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From Jingyi Mei <j...@pivotal.io>
Subject [Announce] Apache MADlib v1.14 released
Date Thu, 03 May 2018 00:23:50 GMT
The Apache MADlib team is pleased to announce the immediate
availability of the 1.14 release.

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=3DTrue (MADLIB-1209)
   - Fixed RF issue that showed up when var_importance=3DTrue with no
   continuous features (MADLIB-1219)
   - Fixed DT/RF issue for null_as_category=3DTrue 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


All release changes can be found here:

  https://cwiki.apache.org/confluence/display/MADLIB/MADlib+1.14

You can download the source release and convenience binary packages
from Apache MADlib's download page here:

  http://madlib.apache.org/download.html

Alternatively, you can download through an ASF mirror near you:

  https://www.apache.org/dyn/closer.lua/madlib/1.14

----

Apache MADlib is an open-source library for scalable in-database
analytics. It provides data-parallel implementations of mathematical,
statistical and machine learning methods for structured and
unstructured data.

The MADlib mission: to foster widespread development of scalable
analytic skills, by harnessing efforts from commercial practice,
academic research, and open-source development.

We welcome your help and feedback. For more information on how to
report problems, and to get involved, visit the project website at
https://madlib.apache.org

----

Thank you, everyone who contributed to the MADlib 1.13 release. We
look forward to continued community participation for the next
release.

Regards,
Jingyi Mei

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