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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2014/11/25 05:33:12 UTC
[jira] [Commented] (SPARK-3588) Gaussian Mixture Model clustering

    [ https://issues.apache.org/jira/browse/SPARK-3588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14224042#comment-14224042 ] 

Xiangrui Meng commented on SPARK-3588:
--------------------------------------

[~MeethuMathew] Just want to check with you whether you are working on the Scala implementation. [~tgaloppo] sent out a PR in SPARK-4156 . If you haven't spent much time on the Scala implementation, I'd like to invite you to review that PR, or we can think of a way to merge both implementations. Does it sound good to you?

> Gaussian Mixture Model clustering
> ---------------------------------
>
>                 Key: SPARK-3588
>                 URL: https://issues.apache.org/jira/browse/SPARK-3588
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib, PySpark
>            Reporter: Meethu Mathew
>            Assignee: Meethu Mathew
>         Attachments: GMMSpark.py
>
>
> Gaussian Mixture Models (GMM) is a popular technique for soft clustering. GMM models the entire data set as a finite mixture of Gaussian distributions,each parameterized by a mean vector µ ,a covariance matrix ∑ and  a mixture weight π. In this technique, probability of  each point to belong to each cluster is computed along with the cluster statistics.
> We have come up with an initial distributed implementation of GMM in pyspark where the parameters are estimated using the  Expectation-Maximization algorithm.Our current implementation considers diagonal covariance matrix for each component.
> We did an initial benchmark study on a  2 node Spark standalone cluster setup where each node config is 8 Cores,8 GB RAM, the spark version used is 1.0.0. We also evaluated python version of k-means available in spark on the same datasets.
> Below are the results from this benchmark study. The reported stats are average from 10 runs.Tests were done on multiple datasets with varying number of features and instances.
> ||&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Dataset  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;||&nbsp;&nbsp;&nbsp;Gaussian mixture model&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kmeans(Python)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;||         
> |Instances|Dimensions |Avg time per iteration|Time for  100 iterations |Avg time per iteration |Time for 100 iterations | 
> |0.7million| &nbsp;&nbsp;&nbsp;13 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;   7s &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;     | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;     12min &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;   |  &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;     13s  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  |  &nbsp;&nbsp;&nbsp;&nbsp;    26min &nbsp;&nbsp;&nbsp;    |
> |1.8million| &nbsp;&nbsp;&nbsp;11 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|   &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  17s &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;     | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;     29min &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  |  &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;     33s  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;   |  &nbsp;&nbsp;&nbsp;&nbsp;    53min &nbsp;&nbsp;&nbsp;  |
> |10million|&nbsp;&nbsp;&nbsp;16 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  1.6min &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;    | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;     2.7hr &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;   |  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;     1.2min &nbsp;&nbsp;&nbsp;&nbsp;    |  &nbsp;&nbsp;&nbsp;&nbsp;    2hr &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;    |



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