[GitHub] spark pull request #19857: [SPARK-22667][ML] Fix model-specific optimization...

[WeichenXu123]

GitHub user WeichenXu123 opened a pull request:

    https://github.com/apache/spark/pull/19857

    [SPARK-22667][ML] Fix model-specific optimization support for ML tuning: 
Python API

    ## What changes were proposed in this pull request?
    
    Python CrossValidator/TrainValidationSplit:
    With base Estimator implemented in Scala/Java
    → Convert base Estimator to Scala/Java object, and call the JVM fit() (as 
in Weichen’s comment)
    With base Estimator implemented in Python
    → Python needs the same machinery for multi-model fitting and parallelism 
as Scala.  We can call directly into it. New API added:
    ```
    class Estimator:
      def parallelFit(self, dataset, paramMaps, threadPool, modelCallback):
    ```
    
    **Note** This PR also fix the `# TODO: persist average metrics as well` in 
CV/TVS. Because the testsuite need to check consistency of `avgMetrics` so this 
need to be fixed.
    If this need backport to old spark version, I can split it to a separate PR.
    
    ## How was this patch tested?
    
    Existing UT already covers each code paths which need test.


You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/WeichenXu123/spark fix_model_spec_optim_py

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/spark/pull/19857.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #19857
    
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commit 980c8ec87ddbc9f938942e78bb4cfe9753722bd2
Author: WeichenXu <weichen...@databricks.com>
Date:   2017-11-30T10:08:55Z

    init pr

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