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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/02/05 20:22:42 UTC
[jira] [Assigned] (SPARK-17629) Should ml Word2Vec findSynonyms
match the mllib implementation?
[ https://issues.apache.org/jira/browse/SPARK-17629?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-17629:
------------------------------------
Assignee: Apache Spark
> Should ml Word2Vec findSynonyms match the mllib implementation?
> ---------------------------------------------------------------
>
> Key: SPARK-17629
> URL: https://issues.apache.org/jira/browse/SPARK-17629
> Project: Spark
> Issue Type: Question
> Reporter: Asher Krim
> Assignee: Apache Spark
> Priority: Minor
>
> ml Word2Vec's findSynonyms methods depart from mllib in that they return distributed results, rather than the results directly:
> {code}
> def findSynonyms(word: String, num: Int): DataFrame = {
> val spark = SparkSession.builder().getOrCreate()
> spark.createDataFrame(wordVectors.findSynonyms(word, num)).toDF("word", "similarity")
> }
> {code}
> What was the reason for this decision? I would think that most users would request a reasonably small number of results back, and want to use them directly on the driver, similar to the _take_ method on dataframes. Returning parallelized results creates a costly round trip for the data that doesn't seem necessary.
> The original PR: https://github.com/apache/spark/pull/7263
> [~MechCoder] - do you perhaps recall the reason?
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