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From Frank McQuillan <fmcquil...@pivotal.io>
Subject Re: [VOTE] MADlib v1.14-rc1
Date Fri, 27 Apr 2018 18:44:50 GMT
Hi Jingyi,

Thanks for posting the artifacts and sending out the vote.

My findings:

Installation and IC passed on postgres 9.6.7

Also I tested a cpl of the new features (personalized page rank and
mini-batch preprocessor)
and they worked OK for me with a small sample data set.

+1 (binding)

On Thu, Apr 26, 2018 at 2:57 PM, Jingyi Mei <jmei@pivotal.io> wrote:

> Hello Apache MADlib dev community,
>
> This is the vote for Apache MADlib 1.14 Release (RC1). It provides the
> source release tarball and convenience binaries. This is the third
> Apache MADlib release as an Apache Top Level Project (TLP).
>
> The vote will run for at least 72 working hours and will close on
> Tuesday, May 1st, 2018 @ 6pm PDT. A minimum of 3 binding +1 votes and
> more binding +1 than binding -1 are required to pass.
>
> The main goals of this release are:
>
> 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,
>    MADLIB-1220, MADLIB-1224, MADLIB-1226, MADLIB-1227)
>    - 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 missing 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)
>    - Fixed MLP failure on GPDB 4.3 when verbose=True (MADLIB-1209)
>    - Fixed RF issue that showed up when var_importance=True with no
>    continuous features (MADLIB-1219)
>    - Fixed DT/RF issue for null_as_category=True and grouping enabled
>    (MADLIB-1217)
>
> Other:
>
>    - Reduced install-check runtime for PCA, DT, RF, elastic net
>    (MADLIB-1216)
>    - Added CentOS 7 PostgreSQL 9.6/10 docker files
>
> For additional information, please see:
> https://cwiki.apache.org/confluence/display/MADLIB/MADlib+1.14
>
> Here are the release artifact details:
>
> Source release tag to be voted on: rc/1.14-rc1, located here:
> https://git-wip-us.apache.org/repos/asf?p=madlib.git;a=tag;
> h=refs/tags/rc/1.14-rc1
>
> Source release tarball can be retrieved from the following locations:
>
> Package:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-src.tar.gz
> PGP Signature:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-src.tar.gz.asc
> SHA512 Hash:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-src.tar.gz.sha512
>
> Convenience binary packages can be retrieved from the following
> locations:
>
> macOS: 10.* PostgreSQL 9.6 & 10.2
>
> Package:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Darwin.dmg
> PGP Signature:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Darwin.dmg.asc
> SHA512 Hash:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Darwin.dmg.sha512
>
> CentOS* GPDB 4.3.5+
>
> Package:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Linux-GPDB43.rpm
> PGP Signature:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Linux-GPDB43.rpm.asc
> SHA512 Hash:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Linux-GPDB43.rpm.sha512
>
> CentOS 6 &* GPDB 5.3.0, PostgreSQL 9.6 & 10.2
>
> Package:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Linux.rpm
> PGP Signature:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Linux.rpm.asc
> SHA512 Hash:
> https://dist.apache.org/repos/dist/dev/madlib/1.14-RC1/
> apache-madlib-1.14-bin-Linux.rpm.sha512
>
> The PGP KEYS file used to validate the signature of the release artifacts
> is available here:
> https://dist.apache.org/repos/dist/dev/madlib/KEYS
>
> To help in tallying the vote, PMC members please be sure to indicate
> “(binding)” with the vote.
>
> [ ] +1 approve
> [ ] +0 no opinion
> [ ] -1 disapprove (and reason why)
>
> Regards,
> Jingyi Mei
>
> Pivotal R&D Advanced Analytics
> ​
>

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