Hi Trevor, Tarek You make non standard algorithms (PCA or otherwise) available to users of Spark as Spark Packages. http://spark-packages.org https://databricks.com/blog/2014/12/22/announcing-spark-packages.html
With the availability of spark packages, adding powerful experimental / alternative machine learning algorithms to the pipeline has never been easier. I would suggest that route in scenarios where one machine learning algorithm is not clearly better in the common scenarios than an existing implementation in MLLib. If your algorithm is for a large class of use cases better than the existing PCA implementation, then we should open a JIRA and discuss the relative strengths/ weaknesses (perhaps with some benchmarks) so we can better understand if it makes sense to switch out the existing PCA implementation and make yours the default. Ram On Tue, May 19, 2015 at 6:56 AM, Trevor Grant <trevor.d.gr...@gmail.com> wrote: > There are most likely advantages and disadvantages to Tarek's algorithm > against the current implementation, and different scenarios where each is > more appropriate. > > Would we not offer multiple PCA algorithms and let the user choose? > > Trevor > > Trevor Grant > Data Scientist > > *"Fortunate is he, who is able to know the causes of things." -Virgil* > > > On Mon, May 18, 2015 at 4:18 PM, Joseph Bradley <jos...@databricks.com> > wrote: > >> Hi Tarek, >> >> Thanks for your interest & for checking the guidelines first! On 2 >> points: >> >> Algorithm: PCA is of course a critical algorithm. The main question is >> how your algorithm/implementation differs from the current PCA. If it's >> different and potentially better, I'd recommend opening up a JIRA for >> explaining & discussing it. >> >> Java/Scala: We really do require that algorithms be in Scala, for the >> sake of maintainability. The conversion should be doable if you're willing >> since Scala is a pretty friendly language. If you create the JIRA, you >> could also ask for help there to see if someone can collaborate with you to >> convert the code to Scala. >> >> Thanks! >> Joseph >> >> On Mon, May 18, 2015 at 3:13 AM, Tarek Elgamal <tarek.elga...@gmail.com> >> wrote: >> >>> Hi, >>> >>> I would like to contribute an algorithm to the MLlib project. I have >>> implemented a scalable PCA algorithm on spark. It is scalable for both tall >>> and fat matrices and the paper around it is accepted for publication in >>> SIGMOD 2015 conference. I looked at the guidelines in the following link: >>> >>> >>> https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-MLlib-specificContributionGuidelines >>> >>> I believe that most of the guidelines applies in my case, however, the >>> code is written in java and it was not clear in the guidelines whether >>> MLLib project accepts java code or not. >>> My algorithm can be found under this repository: >>> https://github.com/Qatar-Computing-Research-Institute/sPCA >>> >>> Any help on how to make it suitable for MLlib project will be greatly >>> appreciated. >>> >>> Best Regards, >>> Tarek Elgamal >>> >>> >>> >>> >> >
