How about 0.10 is the first block and 0.10.1 is the second? On Wed, Mar 18, 2015 at 1:12 AM, Andrew Palumbo <ap....@outlook.com> wrote:
> I like this timeline... though mid April is coming up quickly.. Going back > to Pat's list for 0.10.0: > > 1) refactor mrlegacy out of scala deps. >> 2) build fixes for release. >> 3) docs — might be good to guinea-pig the new CMS with git pubsub so we >> don’t have to do svn, not sure when that will be ready >> > > I would add: > > 4) Fix any remaining legacy bugs. >> 5) docs, docs, docs >> > > along with just some general cleanup. > > Is anything else missing? > > > > > On 03/17/2015 07:16 PM, Andrew Musselman wrote: > >> I'm good with that timing pending scope.. >> >> On Wed, Mar 18, 2015 at 12:13 AM, Dmitriy Lyubimov <dlie...@gmail.com> >> wrote: >> >> i was thinking 0.10.0 mid-april, update 0.10.1 end of spring. >>> >>> i would suggest feature extraction topics for 0.11.x. Esp. w.r.t. >>> SchemaRDD aka DataFrame -- vectorizing, hashing, ML schema support, >>> imputation of missing data, outlier cleanups etc. There's a lot. >>> >>> Hardware backs integration -- i will certainly be looking at those, >>> but perhaps the easiest is to start with automatic detection and >>> configuration of capabilities via netlib, since it is already in the >>> path and it seems likely that it will (eventually) support cuda as >>> well in some form. This is for 0.11 or 0.12.x, depends on >>> availability. >>> >>> Higher order methods are somewhat a matter of inspiration. I think i >>> could offer some stuff there too as I already have implemented a lot >>> of those on top of Mahout before. I did bayesian optimization (aka >>> "spearmint", GP-EI etc.) on Mahout algebra, line search, (L)bfgs, >>> stats including Gaussian Process support. BFGS and line search are >>> fairly simple methods and i will give a reference if anybody is >>> interested. also, breeze also has line search with strong wolfe >>> conditions (if a coded reference is needed). All that is up for grabs >>> as a fairly well understood subject. >>> >>> (5-6 months out) Once GP-EI is available, it becomes a fairly >>> interesting topic to resurrect implicit feedback issue. Important >>> insight there is that in fact feature incoding can be done by a custom >>> scheme (not necessarily using encoding schme done in paper; in fact, >>> there are 2 of them there; or the way mllib encodes that as well). >>> once custom encoding schemes are adjusted, using bayesian optimization >>> is increasingly important, especially if there are more than just 2 >>> parameters there. >>> >>> >
