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https://issues.apache.org/jira/browse/SPARK-21199?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16063416#comment-16063416
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Franklyn Dsouza edited comment on SPARK-21199 at 6/26/17 5:16 PM:
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For this particular scenario I have a table with two columns one is a string
`document_type` and the other is a an array of tokens for the document. I want
to do a TF-IDF on these tokens.
the IDF needs to be done per `document_type` so i pivot on `document_type` and
then do the IDF on the TF vetors.
This pivoting introduces nulls for missing columns that need to be imputed. I
can't impute array type either and fixing it at the token generation step would
involve a lot of left joins to align various data sources.
was (Author: franklyndsouza):
For this particular scenario I have a table with two columns one is a string
`document_type` and the other is a an array of tokens for the document. I want
to do a TF-IDF on these tokens.
the IDF needs to be done per `document_type` so i do pivot on `document_type`
and then do the IDF on the TF vetors.
This pivoting introduces nulls for missing columns that need to be imputed. I
can't impute array type either and fixing it at the token generation step would
involve a lot of left joins to align various data sources.
> Its not possible to impute Vector types
> ---------------------------------------
>
> Key: SPARK-21199
> URL: https://issues.apache.org/jira/browse/SPARK-21199
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.0.0, 2.1.1
> Reporter: Franklyn Dsouza
>
> There are cases where nulls end up in vector columns in dataframes. Currently
> there is no way to fill in these nulls because its not possible to create a
> literal vector column expression using lit().
> Also the entire pyspark ml api will fail when they encounter nulls so this
> makes it hard to work with the data.
> I think that either vector support should be added to the imputer or vectors
> should be supported in column expressions so they can be used in a coalesce.
> [~mlnick]
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