[jira] [Updated] (SPARK-5981) pyspark ML models should support predict/transform on vector within map

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-5981?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley updated SPARK-5981:
-------------------------------------
    Target Version/s:   (was: 1.5.0)

> 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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