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/ >> >> >> >> >> > > >
