[jira] [Commented] (SPARK-12861) Changes to support KMeans with large feature space

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-12861?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15121412#comment-15121412 ] 

Sean Owen commented on SPARK-12861:
-----------------------------------

Yes, I'm inclined to close this as a duplicate of SPARK-4039

> Changes to support KMeans with large feature space
> --------------------------------------------------
>
>                 Key: SPARK-12861
>                 URL: https://issues.apache.org/jira/browse/SPARK-12861
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>    Affects Versions: 1.6.0
>            Reporter: Roy Levin
>              Labels: patch
>
>     The problem:
>     -----------------
>     In Spark's KMeans code the center vectors are always represented as dense vectors. As a result, when each such center has a large domain space the algorithm quickly runs out of memory. In my example I have a feature space of around 50000 and k ~= 500. This sums up to around 200MB RAM for the center vectors alone while in fact the center vectors are very sparse and require a lot less RAM.
>     Since I am running on a system with relatively low resources I keep getting OutOfMemory errors. In my setting it is OK to trade off runtime for using less RAM. This is what I set out to do in my solution while allowing users the flexibility to choose.
>     One solution could be to reduce the dimensions of the feature space but this is not always the best approach. For example, when the object space is comprised of users and the feature space of items. In such an example we may want to run kmeans over a feature space which is a function of how many times user i clicked item j. If we reduce the dimensions of the items we will not be able to map the centers vectors back to the items. Moreover in a streaming context detecting the changes WRT previous runs gets more difficult.
>     My solution:
>     ----------------
>     Allow the kmeans algorithm to accept a VectorFactory which decides when vectors used inside the algorithm should be sparse and when they should be dense. For backward compatibility the default behavior is to always make them dense (like the situation is now). But now potentially the user can provide a SmartVectorFactory (or some proprietary VectorFactory) which can decide to make vectors sparse.
>     For this I made the following changes:
>     (1) Added a method called reassign to SparseVectors allowing to change the indices and values
>     (2) Allow axpy to accept SparseVectors
>     (3) create a trait called VectorFactory and two implementations for it that are used within KMeans code
>     To get the above described solution do the following:
>     git clone https://github.com/levin-royl/spark.git -b SupportLargeFeatureDomains
> Note
> ------
> There are some similar issues opened in JIRA in the past, e.g.:
> https://issues.apache.org/jira/browse/SPARK-4039
> https://issues.apache.org/jira/browse/SPARK-1212
> https://github.com/mesos/spark/pull/736
> But the difference is that in the problem I describe reducing the dimensions of the problem (i.e., the feature space) to allow using dense vectors is not suitable. Also, the solution I implemented supports this while allowing full flexibility to the user --- i.e., using the default dense vector implementation or selecting an alternative (only when the default it is not desired). 



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