Looks cool im already starting to play with it.
On Friday, October 4, 2013, Makoto Yui <[EMAIL PROTECTED]> wrote:
> Hi Dean,
> Thank you for your interest in Hivemall.
> Twitter's paper actually influenced me in developing Hivemall and I
> initially implemented such functionality as Pig UDFs.
> Though my Pig ML library is not released, you can find a similar
> attempt for Pig in
> 2013/10/3 Dean Wampler <[EMAIL PROTECTED]>:
>> This is great news! I know that Twitter has done something similar with
>> for Pig, as described in this paper:
>> I'm glad to see the same thing start with Hive.
>> On Wed, Oct 2, 2013 at 10:21 AM, Makoto YUI <[EMAIL PROTECTED]> wrote:
>>> Hello all,
>>> My employer, AIST, has given the thumbs up to open source our machine
>>> learning library, named Hivemall.
>>> Hivemall is a scalable machine learning library running on Hive/Hadoop,
>>> licensed under the LGPL 2.1.
>>> Hivemall provides machine learning functionality as well as feature
>>> engineering functions through UDFs/UDAFs/UDTFs of Hive. It is designed
>>> to be scalable to the number of training instances as well as the number
>>> of training features.
>>> Hivemall is very easy to use as every machine learning step is done
>>> within HiveQL.
>>> -- Installation is just as follows:
>>> add jar /tmp/hivemall.jar;
>>> source /tmp/define-all.hive;
>>> -- Logistic regression is performed by a query.
>>> avg(weight) as weight
>>> (SELECT logress(features,label) as (feature,weight) FROM
>>> training_features) t
>>> GROUP BY feature;
>>> You can find detailed examples on our wiki pages.
>>> Though we consider that Hivemall is much easier to use and more scalable
>>> than Mahout for classification/regression tasks, please check it by
>>> yourself. If you have a Hive environment, you can evaluate Hivemall
>>> within 5 minutes or so.
>>> Hope you enjoy the release! Feedback (and pull request) is always
>>> Thank you,
>> Dean Wampler, Ph.D.