Taras Matyashovskyy created SPARK-17048:
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Summary: 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
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 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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