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https://issues.apache.org/jira/browse/SPARK-1406?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13958776#comment-13958776
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Thomas Darimont edited comment on SPARK-1406 at 4/3/14 12:49 PM:
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Hi Sean,
thanks for responding so quickly :)
Sure you can do that of course (thats what I currently do), but there IMHO many
interesting use cases that would benefit from having direct PMML support, e.g.:
1) Initialize an algorithm with a set of prepared parameters by loading a PMML
file and evaluate the algorthm with spark's infrastructure.
2) Abstract the configuration or construction of an Algorithm via some kind of
Producer that gets the PMML model as an Input and returns a fully configured
Spark representation of the algorithm which is encoded in the PMML.
3) Support hot-replacing an algorithm (configuration) at runtime by just
providing an updated PMML model to the spark infrastructure.
4) Use the transformation / normalization or even dynamic model selection
support build into PMML to select the appropriate algorithm (configuration)
based on the input.
You could even use JPMML to get the PMML object model as a starting point.
was (Author: thomasd):
Hi Sean,
thanks for responding so quickly :)
Sure you can do that of course (thats what I currently do), but there IMHO many
interesting use cases that would benefit from having direct PMML support, e.g.:
1) Initialize an algorithm with a set of prepared parameters by loading a PMML
file and evaluate the algorthm with spark's infrastructure.
2) Abstract the configuration or construction of an Algorithm via some kind of
Producer that gets the PMML model as an Input and returns a fully configured
Spark representation of the algorithm which is encoded in the PMML.
3) Support hot-replacing an algorithm (configuration) at runtime by just
providing an updated PMML model to the spark infrastructure.
You could even use JPMML to get the PMML object model as a starting point.
> PMML model evaluation support via MLib
> --------------------------------------
>
> Key: SPARK-1406
> URL: https://issues.apache.org/jira/browse/SPARK-1406
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Thomas Darimont
>
> It would be useful if spark would provide support the evaluation of PMML
> models (http://www.dmg.org/v4-2/GeneralStructure.html).
> This would allow to use analytical models that were created with a
> statistical modeling tool like R, SAS, SPSS, etc. with Spark (MLib) which
> would perform the actual model evaluation for a given input tuple. The PMML
> model would then just contain the "parameterization" of an analytical model.
> Other projects like JPMML-Evaluator do a similar thing.
> https://github.com/jpmml/jpmml/tree/master/pmml-evaluator
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