Re: [VOTE] Release Spark 3.2.1 (RC2)

2022-01-22 Thread Mridul Muralidharan
+1

Signatures, digests, etc check out fine.
Checked out tag and build/tested with -Pyarn -Pmesos -Pkubernetes

Regards,
Mridul

On Fri, Jan 21, 2022 at 9:01 PM Sean Owen  wrote:

> +1 with same result as last time.
>
> On Thu, Jan 20, 2022 at 9:59 PM huaxin gao  wrote:
>
>> Please vote on releasing the following candidate as Apache Spark version
>> 3.2.1. The vote is open until 8:00pm Pacific time January 25 and passes if
>> a majority +1 PMC votes are cast, with a minimum of 3 +1 votes. [ ] +1
>> Release this package as Apache Spark 3.2.1[ ] -1 Do not release this
>> package because ... To learn more about Apache Spark, please see
>> http://spark.apache.org/ The tag to be voted on is v3.2.1-rc2 (commit
>> 4f25b3f71238a00508a356591553f2dfa89f8290):
>> https://github.com/apache/spark/tree/v3.2.1-rc2
>> The release files, including signatures, digests, etc. can be found at:
>> https://dist.apache.org/repos/dist/dev/spark/v3.2.1-rc2-bin/
>> Signatures used for Spark RCs can be found in this file:
>> https://dist.apache.org/repos/dist/dev/spark/KEYS The staging repository
>> for this release can be found at:
>> https://repository.apache.org/content/repositories/orgapachespark-1398/
>>
>> The documentation corresponding to this release can be found at:
>> https://dist.apache.org/repos/dist/dev/spark/v3.2.1-rc2-docs/_site/
>> The list of bug fixes going into 3.2.1 can be found at the following URL:
>> https://s.apache.org/yu0cy
>>
>> This release is using the release script of the tag v3.2.1-rc2. FAQ
>> = How can I help test this release?
>> = If you are a Spark user, you can help us test
>> this release by taking an existing Spark workload and running on this
>> release candidate, then reporting any regressions. If you're working in
>> PySpark you can set up a virtual env and install the current RC and see if
>> anything important breaks, in the Java/Scala you can add the staging
>> repository to your projects resolvers and test with the RC (make sure to
>> clean up the artifact cache before/after so you don't end up building with
>> a out of date RC going forward).
>> === What should happen to JIRA
>> tickets still targeting 3.2.1? ===
>> The current list of open tickets targeted at 3.2.1 can be found at:
>> https://issues.apache.org/jira/projects/SPARK and search for "Target
>> Version/s" = 3.2.1 Committers should look at those and triage. Extremely
>> important bug fixes, documentation, and API tweaks that impact
>> compatibility should be worked on immediately. Everything else please
>> retarget to an appropriate release. == But my bug isn't
>> fixed? == In order to make timely releases, we will
>> typically not hold the release unless the bug in question is a regression
>> from the previous release. That being said, if there is something which is
>> a regression that has not been correctly targeted please ping me or a
>> committer to help target the issue.
>>
>


Re: Log likelhood in GeneralizedLinearRegression

2022-01-22 Thread Sean Owen
This exists in the evaluator MulticlassClassificationEvaluator instead
(which can be used for binary), does that work?

On Sat, Jan 22, 2022 at 4:36 AM Phillip Henry 
wrote:

> Hi,
>
> As far as I know, there is no function to generate the log likelihood from
> a GeneralizedLinearRegression model. Are there any plans to implement one?
>
> I've coded my own in PySpark and in testing it agrees with the values we
> get from the Python library StatsModels to one part in a million. It's
> kinda yucky code as it relies on some inefficient UDFs but I could port it
> to Scala.
>
> Would anybody be interested in me raising a PR and coding an efficient
> Scala implementation that can be called from PySpark?
>
> Regards,
>
> Phillip
>
>


Log likelhood in GeneralizedLinearRegression

2022-01-22 Thread Phillip Henry
Hi,

As far as I know, there is no function to generate the log likelihood from
a GeneralizedLinearRegression model. Are there any plans to implement one?

I've coded my own in PySpark and in testing it agrees with the values we
get from the Python library StatsModels to one part in a million. It's
kinda yucky code as it relies on some inefficient UDFs but I could port it
to Scala.

Would anybody be interested in me raising a PR and coding an efficient
Scala implementation that can be called from PySpark?

Regards,

Phillip