I got your checkin....I need to run logistic regression SGD vs BFGS for my current usecases but your next checkin will update the logistic regression with LBFGS right ? Are you adding it to regression package as well ?
Thanks. Deb On Mon, Apr 7, 2014 at 7:00 PM, DB Tsai <dbt...@stanford.edu> wrote: > Hi guys, > > The latest PR uses Breeze's L-BFGS implement which is introduced by > Xiangrui's sparse input format work in SPARK-1212. > > https://github.com/apache/spark/pull/353 > > Now, it works with the new sparse framework! > > Any feedback would be greatly appreciated. > > Thanks. > > Sincerely, > > DB Tsai > ------------------------------------------------------- > My Blog: https://www.dbtsai.com > LinkedIn: https://www.linkedin.com/in/dbtsai > > > On Thu, Apr 3, 2014 at 5:02 PM, DB Tsai <dbt...@alpinenow.com> wrote: > > ---------- Forwarded message ---------- > > From: David Hall <d...@cs.berkeley.edu> > > Date: Sat, Mar 15, 2014 at 10:02 AM > > Subject: Re: MLLib - Thoughts about refactoring Updater for LBFGS? > > To: DB Tsai <dbt...@alpinenow.com> > > > > > > On Fri, Mar 7, 2014 at 10:56 PM, DB Tsai <dbt...@alpinenow.com> wrote: > >> > >> Hi David, > >> > >> Please let me know the version of Breeze that LBFGS can be serialized, > >> and CachedDiffFunction is built-in in LBFGS once you finish. I'll > >> update the PR to Spark from using RISO implementation to Breeze > >> implementation. > > > > > > The current master (0.7-SNAPSHOT) has these problems fixed. > > > >> > >> > >> Thanks. > >> > >> Sincerely, > >> > >> DB Tsai > >> Machine Learning Engineer > >> Alpine Data Labs > >> -------------------------------------- > >> Web: http://alpinenow.com/ > >> > >> > >> On Thu, Mar 6, 2014 at 4:26 PM, David Hall <d...@cs.berkeley.edu> > wrote: > >> > 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. > >> >> > >> > > >> > great to hear! There were some bugs in LBFGS about 6 months ago, so > >> > depending on the last time you tried it, it might indeed have been > >> > bugged. > >> > > >> > > >> >> > >> >> 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. > >> >> > >> > > >> > I'm not sure why Spark should be serializing LBFGS? Shouldn't it live > on > >> > the controller node? Or is this a per-node thing? > >> > > >> > But no problem to make it serializable. > >> > > >> > > >> >> > >> >> 2) Breeze computes redundant gradient and loss. See the following log > >> >> from both Fortran and Breeze implementations. > >> >> > >> > > >> > Err, yeah. I should probably have LBFGS do this automatically, but > >> > there's > >> > a CachedDiffFunction that gets rid of the redundant calculations. > >> > > >> > -- David > >> > > >> > > >> >> > >> >> 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/ > >> >> >> > >> >> > > > > > > >
