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Posted to dev@mahout.apache.org by "Robin Anil (JIRA)" <ji...@apache.org> on 2010/01/05 03:48:54 UTC
[jira] Updated: (MAHOUT-237) Map/Reduce Implementation of Document
Vectorizer
[ https://issues.apache.org/jira/browse/MAHOUT-237?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Robin Anil updated MAHOUT-237:
------------------------------
Attachment: DictionaryVectorizer.patch
single shard based document vectorizer. Needs some tidying up. (Work in progress)
TODO: Split the output of first Map/Reduce into shards of given chunk size
TODO: Map/Reduce job for the merge phase
> Map/Reduce Implementation of Document Vectorizer
> ------------------------------------------------
>
> Key: MAHOUT-237
> URL: https://issues.apache.org/jira/browse/MAHOUT-237
> Project: Mahout
> Issue Type: New Feature
> Affects Versions: 0.3
> Reporter: Robin Anil
> Assignee: Robin Anil
> Fix For: 0.3
>
> Attachments: DictionaryVectorizer.patch
>
>
> Current Vectorizer uses Lucene Index to convert documents into SparseVectors
> Ted is working on a Hash based Vectorizer which can map features into Vectors of fixed size and sum it up to get the document Vector
> This is a pure bag-of-words based Vectorizer written in Map/Reduce.
> The input document is in SequenceFile<Text,Text> . with key = docid, value = content
> First Map/Reduce over the document collection and generate the feature counts.
> Second Sequential pass reads the output of the map/reduce and converts them to SequenceFile<Text, LongWritable> where key=feature, value = unique id
> Second stage should create shards of features of a given split size
> Third Map/Reduce over the document collection, using each shard and create Partial(containing the features of the given shard) SparseVectors
> Fourth Map/Reduce over partial shard, group by docid, create full document Vector
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