[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-15 Thread jkbradley
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https://github.com/apache/spark/pull/20594
  
Success!  Merged to branch-2.3 too.


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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-15 Thread jkbradley
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Well I succeeded in merging this with master, but the merge script isn't 
working for branch-2.3.  I wait to see if the read-only repo syncs and fixes 
the issue.


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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

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Merging with master and branch-2.3


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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-14 Thread jkbradley
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Thanks!  LGTM pending tests


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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-14 Thread viirya
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Thanks @jkbradley ! I've updated this based on your comments.


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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-14 Thread mgaido91
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@jkbradley thanks for your answer. I think that the 3rd approach you 
suggested on the JIRA is the right way to go on a long term plan. Personally, I 
disagree with you when you say that we should keep the default values. I think 
that changing a default value doesn't happen often and if it happens it is not 
a problem: if the user cares about the value of a parameter, he sets it. But 
this is just my opinion.


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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-14 Thread jkbradley
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@mgaido91 Thanks for your thoughts.  We do need to persist default values; 
please check out the JIRA.  For fixing Python, I think the best fix will be to 
transfer the default & explicitly set Params to Java separately, rather than 
treating them all as explicitly set.


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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-14 Thread mgaido91
Github user mgaido91 commented on the issue:

https://github.com/apache/spark/pull/20594
  
@WeichenXu123 @viirya as I said in the other PR, I think no default value 
should be persisted. #20410 and #18982 are not regression: they are problem 
which have been present in all the release so far, but they are showing up more 
and more "thanks" to all the models having the dualism inputCol/inputCols.

Every usage of `isSet`on Scala side is a problem with the Python API until 
either one of them will be merged. And this issue is the same. After 
persisting, every usage of `isSet` is not working as intended. Therefore I'd be 
for either not to store any default value or store them writing explicitly that 
they are default values. 


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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-13 Thread viirya
Github user viirya commented on the issue:

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Because this is a quick fix, my idea is to have a surface patch that 
doesn't change existing API. The approach of adding parameter to 
`DefaultParamsWriter.saveMetadata` also sounds good to me, but the parameter 
seems useless if we get rid of this quick fix in the future.

Instead of adding parameter, I think we can pass the `paramMap` parameter 
when calling `saveMetadata`.

For #20410 and #18982, I have a question, are they regression? Seems to me 
they are not new issues to 2.3.




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[GitHub] spark issue #20594: [SPARK-23377][ML] Fixes Bucketizer with multiple columns...

2018-02-13 Thread WeichenXu123
Github user WeichenXu123 commented on the issue:

https://github.com/apache/spark/pull/20594
  
I thought again, instead of "removing default value and restore it again 
later (which may cause some side effects)", maybe the better way is, add a 
parameter to `DefaultParamsWriter.saveMetadata`, specify which default param 
need to skip when saving.

@mgaido91 Yes I agree with you. Either #20410 or #18982 need to be merged 
to 2.3, the related issue is possible to cause some strange bugs.

cc @jkbradley 


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retest this please.


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retest this please.


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2018-02-12 Thread viirya
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cc @jkbradley 


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