The markdown files are under spark/docs. You can submit a PR for changes. -Xiangrui
On Mon, Apr 21, 2014 at 6:01 PM, Sandy Ryza <sandy.r...@cloudera.com> wrote: > How do I get permissions to edit the wiki? > > > On Mon, Apr 21, 2014 at 3:19 PM, Xiangrui Meng <men...@gmail.com> wrote: > >> Cannot agree more with your words. Could you add one section about >> "how and what to contribute" to MLlib's guide? -Xiangrui >> >> On Mon, Apr 21, 2014 at 1:41 PM, Nick Pentreath >> <nick.pentre...@gmail.com> wrote: >> > I'd say a section in the "how to contribute" page would be a good place >> to put this. >> > >> > In general I'd say that the criteria for inclusion of an algorithm is it >> should be high quality, widely known, used and accepted (citations and >> concrete use cases as examples of this), scalable and parallelizable, well >> documented and with reasonable expectation of dev support >> > >> > Sent from my iPhone >> > >> >> On 21 Apr 2014, at 19:59, Sandy Ryza <sandy.r...@cloudera.com> wrote: >> >> >> >> If it's not done already, would it make sense to codify this philosophy >> >> somewhere? I imagine this won't be the first time this discussion comes >> >> up, and it would be nice to have a doc to point to. I'd be happy to >> take a >> >> stab at this. >> >> >> >> >> >>> On Mon, Apr 21, 2014 at 10:54 AM, Xiangrui Meng <men...@gmail.com> >> wrote: >> >>> >> >>> +1 on Sean's comment. MLlib covers the basic algorithms but we >> >>> definitely need to spend more time on how to make the design scalable. >> >>> For example, think about current "ProblemWithAlgorithm" naming scheme. >> >>> That being said, new algorithms are welcomed. I wish they are >> >>> well-established and well-understood by users. They shouldn't be >> >>> research algorithms tuned to work well with a particular dataset but >> >>> not tested widely. You see the change log from Mahout: >> >>> >> >>> === >> >>> The following algorithms that were marked deprecated in 0.8 have been >> >>> removed in 0.9: >> >>> >> >>> From Clustering: >> >>> Switched LDA implementation from using Gibbs Sampling to Collapsed >> >>> Variational Bayes (CVB) >> >>> Meanshift >> >>> MinHash - removed due to poor performance, lack of support and lack of >> >>> usage >> >>> >> >>> From Classification (both are sequential implementations) >> >>> Winnow - lack of actual usage and support >> >>> Perceptron - lack of actual usage and support >> >>> >> >>> Collaborative Filtering >> >>> SlopeOne implementations in >> >>> org.apache.mahout.cf.taste.hadoop.slopeone and >> >>> org.apache.mahout.cf.taste.impl.recommender.slopeone >> >>> Distributed pseudo recommender in >> >>> org.apache.mahout.cf.taste.hadoop.pseudo >> >>> TreeClusteringRecommender in >> >>> org.apache.mahout.cf.taste.impl.recommender >> >>> >> >>> Mahout Math >> >>> Hadoop entropy stuff in org.apache.mahout.math.stats.entropy >> >>> === >> >>> >> >>> In MLlib, we should include the algorithms users know how to use and >> >>> we can provide support rather than letting algorithms come and go. >> >>> >> >>> My $0.02, >> >>> Xiangrui >> >>> >> >>>> On Mon, Apr 21, 2014 at 10:23 AM, Sean Owen <so...@cloudera.com> >> wrote: >> >>>>> On Mon, Apr 21, 2014 at 6:03 PM, Paul Brown <p...@mult.ifario.us> >> wrote: >> >>>>> - MLlib as Mahout.next would be a unfortunate. There are some gems >> in >> >>>>> Mahout, but there are also lots of rocks. Setting a minimal bar of >> >>>>> working, correctly implemented, and documented requires a surprising >> >>> amount >> >>>>> of work. >> >>>> >> >>>> As someone with first-hand knowledge, this is correct. To Sang's >> >>>> question, I can't see value in 'porting' Mahout since it is based on a >> >>>> quite different paradigm. About the only part that translates is the >> >>>> algorithm concept itself. >> >>>> >> >>>> This is also the cautionary tale. The contents of the project have >> >>>> ended up being a number of "drive-by" contributions of implementations >> >>>> that, while individually perhaps brilliant (perhaps), didn't >> >>>> necessarily match any other implementation in structure, input/output, >> >>>> libraries used. The implementations were often a touch academic. The >> >>>> result was hard to document, maintain, evolve or use. >> >>>> >> >>>> Far more of the structure of the MLlib implementations are consistent >> >>>> by virtue of being built around Spark core already. That's great. >> >>>> >> >>>> One can't wait to completely build the foundation before building any >> >>>> implementations. To me, the existing implementations are almost >> >>>> exactly the basics I would choose. They cover the bases and will >> >>>> exercise the abstractions and structure. So that's also great IMHO. >> >>> >>
