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https://issues.apache.org/jira/browse/SPARK-3770?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-3770.
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Resolution: Fixed
Fix Version/s: 1.2.0
Issue resolved by pull request 2636
[https://github.com/apache/spark/pull/2636]
> The userFeatures RDD from MatrixFactorizationModel isn't accessible from the
> python bindings
> --------------------------------------------------------------------------------------------
>
> Key: SPARK-3770
> URL: https://issues.apache.org/jira/browse/SPARK-3770
> Project: Spark
> Issue Type: Improvement
> Components: MLlib, PySpark
> Reporter: Michelangelo D'Agostino
> Assignee: Michelangelo D'Agostino
> Fix For: 1.2.0
>
>
> We need access to the underlying latent user features from python. However,
> the userFeatures RDD from the MatrixFactorizationModel isn't accessible from
> the python bindings. I've fixed this with a PR that I'll submit shortly that
> adds a method to the underlying scala class to turn the RDD[(Int,
> Array[Double])] to an RDD[String]. This is then accessed from the python
> recommendation.py
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