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Posted to dev@madlib.apache.org by iyerr3 <gi...@git.apache.org> on 2018/04/19 15:45:39 UTC

[GitHub] madlib pull request #265: Release: Add v1.14 release notes

GitHub user iyerr3 opened a pull request:

    https://github.com/apache/madlib/pull/265

    Release: Add v1.14 release notes

    

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/madlib/madlib rel/release_notes_1.14

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/madlib/pull/265.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #265
    
----
commit e21efee58401a3b47d9eae7e130ddd1c826656a3
Author: Rahul Iyer <ri...@...>
Date:   2018-04-19T15:44:29Z

    Release: Add v1.14 release notes

----


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[GitHub] madlib issue #265: Release: Add v1.14 release notes

Posted by jingyimei <gi...@git.apache.org>.
Github user jingyimei commented on the issue:

    https://github.com/apache/madlib/pull/265
  
    LGTM


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[GitHub] madlib issue #265: Release: Add v1.14 release notes

Posted by fmcquillan99 <gi...@git.apache.org>.
Github user fmcquillan99 commented on the issue:

    https://github.com/apache/madlib/pull/265
  
    there are 33 JIRAs in 1.14
    https://issues.apache.org/jira/projects/MADLIB/versions/12342305
    
    but only about 26 or 27 JIRAs in listed in these release notes.
    
    If that is explainable then +1.
    
    Otherwise check if there is anything left out and add it.
    
    Then you can merge, I don't need to review it again
    
    thanks
    



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[GitHub] madlib pull request #265: Release: Add v1.14 release notes

Posted by jingyimei <gi...@git.apache.org>.
Github user jingyimei commented on a diff in the pull request:

    https://github.com/apache/madlib/pull/265#discussion_r182844909
  
    --- Diff: RELEASE_NOTES ---
    @@ -9,6 +9,56 @@ commit history located at https://github.com/apache/madlib/commits/master.
     
     Current list of bugs and issues can be found at https://issues.apache.org/jira/browse/MADLIB.
     —-------------------------------------------------------------------------
    +MADlib v1.14:
    +
    +Release Date: 2018-April-28
    +
    +New features:
    +* New module - Balanced datasets: A sampling module to balance classification
    +    datasets by resampling using various techniques including undersampling,
    +    oversampling, uniform sampling or user-defined proportion sampling
    +    (MADLIB-1168)
    +* Mini-batch: Added a mini-batch optimizer for MLP and a preprocessor function
    +    necessary to create batches from the data (MADLIB-1200, MADLIB-1206)
    +* k-NN: Added weighted averaging/voting by distance (MADLIB-1181)
    +* Summary: Added additional stats: number of positive, negative, zero values and
    +    95% confidence intervals for the mean (MADLIB-1167)
    +* Encode categorical: Updated to produce lower-case column names when possible
    +    (MADLIB-1202)
    +* MLP: Added support for already one-hot encoded categorical dependent variable
    +    in a classification task (MADLIB-1222)
    +* Pagerank: Added option for personalized vertices that allows higher weightage
    +    for a subset of vertices which will have a higher jump probability as
    +    compared to other vertices and a random surfer is more likely to
    +    jump to these personalization vertices (MADLIB-1084)
    +
    +Bug fixes:
    +    - Fixed issue with invalid calls of construct_array that led to problems
    +    in Postgresql 10 (MADLIB-1185)
    +    - Added newline between file concatenation during PGXN install (MADLIB-1194)
    +    - Fixed upgrade issues in knn (MADLIB-1197)
    +    - Added fix to ensure RF variable importance are always non-negative
    +    - Fixed inconsistency in LDA output and improved usability
    +        (MADLIB-1160, MADLIB-1201)
    +    - Fixed MLP and RF predict for models trained in earlier versions to
    +        ensure misisng optional parameters are given appropriate default values
    +        (MADLIB-1207)
    +    - Fixed a scenario in DT where no features exist due categorical columns
    +        with single level being dropped led to the database crashing
    --- End diff --
    
    We should mention the other DT/RF bug fixes from MADLIB-1217 & 1219 & 1225.


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[GitHub] madlib issue #265: Release: Add v1.14 release notes

Posted by asfgit <gi...@git.apache.org>.
Github user asfgit commented on the issue:

    https://github.com/apache/madlib/pull/265
  
    
    Refer to this link for build results (access rights to CI server needed): 
    https://builds.apache.org/job/madlib-pr-build/457/



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[GitHub] madlib pull request #265: Release: Add v1.14 release notes

Posted by jingyimei <gi...@git.apache.org>.
Github user jingyimei commented on a diff in the pull request:

    https://github.com/apache/madlib/pull/265#discussion_r182843994
  
    --- Diff: RELEASE_NOTES ---
    @@ -9,6 +9,56 @@ commit history located at https://github.com/apache/madlib/commits/master.
     
     Current list of bugs and issues can be found at https://issues.apache.org/jira/browse/MADLIB.
     —-------------------------------------------------------------------------
    +MADlib v1.14:
    +
    +Release Date: 2018-April-28
    +
    +New features:
    +* New module - Balanced datasets: A sampling module to balance classification
    +    datasets by resampling using various techniques including undersampling,
    +    oversampling, uniform sampling or user-defined proportion sampling
    +    (MADLIB-1168)
    +* Mini-batch: Added a mini-batch optimizer for MLP and a preprocessor function
    +    necessary to create batches from the data (MADLIB-1200, MADLIB-1206)
    +* k-NN: Added weighted averaging/voting by distance (MADLIB-1181)
    +* Summary: Added additional stats: number of positive, negative, zero values and
    +    95% confidence intervals for the mean (MADLIB-1167)
    +* Encode categorical: Updated to produce lower-case column names when possible
    +    (MADLIB-1202)
    +* MLP: Added support for already one-hot encoded categorical dependent variable
    +    in a classification task (MADLIB-1222)
    +* Pagerank: Added option for personalized vertices that allows higher weightage
    +    for a subset of vertices which will have a higher jump probability as
    +    compared to other vertices and a random surfer is more likely to
    +    jump to these personalization vertices (MADLIB-1084)
    +
    +Bug fixes:
    +    - Fixed issue with invalid calls of construct_array that led to problems
    +    in Postgresql 10 (MADLIB-1185)
    +    - Added newline between file concatenation during PGXN install (MADLIB-1194)
    +    - Fixed upgrade issues in knn (MADLIB-1197)
    +    - Added fix to ensure RF variable importance are always non-negative
    +    - Fixed inconsistency in LDA output and improved usability
    +        (MADLIB-1160, MADLIB-1201)
    +    - Fixed MLP and RF predict for models trained in earlier versions to
    +        ensure misisng optional parameters are given appropriate default values
    +        (MADLIB-1207)
    +    - Fixed a scenario in DT where no features exist due categorical columns
    +        with single level being dropped led to the database crashing
    +    - Fixed step size initialization in MLP based on learning rate policy
    +        (MADLIB-1212)
    +    - Fixed PCA issue that leads to failure when grouping column is a TEXT type
    +        (MADLIB-1215)
    +    - Fixed cat levels output in DT when grouping is enabled (MADLIB-1218)
    +    - Fixed and simplified initialization of model coefficients in MLP
    +    - Removed source table dependency for predicting regression models in MLP
    +        (MADLIB-1223)
    +    - Print loss of first iteration in MLP (MADLIB-1228)
    +
    --- End diff --
    
    We should mention MADLIB-1209 Neural net related bug fix.


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[GitHub] madlib pull request #265: Release: Add v1.14 release notes

Posted by jingyimei <gi...@git.apache.org>.
Github user jingyimei commented on a diff in the pull request:

    https://github.com/apache/madlib/pull/265#discussion_r182845348
  
    --- Diff: RELEASE_NOTES ---
    @@ -9,6 +9,56 @@ commit history located at https://github.com/apache/madlib/commits/master.
     
     Current list of bugs and issues can be found at https://issues.apache.org/jira/browse/MADLIB.
     —-------------------------------------------------------------------------
    +MADlib v1.14:
    +
    +Release Date: 2018-April-28
    +
    +New features:
    +* New module - Balanced datasets: A sampling module to balance classification
    +    datasets by resampling using various techniques including undersampling,
    +    oversampling, uniform sampling or user-defined proportion sampling
    +    (MADLIB-1168)
    +* Mini-batch: Added a mini-batch optimizer for MLP and a preprocessor function
    --- End diff --
    
    Other JIRAs related to this: MADLIB-1220, MADLIB-1224, MADLIB-1226, MADLIB-1227


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[GitHub] madlib issue #265: Release: Add v1.14 release notes

Posted by iyerr3 <gi...@git.apache.org>.
Github user iyerr3 commented on the issue:

    https://github.com/apache/madlib/pull/265
  
    Thanks @jingyimei and @orhankislal. I'll go ahead and merge this. 


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[GitHub] madlib issue #265: Release: Add v1.14 release notes

Posted by asfgit <gi...@git.apache.org>.
Github user asfgit commented on the issue:

    https://github.com/apache/madlib/pull/265
  
    
    Refer to this link for build results (access rights to CI server needed): 
    https://builds.apache.org/job/madlib-pr-build/459/



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[GitHub] madlib pull request #265: Release: Add v1.14 release notes

Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:

    https://github.com/apache/madlib/pull/265


---

[GitHub] madlib issue #265: Release: Add v1.14 release notes

Posted by jingyimei <gi...@git.apache.org>.
Github user jingyimei commented on the issue:

    https://github.com/apache/madlib/pull/265
  
    @fmcquillan99 w/ @orhankislal  We did go through JIRAs one by one. Those not mentioned are either no-op or duplicates.


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