Hi Liu, In ML, even after extracting the data into RDD, the versions between MLib and ML are quite different. Due to legacy design, in MLlib, we use Updater for handling regularization, and this layer of abstraction also does adaptive step size which is only for SGD. In order to get it working with LBFGS, some hacks were being done here and there, and in Updater, all the components including intercept are regularized which is not desirable in many cases. Also, in the legacy design, it's hard for us to do in-place standardization to improve the convergency rate. As a result, at some point, we decide to ditch those abstractions, and customize them for each algorithms. (Even LiR and LoR use different tricks to have better performance for numerical optimization, so it's hard to share code at that time. But I can see the point that we have working code now, so it's time to try to refactor those code to share more.)
Sincerely, DB Tsai ---------------------------------------------------------- Blog: https://www.dbtsai.com PGP Key ID: 0xAF08DF8D <https://pgp.mit.edu/pks/lookup?search=0x59DF55B8AF08DF8D> On Mon, Oct 12, 2015 at 1:24 AM, YiZhi Liu <javeli...@gmail.com> wrote: > Hi Joseph, > > Thank you for clarifying the motivation that you setup a different API > for ml pipelines, it sounds great. But I still think we could extract > some common parts of the training & inference procedures for ml and > mllib. In ml.classification.LogisticRegression, you simply transform > the DataFrame into RDD and follow the same procedures in > mllib.optimization.{LBFGS,OWLQN}, right? > > My suggestion is, if I may, ml package should focus on the public API, > and leave the underlying implementations, e.g. numerical optimization, > to mllib package. > > Please let me know if my understanding has any problem. Thank you! > > 2015-10-08 1:15 GMT+08:00 Joseph Bradley <jos...@databricks.com>: > > Hi YiZhi Liu, > > > > The spark.ml classes are part of the higher-level "Pipelines" API, which > > works with DataFrames. When creating this API, we decided to separate it > > from the old API to avoid confusion. You can read more about it here: > > http://spark.apache.org/docs/latest/ml-guide.html > > > > For (3): We use Breeze, but we have to modify it in order to do > distributed > > optimization based on Spark. > > > > Joseph > > > > On Tue, Oct 6, 2015 at 11:47 PM, YiZhi Liu <javeli...@gmail.com> wrote: > >> > >> Hi everyone, > >> > >> I'm curious about the difference between > >> ml.classification.LogisticRegression and > >> mllib.classification.LogisticRegressionWithLBFGS. Both of them are > >> optimized using LBFGS, the only difference I see is LogisticRegression > >> takes DataFrame while LogisticRegressionWithLBFGS takes RDD. > >> > >> So I wonder, > >> 1. Why not simply add a DataFrame training interface to > >> LogisticRegressionWithLBFGS? > >> 2. Whats the difference between ml.classification and > >> mllib.classification package? > >> 3. Why doesn't ml.classification.LogisticRegression call > >> mllib.optimization.LBFGS / mllib.optimization.OWLQN directly? Instead, > >> it uses breeze.optimize.LBFGS and re-implements most of the procedures > >> in mllib.optimization.{LBFGS,OWLQN}. > >> > >> Thank you. > >> > >> Best, > >> > >> -- > >> Yizhi Liu > >> Senior Software Engineer / Data Mining > >> www.mvad.com, Shanghai, China > >> > >> --------------------------------------------------------------------- > >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > >> For additional commands, e-mail: user-h...@spark.apache.org > >> > > > > > > -- > Yizhi Liu > Senior Software Engineer / Data Mining > www.mvad.com, Shanghai, China > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > >
