[
https://issues.apache.org/jira/browse/MAHOUT-612?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Frank Scholten updated MAHOUT-612:
----------------------------------
Fix Version/s: Backlog
Putting this in backlog for now. As much as I like the idea of this patch,
improving parts of the clustering code I work with regularly has a higher
priority for me. So far the kmeans, canopy and seq2sparse jobs have been
refactored to have a bean configuration. If you want to help with this, check
out the github repo at
https://github.com/frankscholten/mahout/tree/MAHOUT-612-0.5
> Simplify configuring and running Mahout MapReduce jobs from Java using Java
> bean configuration
> ----------------------------------------------------------------------------------------------
>
> Key: MAHOUT-612
> URL: https://issues.apache.org/jira/browse/MAHOUT-612
> Project: Mahout
> Issue Type: Improvement
> Components: Classification, Clustering, Collaborative Filtering
> Affects Versions: 0.4, 0.5
> Reporter: Frank Scholten
> Assignee: Sean Owen
> Fix For: Backlog
>
> Attachments: MAHOUT-612-canopy.patch, MAHOUT-612-kmeans.patch,
> MAHOUT-612-v2.patch, MAHOUT-612.patch
>
>
> Most of the Mahout features require running several jobs in sequence. This
> can be done via the command line or using one of the driver classes.
> Running and configuring a Mahout job from Java requires using either the
> Driver's static methods or creating a String array of parameters and pass
> them to the main method of the job. If we can instead configure jobs through
> a Java bean or factory we it will be type safe and easier to use in by DI
> frameworks such as Spring and Guice.
> I have added a patch where I factored out a KMeans MapReduce job plus a
> configuration Java bean, from KMeansDriver.buildClustersMR(...)
> * The KMeansMapReduceConfiguration takes care of setting up the correct
> values in the Hadoop Configuration object and initializes defaults. I copied
> the config keys from KMeansConfigKeys.
> * The KMeansMapReduceJob contains the code for the actual algorithm running
> all iterations of KMeans and returns the KMeansMapReduceConfiguration, which
> contains the cluster path for the final iteration.
> I like to extend this approach to other Hadoop jobs for instance the job for
> creating points in KMeansDriver, but I first want some feedback on this.
> One of the benefits of this approach is that it becomes easier to chain jobs.
> For instance we can chain Canopy to KMeans by connecting the output dir of
> Canopy's configuration to the input dir of the configuration of the KMeans
> job next in the chain. Hadoop's JobControl class can then be used to connect
> and execute the entire chain.
> This approach can be further improved by turning the configuration bean into
> a factory for creating MapReduce or sequential jobs. This would probably
> remove some duplicated code in the KMeansDriver.
--
This message is automatically generated by JIRA.
If you think it was sent incorrectly, please contact your JIRA administrators:
https://issues.apache.org/jira/secure/ContactAdministrators!default.jspa
For more information on JIRA, see: http://www.atlassian.com/software/jira
