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https://issues.apache.org/jira/browse/SPARK-17048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15422365#comment-15422365
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Yanbo Liang commented on SPARK-17048:
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[~taras.matyashov...@gmail.com] Would you mind to share your code or provide a
simple example to make others can help you diagnose this issue? Thanks!
> ML model read for custom transformers in a pipeline does not work
> ------------------------------------------------------------------
>
> Key: SPARK-17048
> URL: https://issues.apache.org/jira/browse/SPARK-17048
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.0.0
> Environment: Spark 2.0.0
> Java API
> Reporter: Taras Matyashovskyy
> Labels: easyfix, features
> Original Estimate: 2h
> Remaining Estimate: 2h
>
> 0. Use Java API :(
> 1. Create any custom ML transformer
> 2. Make it MLReadable and MLWritable
> 3. Add to pipeline
> 4. Evaluate model, e.g. CrossValidationModel, and save results to disk
> 5. For custom transformer you can use DefaultParamsReader and
> DefaultParamsWriter, for instance
> 6. Load model from saved directory
> 7. All out-of-the-box objects are loaded successfully, e.g. Pipeline,
> Evaluator, etc.
> 8. Your custom transformer will fail with NPE
> Reason:
> ReadWrite.scala:447
> cls.getMethod("read").invoke(null).asInstanceOf[MLReader[T]].load(path)
> In Java this only works for static methods.
> As we are implementing MLReadable or MLWritable, then this call should be
> instance method call.
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