I concur that deprecation is a better way than simply removing the classes. Will do so, thanks.
/s On 07.12.2012 03:38, Marty Kube wrote: > One nice way to do this to to mark the classes in question depreciated > for a few releases, and then remove them on an announced schedule. That > lets any end users know what is coming and gives them time to respond. > > On 12/06/2012 10:21 AM, Sebastian Schelter wrote: >> FunkSVD is a suboptimal duplicate of RatingSGDFactorizer, >> ImplicitLinearRegressionFactorizer is a duplicate of ALSWR so I think we >> should only keep one of each. >> >> The other three recommenders seem to be used almost never, so I'd >> likean announced >> to remove them, however I wouldn't have a problem with keeping them for >> any reason. >> >> Best, >> Sebastian >> >> On 06.12.2012 16:14, Sean Owen wrote: >>> The tree-based ones are very old and not fast, and were more of an >>> experiment. I recall a few questions about them but it seemed like >>> people were really just trying to do clustering, and this is a bad way >>> to do clustering. >>> >>> knn is old too, and in a sense spiritually quite similar to ALS. I >>> don't mind removing it either. >>> >>> It would seal it if there were even a nominal argument that this >>> improves the rest of the code base -- less to maintain, removes >>> duplication, inconsistency, etc. I could imagine that argument here. >>> >>> On Thu, Dec 6, 2012 at 3:06 PM, Sebastian Schelter <[email protected]> >>> wrote: >>>> Hi there, >>>> >>>> I'm currently thinking whether we should do a little cleanup in the >>>> non-distributed recommenders package and throw out recommenders that >>>> have not been used/asked about on the mailinglist or that have been >>>> replaced by a superior implementation. >>>> >>>> If anyone reads this and sees a recommender, he/she wants to be kept, >>>> please shout! >>>> >>>> /s >>>> >>>> Here's a list of suggested stuff to remove, let me know what you think: >>>> >>>> org.apache.mahout.cf.taste.impl.recommender.svd.FunkSVDFactorizer >>>> >>>> RatingSGDFactorizer should be learning faster and has a nicer model as >>>> it includes user/item biases >>>> >>>> >>>> org.apache.mahout.cf.taste.impl.recommender.svd.ImplicitLinearRegressionFactorizer >>>> >>>> >>>> Seems to be using the same model as ALSWRFactorizer, however there are >>>> no tests and ALSWR can handle more explicit and implicit feedback >>>> >>>> >>>> org.apache.mahout.cf.taste.impl.recommender.TreeClusteringRecommender >>>> org.apache.mahout.cf.taste.impl.recommender.TreeClusteringRecommender2 >>>> org.apache.mahout.cf.taste.impl.recommender.knn >>>> >>>> I don't recall anybody using those or asking about them the last years. >>>> >>>> >>>> >> >> >
