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https://issues.apache.org/jira/browse/PIO-192?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16680893#comment-16680893
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ASF GitHub Bot commented on PIO-192:
------------------------------------

shimamoto commented on issue #494: [PIO-192] Enhance PySpark support
URL: https://github.com/apache/predictionio/pull/494#issuecomment-437253361
 
 
   @marevol @takezoe Can you take a look?

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> Enhance PySpark support
> -----------------------
>
>                 Key: PIO-192
>                 URL: https://issues.apache.org/jira/browse/PIO-192
>             Project: PredictionIO
>          Issue Type: Improvement
>          Components: Core
>    Affects Versions: 0.13.0
>            Reporter: Takako Shimamoto
>            Assignee: Takako Shimamoto
>            Priority: Major
>
> h3. Summary
> Enhance the pypio, which is the Python API for PIO.
> h3. Goals
> The limitations of the current Python support always force developers to have 
> access to sbt. This enhancement will get rid of the build phase.
> h3. Description
> A Python engine template requires 3 files:
> * Python code to specify for the --main-py-file option
> * template.json
> {code:json}
> {"pio": {"version": { "min": "0.14.0-SNAPSHOT" }}}
> {code}
> * engine.json
> {code:json}
> {
>   "id": "default",
>   "description": "Default settings",
>   "engineFactory": "org.apache.predictionio.e2.engine.PythonEngine",
>   "algorithms": [
>     {
>       "name": "default",
>       "params": {
>         "name": "BHPApp"
>       }
>     }
>   ],
>   "serving": {
>     "params": {
>       "columns": ["prediction"]
>     }
>   }
> }
> {code}
> h4. pypio module
> Developers can use the pypio module with jupyter notebook and Python code.
> First, import the necessary modules.
> {code:python}
> from pypio import pypio
> {code}
> Once the module in imported, the first step is to initialize the pypio module.
> {code:python}
> pypio.init()
> {code}
> Next, find data from the event store.
> {code:python}
> event_df = pypio.find('BHPApp')
> {code}
> And then, save the model.
> {code:python}
> # model is a PipelineModel, which is produced after a Pipeline’s fit() method 
> runs
> pipeline = Pipeline(...)
> model = pipeline.fit(train_df)
> pypio.save(model)
> {code}



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