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