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https://issues.apache.org/jira/browse/SPARK-5981?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14349179#comment-14349179
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Manoj Kumar commented on SPARK-5981:
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Sorry for being slow. But do you mean when predict is called in PySpark, this
calls the stored model in scala, which is used in PySpark to predict (instead
of doing the whole thing in PySpark itself as done by GLM's)?
But even in this case isn't the SparkContext used during
`JavaModelWrapper.call` (which cannot be accessed by the worker nodes)?
> pyspark ML models should support predict/transform on vector within map
> -----------------------------------------------------------------------
>
> Key: SPARK-5981
> URL: https://issues.apache.org/jira/browse/SPARK-5981
> Project: Spark
> Issue Type: Improvement
> Components: MLlib, PySpark
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
>
> Currently, most Python models only have limited support for single-vector
> prediction.
> E.g., one can call {code}model.predict(myFeatureVector){code} for a single
> instance, but that fails within a map for Python ML models and transformers
> which use JavaModelWrapper:
> {code}
> data.map(lambda features: model.predict(features))
> {code}
> This fails because JavaModelWrapper.call uses the SparkContext (within the
> transformation). (It works for linear models, which do prediction within
> Python.)
> Supporting prediction within a map would require storing the model and doing
> prediction/transformation within Python.
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