259870936519939, diff 0.1006438117513297
> > >> Iteration 18: loss 0.8259870936519939, diff 0.0
> > >> Iteration 19: loss 0.8259870936519939, diff 0.0
> > >> Iteration 20: loss 0.6327447552109576, diff 0.233952934583647
> > >> Iteration 21: loss 0.6327447552109576, diff 0.0
> > >> Iteration 22: loss 0.6327447552109576, diff 0.0
> > >> Iteration 23: loss 0.5534101162436362, diff 0.12538154276652747
> > >> Iteration 24: loss 0.5534101162436362, diff 0.0
> > >> Iteration 25: loss 0.5534101162436362, diff 0.0
> > >> Iteration 26: loss 0.40450200866125635, diff 0.2690732137675816
> > >> Iteration 27: loss 0.40450200866125635, diff 0.0
> > >> Iteration 28: loss 0.40450200866125635, diff 0.0
> > >> Iteration 29: loss 0.30788249908237314, diff 0.23885980452569502
> > >>
> > >> Sincerely,
> > >>
> > >> DB Tsai
> > >> Machine Learning Engineer
> > >> Alpine Data Labs
> > >> --------------------------------------
> > >> Web: http://alpinenow.com/
> > >>
> > >>
> > >> On Wed, Mar 5, 2014 at 2:00 PM, David Hall <d...@cs.berkeley.edu>
> > wrote:
> > >>> On Wed, Mar 5, 2014 at 1:57 PM, DB Tsai <dbt...@alpinenow.com>
> wrote:
> > >>>
> > >>>> Hi David,
> > >>>>
> > >>>> On Tue, Mar 4, 2014 at 8:13 PM, dlwh <david.lw.h...@gmail.com>
> wrote:
> > >>>> > I'm happy to help fix any problems. I've verified at points that
> the
> > >>>> > implementation gives the exact same sequence of iterates for a few
> > >>>> different
> > >>>> > functions (with a particular line search) as the c port of lbfgs.
> > So I'm
> > >>>> a
> > >>>> > little surprised it fails where Fortran succeeds... but only a
> > little.
> > >>>> This
> > >>>> > was fixed late last year.
> > >>>> I'm working on a reproducible test case using breeze vs fortran
> > >>>> implementation to show the problem I've run into. The test will be
> in
> > >>>> one of the test cases in my Spark fork, is it okay for you to
> > >>>> investigate the issue? Or do I need to make it as a standalone test?
> > >>>>
> > >>>
> > >>>
> > >>> Um, as long as it wouldn't be too hard to pull out.
> > >>>
> > >>>
> > >>>>
> > >>>> Will send you the test later today.
> > >>>>
> > >>>> Thanks.
> > >>>>
> > >>>> Sincerely,
> > >>>>
> > >>>> DB Tsai
> > >>>> Machine Learning Engineer
> > >>>> Alpine Data Labs
> > >>>> --------------------------------------
> > >>>> Web: http://alpinenow.com/
> > >>>>
> >
>
