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From "Nick Pentreath (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-19962) add DictVectorizor for DataFrame
Date Thu, 16 Mar 2017 07:10:41 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19962?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15927601#comment-15927601
] 

Nick Pentreath commented on SPARK-19962:
----------------------------------------

You may also want to take a look at https://issues.apache.org/jira/browse/SPARK-13969. I plan
to work on the {{FeatureHasher}} (will be for Spark 2.3 now). I do think a {{DictVectorizer}}
would be useful and is conceptually the same as my hasher but maintaining an exact feature
mapping.

> add DictVectorizor for DataFrame
> --------------------------------
>
>                 Key: SPARK-19962
>                 URL: https://issues.apache.org/jira/browse/SPARK-19962
>             Project: Spark
>          Issue Type: Wish
>          Components: ML
>    Affects Versions: 2.1.0
>            Reporter: yu peng
>              Labels: features
>
> it's really useful to have something like sklearn.feature_extraction.DictVectorizor
> Since out features lives in json/data frame like format and classifier/regressors only
take vector input. so there is a gap between them.
> something like 
> ```
> df = sqlCtx.createDataFrame([Row(age=1, gender='male', country='cn', hobbies=['sing',
'dance']),Row(age=3, gender='female', country='us',  hobbies=['sing']), ])
> df.show()
> |age|gender|country|hobbies|
> |1|male|cn|[sing, dance]|
> |3|female|us|[sing]|
> import DictVectorizor
> vec = DictVectorizor()
> matrix = vec.fit_transform(df)
> matrix.show()
> |features|
> |[1, 0, 1, 0, 1, 1, 1]|
> |[3, 1, 0, 1, 0, 1, 1]|
> vec.show()
> |feature_name| feature_dimension|
> |age|0|
> |gender=female|1|
> |gender=male|2|
> |country=us|3|
> |country=cn|4|
> |hobbies=sing|5|
> |hobbies=dance|6|
> ```



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