[jira] [Commented] (SPARK-6065) Optimize word2vec.findSynonyms speed

["Franck Zhang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-6065?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14958585#comment-14958585 ] 

Franck Zhang  commented on SPARK-6065:
--------------------------------------

When I used the same dataset (text8  -  around100mb), same parameters for training, 
python runs 10x faster than spark in my notebook(2015 MacBook Pro 15")
I think the word2vec model in spark still have a long way to go ...

> Optimize word2vec.findSynonyms speed
> ------------------------------------
>
>                 Key: SPARK-6065
>                 URL: https://issues.apache.org/jira/browse/SPARK-6065
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 1.2.0
>            Reporter: Joseph K. Bradley
>            Assignee: Manoj Kumar
>             Fix For: 1.4.0
>
>
> word2vec.findSynonyms iterates through the entire vocabulary to find similar words.  This is really slow relative to the [gcode-hosted word2vec implementation | https://code.google.com/p/word2vec/].  It should be optimized by storing words in a datastructure designed for finding nearest neighbors.
> This would require storing a copy of the model (basically an inverted dictionary), which could be a problem if users have a big model (e.g., 100 features x 10M words or phrases = big dictionary).  It might be best to provide a function for converting the model into a model optimized for findSynonyms.



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