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https://issues.apache.org/jira/browse/SPARK-17048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15592141#comment-15592141
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Nicolas Long edited comment on SPARK-17048 at 10/20/16 3:32 PM:
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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}
But it would be nice to be able to not have to have the singleton object and
simply add the trait to the transformer itself.
Note that StringUnaryTransformer is a simple custom wrapper trait here.
was (Author: nicl):
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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