Yes although I think this difference is on purpose as part of that commercial strategy. If future versions change license it would still be possible to not upgrade. Or fork / recreate the bean classes. Not worried so much but it is a good point. On Nov 11, 2014 10:06 PM, "DB Tsai" <dbt...@dbtsai.com> wrote:
> I also worry about that the author of JPMML changed the license of > jpmml-evaluator due to his interest of his commercial business, and he > might change the license of jpmml-model in the future. > > Sincerely, > > DB Tsai > ------------------------------------------------------- > My Blog: https://www.dbtsai.com > LinkedIn: https://www.linkedin.com/in/dbtsai > > > On Tue, Nov 11, 2014 at 11:43 AM, Sean Owen <so...@cloudera.com> wrote: > > Yes, jpmml-evaluator is AGPL, but things like jpmml-model are not; > they're > > 3-clause BSD: > > > > https://github.com/jpmml/jpmml-model > > > > So some of the scoring components are off-limits for an AL2 project but > the > > core model components are OK. > > > > On Tue, Nov 11, 2014 at 7:40 PM, DB Tsai <dbt...@dbtsai.com> wrote: > >> > >> JPMML evaluator just changed their license to AGPL or commercial > >> license, and I think AGPL is not compatible with apache project. Any > >> advice? > >> > >> https://github.com/jpmml/jpmml-evaluator > >> > >> Sincerely, > >> > >> DB Tsai > >> ------------------------------------------------------- > >> My Blog: https://www.dbtsai.com > >> LinkedIn: https://www.linkedin.com/in/dbtsai > >> > >> > >> On Tue, Nov 11, 2014 at 10:07 AM, Xiangrui Meng <men...@gmail.com> > wrote: > >> > Vincenzo sent a PR and included k-means as an example. Sean is helping > >> > review it. PMML standard is quite large. So we may start with simple > >> > model export, like linear methods, then move forward to tree-based. > >> > -Xiangrui > >> > > >> > On Mon, Nov 10, 2014 at 11:27 AM, Aris <arisofala...@gmail.com> > wrote: > >> >> Hello Spark and MLLib folks, > >> >> > >> >> So a common problem in the real world of using machine learning is > that > >> >> some > >> >> data analysis use tools like R, but the more "data engineers" out > there > >> >> will > >> >> use more advanced systems like Spark MLLib or even Python Scikit > Learn. > >> >> > >> >> In the real world, I want to have "a system" where multiple different > >> >> modeling environments can learn from data / build models, represent > the > >> >> models in a common language, and then have a layer which just takes > the > >> >> model and run model.predict() all day long -- scores the models in > >> >> other > >> >> words. > >> >> > >> >> It looks like the project openscoring.io and jpmml-evaluator are > some > >> >> amazing systems for this, but they fundamentally use PMML as the > model > >> >> representation here. > >> >> > >> >> I have read some JIRA tickets that Xiangrui Meng is interested in > >> >> getting > >> >> PMML implemented to export MLLib models, is that happening? Further, > >> >> would > >> >> something like Manish Amde's boosted ensemble tree methods be > >> >> representable > >> >> in PMML? > >> >> > >> >> Thank you!! > >> >> Aris > >> > > >> > --------------------------------------------------------------------- > >> > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > >> > For additional commands, e-mail: user-h...@spark.apache.org > >> > > > > > >
