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Posted to issues@spark.apache.org by "Huaxin Gao (Jira)" <ji...@apache.org> on 2020/08/21 23:16:00 UTC
[jira] [Resolved] (SPARK-32662) CountVectorizerModel: Remove
requirement for minimum vocabulary size
[ https://issues.apache.org/jira/browse/SPARK-32662?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Huaxin Gao resolved SPARK-32662.
--------------------------------
Fix Version/s: 3.1.0
Resolution: Fixed
Issue resolved by pull request 29482
[https://github.com/apache/spark/pull/29482]
> CountVectorizerModel: Remove requirement for minimum vocabulary size
> --------------------------------------------------------------------
>
> Key: SPARK-32662
> URL: https://issues.apache.org/jira/browse/SPARK-32662
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 3.0.0
> Reporter: Jatin Puri
> Assignee: Jatin Puri
> Priority: Minor
> Fix For: 3.1.0
>
>
> Currently `CountVectorizer.scala` has the following requirement:
> {code:java}
> require(vocab.length > 0, "The vocabulary size should be > 0. Lower minDF as necessary."){code}
> But this is not a necessary constraint. It should be able to function even for empty vocabulary case.
> This also gives the ability to run the model over empty datasets. HashingTF works fine in such scenarios. CountVectorizer doesn't.
>
> spark-user discussion reference: [http://apache-spark-user-list.1001560.n3.nabble.com/Ability-to-have-CountVectorizerModel-vocab-as-empty-td38396.html]
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