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From "Cyanny (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-22872) Spark ML Pipeline Model Persistent Support Save Schema Info
Date Fri, 22 Dec 2017 08:39:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-22872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Cyanny updated SPARK-22872:
---------------------------
    Description: 
Hi all,
I have a project about model transformation with PMML, it  needs to transform models with
different types to pmml files.
Moreover, JPMML(https://github.com/jpmml) has provided tools to do that,such as jpmml-sklearn,
jpmml-xgboost etc. Our transformation API parameters must be concise and simple, in other
words the less the better.

I came with a issue that, sklearn, tensorflow, and lightgbm can produce only one model file,
including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema info in the
model file. However, JPMML-SPARK converter needs two arguments: Data Schema and PipelineModel

*Can spark PipelineModel include input data schema as metadata when do export? *

The situations about machine learning libraries to jpmml are as the attached image, only xgboost
and spark can't include schema info in exported model file.


  was:
Hi all,
I have a project about model transformation with PMML, it  needs to transform models with
different types to pmml files.
Moreover, JPMML(https://github.com/jpmml/jpmml-sparkml) has provided many tools to do that.
I need to provide a uniform API for user, the API parameters must be concise and simple, in
other words the less the better.

I came with a issue that, sklearn, tensorflow, and lightgbm can produce only one model file,
including schema info and model data info.
but Spark PipelineModel only export a model file in parquet, there is no schema info in the
model file. However, JPMML-SPARK converter needs two arguments: Data Schema and PipelineModel

*Can spark PipelineModel include input data schema as metadata when do export? *

The situations about machine learning libraries to jpmml are as the attached img, only xgboost
and spark can't include schema info in exported model file.



> Spark ML Pipeline Model Persistent Support Save Schema Info
> -----------------------------------------------------------
>
>                 Key: SPARK-22872
>                 URL: https://issues.apache.org/jira/browse/SPARK-22872
>             Project: Spark
>          Issue Type: IT Help
>          Components: ML
>    Affects Versions: 2.2.0
>            Reporter: Cyanny
>            Priority: Minor
>         Attachments: jpmml-research.jpg
>
>
> Hi all,
> I have a project about model transformation with PMML, it  needs to transform models
with different types to pmml files.
> Moreover, JPMML(https://github.com/jpmml) has provided tools to do that,such as jpmml-sklearn,
jpmml-xgboost etc. Our transformation API parameters must be concise and simple, in other
words the less the better.
> I came with a issue that, sklearn, tensorflow, and lightgbm can produce only one model
file, including schema info and model data info.
> but Spark PipelineModel only export a model file in parquet, there is no schema info
in the model file. However, JPMML-SPARK converter needs two arguments: Data Schema and PipelineModel
> *Can spark PipelineModel include input data schema as metadata when do export? *
> The situations about machine learning libraries to jpmml are as the attached image, only
xgboost and spark can't include schema info in exported model file.



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