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Posted to issues@spark.apache.org by "Andrea Matsunaga (JIRA)" <ji...@apache.org> on 2016/11/29 02:08:58 UTC

[jira] [Created] (SPARK-18616) Pure Python Implementation of MLWritable for use in Pipeline

Andrea Matsunaga created SPARK-18616:
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             Summary: Pure Python Implementation of MLWritable for use in Pipeline
                 Key: SPARK-18616
                 URL: https://issues.apache.org/jira/browse/SPARK-18616
             Project: Spark
          Issue Type: Improvement
          Components: ML
    Affects Versions: 2.0.2
         Environment: pyspark
            Reporter: Andrea Matsunaga


When developing an estimator and model completely in python, it is possible to implement the save() function, and it works for a standalone model, but not when added to a Pipeline. The reason is that Pipeline save implementation forces the use of JavaMLWritable, thus also requiring the object to have methods that are meaningful only to Java objects. Pipelines implementation need to have a check for the type of writable object defined.



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