[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-10-10 Thread srowen
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Merged to master. I wasn't clear whether this was a pressing problem that 
needed to be backported.


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-10-09 Thread WeichenXu123
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@srowen Any other comments? Thanks!


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-20 Thread WeichenXu123
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ping @srowen Any other comments ?


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-19 Thread WeichenXu123
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Jenkins, test this please.


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-18 Thread jkbradley
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These are fair arguments.  I guess it makes sense to throw an exception; 
that's fine with me.


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-12 Thread srowen
Github user srowen commented on the issue:

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That's my sentiment too, that it is likely patching over another problem. 
I'd go with Joseph on this, but at least, issue a warning maybe?

I also would support throwing an exception to fail clearly and early in 
this case.


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-12 Thread sethah
Github user sethah commented on the issue:

https://github.com/apache/spark/pull/19106
  
Ok, I guess I'm surprised that someone even noticed this...

So, basically, we are changing the behavior of a private function for a 
specific case which is actually impossible to ever run into. I don't see the 
need. I know @jkbradley mentioned that it might happen for linear models, but 
this method is only used for RandomForest and DecisionTree. @srowen mentioned 
that finding the root cause would be better, and seems to me the root cause is: 
there is no root cause. 

Anyway, if we are analyzing the behavior of this method without considering 
the context from which it's called, then we need to test for not only all zero 
probabilities but negative ones as well. Otherwise, we're shoring up one edge 
case without attending to others.

If it is not currently possible to call this method with all zero counts, 
and we don't want it to be possible ever in the future, why don't we just throw 
an error? I'm also fine with not changing this at all. 


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-12 Thread smurching
Github user smurching commented on the issue:

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@sethah I haven't heard of anybody hitting this issue in practice, but it 
did seem best to ensure that valid probability distributions would be produced 
regardless of input. There was some discussion of this in the JIRA: 
https://issues.apache.org/jira/browse/SPARK-21770


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-11 Thread sethah
Github user sethah commented on the issue:

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I'm confused how this issue was discovered in the first place. Did someone 
actually train an RF/DT and receive all zero probabilities? If so, shouldn't 
there be a unit test that recreates that scenario? 

Anyway, AFAICT the only way this could happen is if the RandomForest was 
trained on an empty DataFrame which couldn't happen because it would fail 
before the train method was called anyway.


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-11 Thread smurching
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This looks good to me! @srowen would you be able to give it another look?


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-02 Thread AmplabJenkins
Github user AmplabJenkins commented on the issue:

https://github.com/apache/spark/pull/19106
  
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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-02 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/19106
  
**[Test build #81344 has 
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 for PR 19106 at commit 
[`31510c9`](https://github.com/apache/spark/commit/31510c9ea7a83cdc69c0fb2d9175cfae76c2cbe8).
 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-02 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/19106
  
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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-01 Thread AmplabJenkins
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https://github.com/apache/spark/pull/19106
  
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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-01 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/19106
  
**[Test build #81336 has 
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/81336/testReport)**
 for PR 19106 at commit 
[`a1dc9fc`](https://github.com/apache/spark/commit/a1dc9fc362c079da1b8536383d38e476f53d5ee4).
 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-01 Thread AmplabJenkins
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https://github.com/apache/spark/pull/19106
  
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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...

2017-09-01 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/19106
  
**[Test build #81336 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/81336/testReport)**
 for PR 19106 at commit 
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