Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/11866#discussion_r56911884
--- Diff: python/pyspark/ml/pipeline.py ---
@@ -206,49 +167,65 @@ def save(self, path):
@classmethod
@since("2.0.0")
def read(cls):
- """Returns an JavaMLReader instance for this class."""
+ """Returns an MLReader instance for this class."""
return PipelineMLReader(cls)
@classmethod
- @since("2.0.0")
- def load(cls, path):
- """Reads an ML instance from the input path, a shortcut of
`read().load(path)`."""
- return cls.read().load(path)
+ def _transfer_stage_from_java(cls, java_stage):
--- End diff --
It is a little confusing to call it `java_stage` while the expected input
is a pipeline. Shall we rename it to `_from_java_pipeline` and
`_transfer_stage_to_java` to `_to_java_pipeline`? If we have to use the same
method name, I would recommend `_from_java` and `_to_java` instead.
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