BTW it looks like row and column similarities (cosine based) are coming to MLlib through DIMSUM. Andrew said rowSimilarity doesn’t seem to be in the master yet. Does anyone know the status?
See: https://databricks.com/blog/2014/10/20/efficient-similarity-algorithm-now-in-spark-twitter.html <https://databricks.com/blog/2014/10/20/efficient-similarity-algorithm-now-in-spark-twitter.html> Also the method for computation reduction (make it less than O(n^2)) seems rooted in cosine. A different computation reduction method is used in the Mahout code tied to LLR. Seems like we should get these together. On Jan 17, 2015, at 9:37 AM, Andrew Musselman <andrew.mussel...@gmail.com> wrote: Excellent, thanks Pat. On Jan 17, 2015, at 9:27 AM, Pat Ferrel <p...@occamsmachete.com <mailto:p...@occamsmachete.com>> wrote: > Mahout’s Spark implementation of rowsimilarity is in the Scala > SimilarityAnalysis class. It actually does either row or column similarity > but only supports LLR at present. It does [AA’] for columns or [A’A] for rows > first then calculates the distance (LLR) for non-zero elements. This is a > major optimization for sparse matrices. As I recall the old hadoop code only > did this for half the matrix since it’s symmetric but that optimization isn’t > in the current code because the downsampling is done as LLR is calculated, so > the entire similarity matrix is never actually calculated unless you disable > downsampling. > > The primary use is for recommenders but I’ve used it (in the test suite) for > row-wise text token similarity too. > > On Jan 17, 2015, at 9:00 AM, Andrew Musselman <andrew.mussel...@gmail.com > <mailto:andrew.mussel...@gmail.com>> wrote: > > Yeah that's the kind of thing I'm looking for; was looking at SPARK-4259 and > poking around to see how to do things. > > https://issues.apache.org/jira/plugins/servlet/mobile#issue/SPARK-4259 > <https://issues.apache.org/jira/plugins/servlet/mobile#issue/SPARK-4259> > > On Jan 17, 2015, at 8:35 AM, Suneel Marthi <suneel_mar...@yahoo.com > <mailto:suneel_mar...@yahoo.com>> wrote: > >> Andrew, u would be better off using Mahout's RowSimilarityJob for what u r >> trying to accomplish. >> >> 1. It does give u pair-wise distances >> 2. U can specify the Distance measure u r looking to use >> 3. There's the old MapReduce impl and the Spark DSL impl per ur preference. >> >> From: Andrew Musselman <andrew.mussel...@gmail.com >> <mailto:andrew.mussel...@gmail.com>> >> To: Reza Zadeh <r...@databricks.com <mailto:r...@databricks.com>> >> Cc: user <user@spark.apache.org <mailto:user@spark.apache.org>> >> Sent: Saturday, January 17, 2015 11:29 AM >> Subject: Re: Row similarities >> >> Thanks Reza, interesting approach. I think what I actually want is to >> calculate pair-wise distance, on second thought. Is there a pattern for >> that? >> >> >> >> On Jan 16, 2015, at 9:53 PM, Reza Zadeh <r...@databricks.com >> <mailto:r...@databricks.com>> wrote: >> >>> You can use K-means >>> <https://spark.apache.org/docs/latest/mllib-clustering.html> with a >>> suitably large k. Each cluster should correspond to rows that are similar >>> to one another. >>> >>> On Fri, Jan 16, 2015 at 5:18 PM, Andrew Musselman >>> <andrew.mussel...@gmail.com <mailto:andrew.mussel...@gmail.com>> wrote: >>> What's a good way to calculate similarities between all vector-rows in a >>> matrix or RDD[Vector]? >>> >>> I'm seeing RowMatrix has a columnSimilarities method but I'm not sure I'm >>> going down a good path to transpose a matrix in order to run that. >>> >> >> >
