[jira] [Commented] (SPARK-17048) ML model read for custom transformers in a pipeline does not work
[
https://issues.apache.org/jira/browse/SPARK-17048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15592141#comment-15592141
]
Nicolas Long commented on SPARK-17048:
--
I hit this today too. The Scala workaround is simply to create an object of the
same name that extends DefaultParamsReadable. E.g.
{code:java}
class HtmlRemover(val uid: String) extends StringUnaryTransformer[String,
HtmlRemover] with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("htmlremover"))
def createTransformFunc: String => String = s => {
Jsoup.parse(s).body().text()
}
}
object HtmlRemover extends DefaultParamsReadable[HtmlRemover]
{code}
Note that StringUnaryTransformer is a simple custom wrapper trait here.
> 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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[jira] [Commented] (SPARK-17048) ML model read for custom transformers in a pipeline does not work
[
https://issues.apache.org/jira/browse/SPARK-17048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15473009#comment-15473009
]
Yicheng Luo commented on SPARK-17048:
-
The reason this is failing is because in Java the MLReadable trait will become
a become an interface. the read method is implemented as an instance method,
which would require an object of the instance in order to call the read
correctly. Hence, in ReadWrite.scala:447 the invoke method is supplied an null
pointer which would mean that this method is a static method. Yet it is an
instance method thus causing the failure.
> 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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[jira] [Commented] (SPARK-17048) ML model read for custom transformers in a pipeline does not work
[
https://issues.apache.org/jira/browse/SPARK-17048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15426358#comment-15426358
]
Taras Matyashovskyy commented on SPARK-17048:
-
All my examples are located under
https://github.com/tmatyashovsky/spark-ml-samples and are quite self
explanatory.
Issue I am talking about is with any of the custom transformers located here:
https://github.com/tmatyashovsky/spark-ml-samples/tree/master/spark-driver/src/main/java/com/lohika/morning/ml/spark/driver/service/lyrics
In order to overcome it, I added explicitly static method
public static MLReader read() without implementing of any interface,
e.g. MLReadable, DefaultParamsReadable, etc. and that obviously worked.
But that is just a workaround so it would be great to have it fixed in
ReadWrite.scala:447 too.
Please let me know in case of further questions.
> 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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[jira] [Commented] (SPARK-17048) ML model read for custom transformers in a pipeline does not work
[
https://issues.apache.org/jira/browse/SPARK-17048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15422365#comment-15422365
]
Yanbo Liang commented on SPARK-17048:
-
[~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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