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From "Ajay Saini (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17025) Cannot persist PySpark ML Pipeline model that includes custom Transformer
Date Wed, 26 Jul 2017 17:41:01 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17025?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16101984#comment-16101984
] 

Ajay Saini commented on SPARK-17025:
------------------------------------


I'm currently working on a solution to this that involves building custom persistence support
for Python-only pipeline stages. As of now, you cannot persist a pipeline stage in Python
unless there is a Java implementation of that stage. The framework I'm working on will make
it much easier to implement Python-only persistence of custom stages so that they don't need
to rely on Java.

> Cannot persist PySpark ML Pipeline model that includes custom Transformer
> -------------------------------------------------------------------------
>
>                 Key: SPARK-17025
>                 URL: https://issues.apache.org/jira/browse/SPARK-17025
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML, PySpark
>    Affects Versions: 2.0.0
>            Reporter: Nicholas Chammas
>            Priority: Minor
>
> Following the example in [this Databricks blog post|https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html]
under "Python tuning", I'm trying to save an ML Pipeline model.
> This pipeline, however, includes a custom transformer. When I try to save the model,
the operation fails because the custom transformer doesn't have a {{_to_java}} attribute.
> {code}
> Traceback (most recent call last):
>   File ".../file.py", line 56, in <module>
>     model.bestModel.save('model')
>   File "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
line 222, in save
>   File "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
line 217, in write
>   File "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/util.py",
line 93, in __init__
>   File "/usr/local/Cellar/apache-spark/2.0.0/libexec/python/lib/pyspark.zip/pyspark/ml/pipeline.py",
line 254, in _to_java
> AttributeError: 'PeoplePairFeaturizer' object has no attribute '_to_java'
> {code}
> Looking at the source code for [ml/base.py|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/base.py],
I see that not even the base Transformer class has such an attribute.
> I'm assuming this is missing functionality that is intended to be patched up (i.e. [like
this|https://github.com/apache/spark/blob/acaf2a81ad5238fd1bc81e7be2c328f40c07e755/python/pyspark/ml/classification.py#L1421-L1433]).
> I'm not sure if there is an existing JIRA for this (my searches didn't turn up clear
results).



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