ps, it doesn't make sense to have weight and gradient sparse unless with strong L1 penalty.
Sincerely, DB Tsai ------------------------------------------------------- My Blog: https://www.dbtsai.com LinkedIn: https://www.linkedin.com/in/dbtsai On Wed, Apr 23, 2014 at 10:17 PM, DB Tsai <dbt...@dbtsai.com> wrote: > In mllib, the weight, and gradient are dense. Only feature is sparse. > > Sincerely, > > DB Tsai > ------------------------------------------------------- > My Blog: https://www.dbtsai.com > LinkedIn: https://www.linkedin.com/in/dbtsai > > > On Wed, Apr 23, 2014 at 10:16 PM, David Hall <d...@cs.berkeley.edu> wrote: >> Was the weight vector sparse? The gradients? Or just the feature vectors? >> >> >> On Wed, Apr 23, 2014 at 10:08 PM, DB Tsai <dbt...@dbtsai.com> wrote: >>> >>> The figure showing the Log-Likelihood vs Time can be found here. >>> >>> >>> https://github.com/dbtsai/spark-lbfgs-benchmark/raw/fd703303fb1c16ef5714901739154728550becf4/result/a9a11M.pdf >>> >>> Let me know if you can not open it. >>> >>> Sincerely, >>> >>> DB Tsai >>> ------------------------------------------------------- >>> My Blog: https://www.dbtsai.com >>> LinkedIn: https://www.linkedin.com/in/dbtsai >>> >>> >>> On Wed, Apr 23, 2014 at 9:34 PM, Shivaram Venkataraman < >>> shiva...@eecs.berkeley.edu> wrote: >>> >>> > I don't think the attachment came through in the list. Could you upload >>> > the results somewhere and link to them ? >>> > >>> > >>> > On Wed, Apr 23, 2014 at 9:32 PM, DB Tsai <dbt...@dbtsai.com> wrote: >>> > >>> >> 123 features per rows, and in average, 89% are zeros. >>> >> On Apr 23, 2014 9:31 PM, "Evan Sparks" <evan.spa...@gmail.com> wrote: >>> >> >>> >> > What is the number of non zeroes per row (and number of features) in >>> >> > the >>> >> > sparse case? We've hit some issues with breeze sparse support in the >>> >> past >>> >> > but for sufficiently sparse data it's still pretty good. >>> >> > >>> >> > > On Apr 23, 2014, at 9:21 PM, DB Tsai <dbt...@stanford.edu> wrote: >>> >> > > >>> >> > > Hi all, >>> >> > > >>> >> > > I'm benchmarking Logistic Regression in MLlib using the newly added >>> >> > optimizer LBFGS and GD. I'm using the same dataset and the same >>> >> methodology >>> >> > in this paper, http://www.csie.ntu.edu.tw/~cjlin/papers/l1.pdf >>> >> > > >>> >> > > I want to know how Spark scale while adding workers, and how >>> >> optimizers >>> >> > and input format (sparse or dense) impact performance. >>> >> > > >>> >> > > The benchmark code can be found here, >>> >> > https://github.com/dbtsai/spark-lbfgs-benchmark >>> >> > > >>> >> > > The first dataset I benchmarked is a9a which only has 2.2MB. I >>> >> > duplicated the dataset, and made it 762MB to have 11M rows. This >>> >> > dataset >>> >> > has 123 features and 11% of the data are non-zero elements. >>> >> > > >>> >> > > In this benchmark, all the dataset is cached in memory. >>> >> > > >>> >> > > As we expect, LBFGS converges faster than GD, and at some point, no >>> >> > matter how we push GD, it will converge slower and slower. >>> >> > > >>> >> > > However, it's surprising that sparse format runs slower than dense >>> >> > format. I did see that sparse format takes significantly smaller >>> >> > amount >>> >> of >>> >> > memory in caching RDD, but sparse is 40% slower than dense. I think >>> >> sparse >>> >> > should be fast since when we compute x wT, since x is sparse, we can >>> >> > do >>> >> it >>> >> > faster. I wonder if there is anything I'm doing wrong. >>> >> > > >>> >> > > The attachment is the benchmark result. >>> >> > > >>> >> > > Thanks. >>> >> > > >>> >> > > Sincerely, >>> >> > > >>> >> > > DB Tsai >>> >> > > ------------------------------------------------------- >>> >> > > My Blog: https://www.dbtsai.com >>> >> > > LinkedIn: https://www.linkedin.com/in/dbtsai >>> >> > >>> >> >>> > >>> > >> >>
