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[jira] [Commented] (MAHOUT-1834) Setup Travis CI for Mahout

[Hudson (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1834?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15248786#comment-15248786
 ] 

Hudson commented on MAHOUT-1834:


SUCCESS: Integrated in Mahout-Quality # (See 
[https://builds.apache.org/job/Mahout-Quality//])
MAHOUT-1834: Travis CI for MAhout, this closes apache/mahout#222 (smarthi: rev 
f26698dc7d8ce928229e39d152da35ac25e226e3)
* README.md
* .travis.yml


> Setup Travis CI for Mahout
> --
>
> Key: MAHOUT-1834
> URL: https://issues.apache.org/jira/browse/MAHOUT-1834
> Project: Mahout
>  Issue Type: Task
>Affects Versions: 0.12.0
>Reporter: Andrew Palumbo
>Assignee: Suneel Marthi
> Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an 
> account with Travis and configuring a {{travis.yml}} file.



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[jira] [Resolved] (MAHOUT-1834) Setup Travis CI for Mahout

[Suneel Marthi (JIRA)]

 [ 
https://issues.apache.org/jira/browse/MAHOUT-1834?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Suneel Marthi resolved MAHOUT-1834.
---
Resolution: Implemented

> Setup Travis CI for Mahout
> --
>
> Key: MAHOUT-1834
> URL: https://issues.apache.org/jira/browse/MAHOUT-1834
> Project: Mahout
>  Issue Type: Task
>Affects Versions: 0.12.0
>Reporter: Andrew Palumbo
>Assignee: Suneel Marthi
> Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an 
> account with Travis and configuring a {{travis.yml}} file.



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Changes:

[smarthi] NoJira: Add correct mvn version to travis.yml

[smarthi] NoJira: Add correct mvn version to travis.yml

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[jira] [Commented] (MAHOUT-1834) Setup Travis CI for Mahout

[ASF GitHub Bot (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1834?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15248526#comment-15248526
 ] 

ASF GitHub Bot commented on MAHOUT-1834:


Github user asfgit closed the pull request at:

https://github.com/apache/mahout/pull/222


> Setup Travis CI for Mahout
> --
>
> Key: MAHOUT-1834
> URL: https://issues.apache.org/jira/browse/MAHOUT-1834
> Project: Mahout
>  Issue Type: Task
>Affects Versions: 0.12.0
>Reporter: Andrew Palumbo
>Assignee: Suneel Marthi
> Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an 
> account with Travis and configuring a {{travis.yml}} file.



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[jira] [Commented] (MAHOUT-1834) Setup Travis CI for Mahout

[ASF GitHub Bot (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1834?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15248510#comment-15248510
 ] 

ASF GitHub Bot commented on MAHOUT-1834:


GitHub user smarthi opened a pull request:

https://github.com/apache/mahout/pull/222

MAHOUT-1834: SetupTravis CI for Mahout



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

$ git pull https://github.com/smarthi/mahout MAHOUT-1834

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

https://github.com/apache/mahout/pull/222.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 #222


commit 461955b0114342a8e350da6b2765173b12a28f05
Author: smarthi 
Date:   2016-04-19T20:02:54Z

MAHOUT-1834: SetupTravis CI for Mahout




> Setup Travis CI for Mahout
> --
>
> Key: MAHOUT-1834
> URL: https://issues.apache.org/jira/browse/MAHOUT-1834
> Project: Mahout
>  Issue Type: Task
>Affects Versions: 0.12.0
>Reporter: Andrew Palumbo
>Assignee: Suneel Marthi
> Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an 
> account with Travis and configuring a {{travis.yml}} file.



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[jira] [Commented] (MAHOUT-1834) Setup Travis CI for Mahout

[Andrew Palumbo (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1834?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15248471#comment-15248471
 ] 

Andrew Palumbo commented on MAHOUT-1834:


filed https://issues.apache.org/jira/browse/INFRA-11708

> Setup Travis CI for Mahout
> --
>
> Key: MAHOUT-1834
> URL: https://issues.apache.org/jira/browse/MAHOUT-1834
> Project: Mahout
>  Issue Type: Task
>Affects Versions: 0.12.0
>Reporter: Andrew Palumbo
>Assignee: Suneel Marthi
> Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an 
> account with Travis and configuring a {{travis.yml}} file.



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[jira] [Commented] (MAHOUT-1834) Setup Travis CI for Mahout

[Andrew Palumbo (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1834?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15248442#comment-15248442
 ] 

Andrew Palumbo commented on MAHOUT-1834:


we may have to file with infra to get the webhooks setup:  
https://issues.apache.org/jira/browse/INFRA-11265.  Probably don't want to do 
that until we have the {{.travis.yml}} file setup.

> Setup Travis CI for Mahout
> --
>
> Key: MAHOUT-1834
> URL: https://issues.apache.org/jira/browse/MAHOUT-1834
> Project: Mahout
>  Issue Type: Task
>Affects Versions: 0.12.0
>Reporter: Andrew Palumbo
>Assignee: Suneel Marthi
> Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an 
> account with Travis and configuring a {{travis.yml}} file.



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RE: [jira] [Created] (MAHOUT-1834) Setup Travis CI for Mahout

[Andrew Palumbo]

I thought I remembered something like that.. maybe going through ASF will 
help.. the mirrors have an actual purchased Travis CI account.

 Original message 
From: Andrew Musselman 
Date: 04/19/2016 2:50 PM (GMT-05:00)
To: dev@mahout.apache.org
Subject: Re: [jira] [Created] (MAHOUT-1834) Setup Travis CI for Mahout

Someone on my team tried that once but the builds were blowing up with OOM
as I recall.

On Tuesday, April 19, 2016, Andrew Palumbo (JIRA)  wrote:

> Andrew Palumbo created MAHOUT-1834:
> --
>
>  Summary: Setup Travis CI for Mahout
>  Key: MAHOUT-1834
>  URL: https://issues.apache.org/jira/browse/MAHOUT-1834
>  Project: Mahout
>   Issue Type: Task
> Affects Versions: 0.12.0
> Reporter: Andrew Palumbo
>  Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an
> account with Travis and configuring a {{travis.yml}} file.
>
>
>
> --
> This message was sent by Atlassian JIRA
> (v6.3.4#6332)
>

[jira] [Assigned] (MAHOUT-1834) Setup Travis CI for Mahout

[Suneel Marthi (JIRA)]

 [ 
https://issues.apache.org/jira/browse/MAHOUT-1834?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Suneel Marthi reassigned MAHOUT-1834:
-

Assignee: Suneel Marthi

> Setup Travis CI for Mahout
> --
>
> Key: MAHOUT-1834
> URL: https://issues.apache.org/jira/browse/MAHOUT-1834
> Project: Mahout
>  Issue Type: Task
>Affects Versions: 0.12.0
>Reporter: Andrew Palumbo
>Assignee: Suneel Marthi
> Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an 
> account with Travis and configuring a {{travis.yml}} file.



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Re: [jira] [Created] (MAHOUT-1834) Setup Travis CI for Mahout

[Andrew Musselman]

Someone on my team tried that once but the builds were blowing up with OOM
as I recall.

On Tuesday, April 19, 2016, Andrew Palumbo (JIRA)  wrote:

> Andrew Palumbo created MAHOUT-1834:
> --
>
>  Summary: Setup Travis CI for Mahout
>  Key: MAHOUT-1834
>  URL: https://issues.apache.org/jira/browse/MAHOUT-1834
>  Project: Mahout
>   Issue Type: Task
> Affects Versions: 0.12.0
> Reporter: Andrew Palumbo
>  Fix For: 0.12.1
>
>
> We need to get Travis CI setup for mahout.  This involves setting up an
> account with Travis and configuring a {{travis.yml}} file.
>
>
>
> --
> This message was sent by Atlassian JIRA
> (v6.3.4#6332)
>

[jira] [Created] (MAHOUT-1834) Setup Travis CI for Mahout

[Andrew Palumbo (JIRA)]

Andrew Palumbo created MAHOUT-1834:
--

 Summary: Setup Travis CI for Mahout
 Key: MAHOUT-1834
 URL: https://issues.apache.org/jira/browse/MAHOUT-1834
 Project: Mahout
  Issue Type: Task
Affects Versions: 0.12.0
Reporter: Andrew Palumbo
 Fix For: 0.12.1


We need to get Travis CI setup for mahout.  This involves setting up an account 
with Travis and configuring a {{travis.yml}} file.



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Re: [jira] [Commented] (MAHOUT-1788) spark-itemsimilarity integration test script cleanup

[Khurrum Nasim]

okay thanks - i’ll run those tests. i actually ran a few others as well like 
the MatrixWritableTest.  

> On Apr 18, 2016, at 8:22 PM, Dmitriy Lyubimov  wrote:
> 
> I am not sure of your question about tests...
> 
> there are in-memory tests which you can by 'mvn test' in /math-scala
> module; distributed tests are done per engine under 'spark', 'h2o' or
> 'flink' modules.
> 
> 
> On Mon, Apr 18, 2016 at 5:19 PM, Dmitriy Lyubimov  wrote:
> 
>> i meant "not so much a library"
>> 
>> On Mon, Apr 18, 2016 at 5:18 PM, Dmitriy Lyubimov 
>> wrote:
>> 
>>> Khurrum,
>>> 
>>> mahout is so much  a library at this point.
>>> 
>>> if you mean if it can be used to build networks with 2d inputs, yes i did
>>> some of that. multi-epoch SGD based systems should be easy enough to build,
>>> and will probably have a reasonable performance -- although I think
>>> dedicated CNN systems like Caffe would still run faster at this point. Full
>>> batch trainers are somewhat slow for larger problems though, my
>>> investigation points that  there are architectural problems in spark that
>>> are hard to overcome at this point for high IO algorithms.
>>> 
>>> On Mon, Apr 18, 2016 at 11:49 AM, Khurrum Nasim >> 
 Hi Guys,
 
 Can Mahout be used for things like face detection ?Also which unit
 tests or integration tests do you recommend I should run just to get a
 better feel of the execution flow.
 
 I’m still slowly acclimating to the project.  But hopefully should come
 up to speed soon.
 
 
 Many Thanks,
 
 Khurrum
 
 
 
 
> On Mar 30, 2016, at 3:10 PM, Suneel Marthi  wrote:
> 
> Thanks Khurrum for stepping up.
> 
> You just need basic programming skills - Java/Scala to be able to
> contribute. We can help you with the algorithms and linear algebra
 stuff.
> 
> 
> Welcome aboard !!
> 
> 
> On Wed, Mar 30, 2016 at 3:05 PM, Khurrum Nasim <
 khurrum.na...@useitc.com>
> wrote:
> 
>> Thanks for the advice Dimitry.  I’m already signed up on ASF jira.
 My
>> handle is “nasimk”
>> 
>> Do I need to be a linear algebra expert and or math phd  to
 contribute ?
>> I have 10 plus years of computer programming experience.  my
 background is
>> comp sci.
>> 
>> Khurrum
>> 
>> 
>> 
>> 
>> 
>>> On Mar 30, 2016, at 2:57 PM, Dmitriy Lyubimov 
 wrote:
>>> 
>>> PS You may also want to sign up with ASF Jira so we can assign
 issues to
>>> yourself.
>>> 
>>> On Wed, Mar 30, 2016 at 11:52 AM, Dmitriy Lyubimov <
 dlie...@gmail.com>
>>> wrote:
>>> 
 
 
 On Wed, Mar 30, 2016 at 11:43 AM, Khurrum Nasim <
>> khurrum.na...@useitc.com>
 wrote:
 
> Thanks Dimirtry.
> 
> I take a look at see where I can start pitching in.  Do I need
> contributor access ? how  would I create feature branch of my work
 ?
> 
 
 Khurrum,
 
 you only need github account. What you need is to create mahout's
 master
 fork in your github space and keep it in sync, as possible, with
 master
>> as
 you go (by doing regular pulls). That way you have the most chance
 of
 having least conflicts possible.
 
 At any point in time (I recommend at perhaps when you feel you are
 about
 50 to 70% done or just need a code advice), you can create a github
 pull
 request to the apache/mahout master. Make sure to include MAHOUT-XXX
>> issue
 in the head of the pull request, that way ASF will automatically
>> propagate
 code comments to jira, and so all discussion can be done entirely on
>> github.
 
 Again, if you take on a signficant contribution (such as a new
 numerical
 method contribution), I recommend to discuss the proposal on the
 @dev
>> list
 
 thanks.
 
 
> 
> Khurrum
> 
>> On Mar 30, 2016, at 1:12 PM, Dmitriy Lyubimov 
> wrote:
>> 
>> Oh but of course! please do!
>> 
>> You may work on any issue, this or any other of your choice, or
 even
>> on
> any
>> new issue you can think of (for sizeable contributions it is
> recommended to
>> start discussion on the @dev list first though, to make sure to
>> benefit
>> from experience of others. Please file any new issue first to
 jira).
>> 
>> On Wed, Mar 30, 2016 at 9:05 AM, shashi bushan dongur (JIRA) <
>> j...@apache.org> wrote:
>> 
>>> 
>>> [
>>> 
> 
>> 
 

[jira] [Commented] (MAHOUT-1572) blockify() to detect (naively) the data sparsity in the loaded data

[ASF GitHub Bot (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1572?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15247992#comment-15247992
 ] 

ASF GitHub Bot commented on MAHOUT-1572:


Github user dlyubimov commented on the pull request:

https://github.com/apache/mahout/pull/10#issuecomment-211983401
  
yes this is not the way to do it 
actually it seems vectors are not reporting density faithfully, i had to do 
a more thorough analysis of data density elsewhere before doing things. this 
probably needs to be patched with a more thorough technique. 
+1


> blockify() to detect (naively) the data sparsity in the loaded data 
> 
>
> Key: MAHOUT-1572
> URL: https://issues.apache.org/jira/browse/MAHOUT-1572
> Project: Mahout
>  Issue Type: Bug
>Reporter: Dmitriy Lyubimov
> Fix For: 0.10.0
>
>
> per [~ssc]:
> .bq a dense matrix is converted into a SparseRowMatrix with dense row vectors 
> by blockify(), after serialization this becomes a dense matrix in sparse 
> format (triggering OOMs)! 
> i guess we can look at first row vector and go on to either DenseMatrix or 
> SparseRowMatrix



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[jira] [Commented] (MAHOUT-1686) Create a documentattion page for ALS

[Saikat Kanjilal (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1686?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15247974#comment-15247974
 ] 

Saikat Kanjilal commented on MAHOUT-1686:
-

Andrew,
This issue seems old, does this still need to be done, if so I can take it on, 
let me know.
Thanks

> Create a documentattion page for ALS
> 
>
> Key: MAHOUT-1686
> URL: https://issues.apache.org/jira/browse/MAHOUT-1686
> Project: Mahout
>  Issue Type: Documentation
>Affects Versions: 0.11.0
>Reporter: Andrew Palumbo
>Assignee: Andrew Musselman
> Fix For: 0.12.1
>
>
> Following the template of the SSVD and QR pages create a page for ALS. This 
> Page would go under Algorithms-> Distributed Matrix Decomposition.



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[jira] [Commented] (MAHOUT-1791) Automatic threading for java based mmul in the front end and the backend.

[Saikat Kanjilal (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1791?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15247966#comment-15247966
 ] 

Saikat Kanjilal commented on MAHOUT-1791:
-

Andrew/Dmitry,
I am wondering if there is any coding related efforts here that I can help 
investigate or are there enough engineers already working on this?

> Automatic threading for java based mmul in the front end and the backend.
> -
>
> Key: MAHOUT-1791
> URL: https://issues.apache.org/jira/browse/MAHOUT-1791
> Project: Mahout
>  Issue Type: Improvement
>Affects Versions: 0.11.1, 0.12.0, 0.11.2
>Reporter: Dmitriy Lyubimov
>Assignee: Andrew Musselman
> Fix For: 0.12.1
>
>
> As we know, we are still struggling with decisions which path to take for 
> bare metal accelerations in in-core math. 
> Meanwhile, a simple no-brainer improvement though is to add decision paths 
> and apply multithreaded matrix-matrix multiplication (and maybe even others; 
> but mmul perhaps is the most prominent beneficiary here at the moment which 
> is both easy to do and to have a statistically significant improvement) 
> So multithreaded logic addition to mmul is one path. 
> Another path is automatic adjustment of multithreading. 
> In front end, we probably want to utilize all cores available. 
> in the backend, we can oversubscribe cores but probably doing so by more than 
> 2x or 3x is unadvisable because of point of diminishing returns driven by 
> growing likelihood of context switching overhead.



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Re: [jira] [Commented] (MAHOUT-1788) spark-itemsimilarity integration test script cleanup

[Suneel Marthi]

On Tue, Apr 19, 2016 at 11:08 AM, Khurrum Nasim 
wrote:

> Thank you Dimitry.
>
> So is there an architectural blueprint for mahout ?   What I mean is how
> can get the 1000 feet overview ? Or the bird eye view of the project.
> I do see Mahout is very modularized - however I’m still trying to make
> heads and tails out it :)
>
> @Dimitry -
> "my investigation points that  there are architectural problems in spark
> that
> are hard to overcome at this point for high IO algorithms.”  - Can you
> share some more details about this - I’m just curious.
>

Long story short - "Distributed != Scalable"

>
>
> > On Apr 18, 2016, at 8:18 PM, Dmitriy Lyubimov  wrote:
> >
> > Khurrum,
> >
> > mahout is so much  a library at this point.
> >
> > if you mean if it can be used to build networks with 2d inputs, yes i did
> > some of that. multi-epoch SGD based systems should be easy enough to
> build,
> > and will probably have a reasonable performance -- although I think
> > dedicated CNN systems like Caffe would still run faster at this point.
> Full
> > batch trainers are somewhat slow for larger problems though, my
> > investigation points that  there are architectural problems in spark that
> > are hard to overcome at this point for high IO algorithms.
> >
> > On Mon, Apr 18, 2016 at 11:49 AM, Khurrum Nasim <
> khurrum.na...@useitc.com>
> > wrote:
> >
> >> Hi Guys,
> >>
> >> Can Mahout be used for things like face detection ?Also which unit
> >> tests or integration tests do you recommend I should run just to get a
> >> better feel of the execution flow.
> >>
> >> I’m still slowly acclimating to the project.  But hopefully should come
> up
> >> to speed soon.
> >>
> >>
> >> Many Thanks,
> >>
> >> Khurrum
> >>
> >>
> >>
> >>
> >>> On Mar 30, 2016, at 3:10 PM, Suneel Marthi  wrote:
> >>>
> >>> Thanks Khurrum for stepping up.
> >>>
> >>> You just need basic programming skills - Java/Scala to be able to
> >>> contribute. We can help you with the algorithms and linear algebra
> stuff.
> >>>
> >>>
> >>> Welcome aboard !!
> >>>
> >>>
> >>> On Wed, Mar 30, 2016 at 3:05 PM, Khurrum Nasim <
> khurrum.na...@useitc.com
> >>>
> >>> wrote:
> >>>
>  Thanks for the advice Dimitry.  I’m already signed up on ASF jira.
> My
>  handle is “nasimk”
> 
>  Do I need to be a linear algebra expert and or math phd  to
> contribute ?
>  I have 10 plus years of computer programming experience.  my
> background
> >> is
>  comp sci.
> 
>  Khurrum
> 
> 
> 
> 
> 
> > On Mar 30, 2016, at 2:57 PM, Dmitriy Lyubimov 
> >> wrote:
> >
> > PS You may also want to sign up with ASF Jira so we can assign issues
> >> to
> > yourself.
> >
> > On Wed, Mar 30, 2016 at 11:52 AM, Dmitriy Lyubimov <
> dlie...@gmail.com>
> > wrote:
> >
> >>
> >>
> >> On Wed, Mar 30, 2016 at 11:43 AM, Khurrum Nasim <
>  khurrum.na...@useitc.com>
> >> wrote:
> >>
> >>> Thanks Dimirtry.
> >>>
> >>> I take a look at see where I can start pitching in.  Do I need
> >>> contributor access ? how  would I create feature branch of my work
> ?
> >>>
> >>
> >> Khurrum,
> >>
> >> you only need github account. What you need is to create mahout's
> >> master
> >> fork in your github space and keep it in sync, as possible, with
> >> master
>  as
> >> you go (by doing regular pulls). That way you have the most chance
> of
> >> having least conflicts possible.
> >>
> >> At any point in time (I recommend at perhaps when you feel you are
> >> about
> >> 50 to 70% done or just need a code advice), you can create a github
> >> pull
> >> request to the apache/mahout master. Make sure to include MAHOUT-XXX
>  issue
> >> in the head of the pull request, that way ASF will automatically
>  propagate
> >> code comments to jira, and so all discussion can be done entirely on
>  github.
> >>
> >> Again, if you take on a signficant contribution (such as a new
> >> numerical
> >> method contribution), I recommend to discuss the proposal on the
> @dev
>  list
> >>
> >> thanks.
> >>
> >>
> >>>
> >>> Khurrum
> >>>
>  On Mar 30, 2016, at 1:12 PM, Dmitriy Lyubimov 
> >>> wrote:
> 
>  Oh but of course! please do!
> 
>  You may work on any issue, this or any other of your choice, or
> even
>  on
> >>> any
>  new issue you can think of (for sizeable contributions it is
> >>> recommended to
>  start discussion on the @dev list first though, to make sure to
>  benefit
>  from experience of others. Please file any new issue first to
> jira).
> 
>  On Wed, Mar 30, 2016 at 9:05 AM, shashi bushan dongur (JIRA) <
>  j...@apache.org> wrote:
> 
> >
> > [
> 

[jira] [Commented] (MAHOUT-1833) One more svec function accepting cardinality as parameter

[Suneel Marthi (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1833?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15247585#comment-15247585
 ] 

Suneel Marthi commented on MAHOUT-1833:
---

Thanks Edmond, please create a PR. Its easier to comment and review a PR.

> One more svec function accepting cardinality as parameter 
> --
>
> Key: MAHOUT-1833
> URL: https://issues.apache.org/jira/browse/MAHOUT-1833
> Project: Mahout
>  Issue Type: Improvement
>Affects Versions: 0.12.0
> Environment: Mahout Spark Shell 0.12.0,
> Spark 1.6.0 Cluster on Hadoop Yarn 2.7.1, 
> Centos7 64bit
>Reporter: Edmond Luo
>
> It will be nice to add one more wrapper function like below to 
> org.apache.mahout.math.scalabindings
> {code}
> /**
>  * create a sparse vector out of list of tuple2's with specific 
> cardinality(size),
>  * throws IllegalArgumentException if cardinality is not bigger than required 
> cardinality of sdata
>  * @param cardinality sdata
>  * @return
>  */
> def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
>   val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
>   if (cardinality < required) {
> throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
> bigger than required[%required]!")
>   }
>   val initialCapacity = sdata.size
>   val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
>   sdata.foreach(t ⇒ sv.setQuick(t._1, 
> t._2.asInstanceOf[Number].doubleValue()))
>   sv
> }
> {code}
> So user can specify the cardinality for the created sparse vector.
> This is very useful and convenient if user wants to create a DRM with many 
> sparse vectors and the vectors are not with the same actual size(but with the 
> same logical size, e.g. rows of a sparse matrix).
> Below code should demonstrate the case:
> {code}
> var cardinality = 20
> val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line => 
> (line(0).toInt, Array((line(1).toInt,1.reduceByKey((v1, v2) => v1 ++ 
> v2).map(row => (row._1, svec(cardinality, row._2)))
> val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))
> // All element wise opperation will fail for those DRM with not 
> cardinality-consistent SparseVector
> val drm2 = drm + drm
> val drm3 = drm - drm
> val drm4 = drm * drm
> val drm5 = drm / drm
> {code}
> Notice that in the last map, the svec acceptted one more cardinality 
> parameter, so the cardinality of those created SparseVector can be consistent.



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RE: [jira] [Updated] (MAHOUT-1833) One more svec function accepting cardinality as parameter

[Andrew Palumbo]

Hi Edmond.  Could you open a pull request so that we can review this?

Thank you,

Andy


 Original message 
From: "Edmond Luo (JIRA)" 
Date: 04/19/2016 4:29 AM (GMT-05:00)
To: dev@mahout.apache.org
Subject: [jira] [Updated] (MAHOUT-1833) One more svec function accepting 
cardinality as parameter


 [ 
https://issues.apache.org/jira/browse/MAHOUT-1833?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Edmond Luo updated MAHOUT-1833:
---
Description:
It will be nice to add one more wrapper function like below to 
org.apache.mahout.math.scalabindings

{code}
/**
 * create a sparse vector out of list of tuple2's with specific 
cardinality(size),
 * throws IllegalArgumentException if cardinality is not bigger than required 
cardinality of sdata
 * @param cardinality sdata
 * @return
 */
def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
  val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
  if (cardinality < required) {
throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
bigger than required[%required]!")
  }

  val initialCapacity = sdata.size
  val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
  sdata.foreach(t ⇒ sv.setQuick(t._1, t._2.asInstanceOf[Number].doubleValue()))
  sv
}
{code}

So user can specify the cardinality for the created sparse vector.

This is very useful and convenient if user wants to create a DRM with many 
sparse vectors and the vectors are not with the same actual size(but with the 
same logical size, e.g. rows of a sparse matrix).

Below code should demonstrate the case:
{code}
var cardinality = 20
val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line => 
(line(0).toInt, Array((line(1).toInt,1.reduceByKey((v1, v2) => v1 ++ 
v2).map(row => (row._1, svec(cardinality, row._2)))

val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))

// All element wise opperation will fail for those DRM with not 
cardinality-consistent SparseVector
val drm2 = drm + drm
val drm3 = drm - drm
val drm4 = drm * drm
val drm5 = drm / drm
{code}

Notice that in the last map, the svec acceptted one more cardinality parameter, 
so the cardinality of those created SparseVector can be consistent.


  was:
It will be nice to add one more wrapper function like below to 
org.apache.mahout.math.scalabindings

{code}
/**
 * create a sparse vector out of list of tuple2's with specific 
cardinality(size),
 * throws IllegalArgumentException if cardinality is not bigger than required 
cardinality of sdata
 * @param cardinality sdata
 * @return
 */
def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
  val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
  if (cardinality < required) {
throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
bigger than required[%required]!")
  }

  val initialCapacity = sdata.size
  val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
  sdata.foreach(t ⇒ sv.setQuick(t._1, t._2.asInstanceOf[Number].doubleValue()))
  sv
}
{code}

So user can specify the cardinality for the created sparse vector.

This is very useful and convenient if user wants to create a DRM with many 
sparse vectors and the vectors are not with the same actual size(but with the 
same logical size, e.g. rows of a sparse matrix).

Below code should demonstrate the case:
{code}
var cardinality = 20
val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line => 
(line(0).toInt, Array((line(1).toInt,1.reduceByKey((v1, v2) => v1 ++ 
v2).map(row => (row._1, svec(cardinality, row._2)))

val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))

// All element wise opperation will fail for those DRM with not 
cardinality-consistent SparseVector
val drm2 = drm + drm
val drm3 = drm - drm
val drm4 = drm * drm
val drm5 = drm / drm
{code}

Notice that in the last map, the svec in above accepts one more parameter, so 
the cardinality of those created SparseVector can be consistent.



> One more svec function accepting cardinality as parameter
> --
>
> Key: MAHOUT-1833
> URL: https://issues.apache.org/jira/browse/MAHOUT-1833
> Project: Mahout
>  Issue Type: Improvement
>Affects Versions: 0.12.0
> Environment: Mahout Spark Shell 0.12.0,
> Spark 1.6.0 Cluster on Hadoop Yarn 2.7.1,
> Centos7 64bit
>Reporter: Edmond Luo
>
> It will be nice to add one more wrapper function like below to 
> org.apache.mahout.math.scalabindings
> {code}
> /**
>  * create a sparse vector out of list of tuple2's with specific 
> cardinality(size),
>  * throws IllegalArgumentException if cardinality is not bigger than required 
> cardinality of sdata
>  * @param cardinality sdata
>  * @return
>  */
> def svec(cardinality: Int, sdata: 

[jira] [Updated] (MAHOUT-1833) One more svec function accepting cardinality as parameter

[Edmond Luo (JIRA)]

 [ 
https://issues.apache.org/jira/browse/MAHOUT-1833?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Edmond Luo updated MAHOUT-1833:
---
Description: 
It will be nice to add one more wrapper function like below to 
org.apache.mahout.math.scalabindings

{code}
/**
 * create a sparse vector out of list of tuple2's with specific 
cardinality(size),
 * throws IllegalArgumentException if cardinality is not bigger than required 
cardinality of sdata
 * @param cardinality sdata
 * @return
 */
def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
  val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
  if (cardinality < required) {
throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
bigger than required[%required]!")
  }

  val initialCapacity = sdata.size
  val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
  sdata.foreach(t ⇒ sv.setQuick(t._1, t._2.asInstanceOf[Number].doubleValue()))
  sv
}
{code}

So user can specify the cardinality for the created sparse vector.

This is very useful and convenient if user wants to create a DRM with many 
sparse vectors and the vectors are not with the same actual size(but with the 
same logical size, e.g. rows of a sparse matrix).

Below code should demonstrate the case:
{code}
var cardinality = 20
val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line => 
(line(0).toInt, Array((line(1).toInt,1.reduceByKey((v1, v2) => v1 ++ 
v2).map(row => (row._1, svec(cardinality, row._2)))

val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))

// All element wise opperation will fail for those DRM with not 
cardinality-consistent SparseVector
val drm2 = drm + drm
val drm3 = drm - drm
val drm4 = drm * drm
val drm5 = drm / drm
{code}

Notice that in the last map, the svec acceptted one more cardinality parameter, 
so the cardinality of those created SparseVector can be consistent.


  was:
It will be nice to add one more wrapper function like below to 
org.apache.mahout.math.scalabindings

{code}
/**
 * create a sparse vector out of list of tuple2's with specific 
cardinality(size),
 * throws IllegalArgumentException if cardinality is not bigger than required 
cardinality of sdata
 * @param cardinality sdata
 * @return
 */
def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
  val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
  if (cardinality < required) {
throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
bigger than required[%required]!")
  }

  val initialCapacity = sdata.size
  val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
  sdata.foreach(t ⇒ sv.setQuick(t._1, t._2.asInstanceOf[Number].doubleValue()))
  sv
}
{code}

So user can specify the cardinality for the created sparse vector.

This is very useful and convenient if user wants to create a DRM with many 
sparse vectors and the vectors are not with the same actual size(but with the 
same logical size, e.g. rows of a sparse matrix).

Below code should demonstrate the case:
{code}
var cardinality = 20
val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line => 
(line(0).toInt, Array((line(1).toInt,1.reduceByKey((v1, v2) => v1 ++ 
v2).map(row => (row._1, svec(cardinality, row._2)))

val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))

// All element wise opperation will fail for those DRM with not 
cardinality-consistent SparseVector
val drm2 = drm + drm
val drm3 = drm - drm
val drm4 = drm * drm
val drm5 = drm / drm
{code}

Notice that in the last map, the svec in above accepts one more parameter, so 
the cardinality of those created SparseVector can be consistent.



> One more svec function accepting cardinality as parameter 
> --
>
> Key: MAHOUT-1833
> URL: https://issues.apache.org/jira/browse/MAHOUT-1833
> Project: Mahout
>  Issue Type: Improvement
>Affects Versions: 0.12.0
> Environment: Mahout Spark Shell 0.12.0,
> Spark 1.6.0 Cluster on Hadoop Yarn 2.7.1, 
> Centos7 64bit
>Reporter: Edmond Luo
>
> It will be nice to add one more wrapper function like below to 
> org.apache.mahout.math.scalabindings
> {code}
> /**
>  * create a sparse vector out of list of tuple2's with specific 
> cardinality(size),
>  * throws IllegalArgumentException if cardinality is not bigger than required 
> cardinality of sdata
>  * @param cardinality sdata
>  * @return
>  */
> def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
>   val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
>   if (cardinality < required) {
> throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
> bigger than required[%required]!")
>   }
>   val initialCapacity = sdata.size
>   val sv = new 

[jira] [Comment Edited] (MAHOUT-1833) One more svec function accepting cardinality as parameter

[Edmond Luo (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1833?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15247368#comment-15247368
 ] 

Edmond Luo edited comment on MAHOUT-1833 at 4/19/16 8:27 AM:
-

I have implemented the new wrapper function as shown above and added some 
testing code for the new function, 
However, I am not sure if I should add some DRM test cases using sparse vector, 
seems now we do not have any test case for those DRM built from sparse vector.


was (Author: resec):
I have implemented all above code and added some testing code for the new 
function, 
However, I am not sure if I should add some DRM test cases using sparse vector, 
seems now we do not have any test case for those DRM built from sparse vector.

> One more svec function accepting cardinality as parameter 
> --
>
> Key: MAHOUT-1833
> URL: https://issues.apache.org/jira/browse/MAHOUT-1833
> Project: Mahout
>  Issue Type: Improvement
>Affects Versions: 0.12.0
> Environment: Mahout Spark Shell 0.12.0,
> Spark 1.6.0 Cluster on Hadoop Yarn 2.7.1, 
> Centos7 64bit
>Reporter: Edmond Luo
>
> It will be nice to add one more wrapper function like below to 
> org.apache.mahout.math.scalabindings
> {code}
> /**
>  * create a sparse vector out of list of tuple2's with specific 
> cardinality(size),
>  * throws IllegalArgumentException if cardinality is not bigger than required 
> cardinality of sdata
>  * @param cardinality sdata
>  * @return
>  */
> def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
>   val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
>   if (cardinality < required) {
> throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
> bigger than required[%required]!")
>   }
>   val initialCapacity = sdata.size
>   val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
>   sdata.foreach(t ⇒ sv.setQuick(t._1, 
> t._2.asInstanceOf[Number].doubleValue()))
>   sv
> }
> {code}
> So user can specify the cardinality for the created sparse vector.
> This is very useful and convenient if user wants to create a DRM with many 
> sparse vectors and the vectors are not with the same actual size(but with the 
> same logical size, e.g. rows of a sparse matrix).
> Below code should demonstrate the case:
> {code}
> var cardinality = 20
> val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line => 
> (line(0).toInt, Array((line(1).toInt,1.reduceByKey((v1, v2) => v1 ++ 
> v2).map(row => (row._1, svec(cardinality, row._2)))
> val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))
> // All element wise opperation will fail for those DRM with not 
> cardinality-consistent SparseVector
> val drm2 = drm + drm
> val drm3 = drm - drm
> val drm4 = drm * drm
> val drm5 = drm / drm
> {code}
> Notice that in the last map, the svec in above accepts one more parameter, so 
> the cardinality of those created SparseVector can be consistent.



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[jira] [Commented] (MAHOUT-1833) One more svec function accepting cardinality as parameter

[Edmond Luo (JIRA)]

[ 
https://issues.apache.org/jira/browse/MAHOUT-1833?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15247368#comment-15247368
 ] 

Edmond Luo commented on MAHOUT-1833:


I have implemented all above code and added some testing code for the new 
function, 
However, I am not sure if I should add some DRM test cases using sparse vector, 
seems now we do not have any test case for those DRM built from sparse vector.

> One more svec function accepting cardinality as parameter 
> --
>
> Key: MAHOUT-1833
> URL: https://issues.apache.org/jira/browse/MAHOUT-1833
> Project: Mahout
>  Issue Type: Improvement
>Affects Versions: 0.12.0
> Environment: Mahout Spark Shell 0.12.0,
> Spark 1.6.0 Cluster on Hadoop Yarn 2.7.1, 
> Centos7 64bit
>Reporter: Edmond Luo
>
> It will be nice to add one more wrapper function like below to 
> org.apache.mahout.math.scalabindings
> {code}
> /**
>  * create a sparse vector out of list of tuple2's with specific 
> cardinality(size),
>  * throws IllegalArgumentException if cardinality is not bigger than required 
> cardinality of sdata
>  * @param cardinality sdata
>  * @return
>  */
> def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
>   val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
>   if (cardinality < required) {
> throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
> bigger than required[%required]!")
>   }
>   val initialCapacity = sdata.size
>   val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
>   sdata.foreach(t ⇒ sv.setQuick(t._1, 
> t._2.asInstanceOf[Number].doubleValue()))
>   sv
> }
> {code}
> So user can specify the cardinality for the created sparse vector.
> This is very useful and convenient if user wants to create a DRM with many 
> sparse vectors and the vectors are not with the same actual size(but with the 
> same logical size, e.g. rows of a sparse matrix).
> Below code should demonstrate the case:
> {code}
> var cardinality = 20
> val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line => 
> (line(0).toInt, Array((line(1).toInt,1.reduceByKey((v1, v2) => v1 ++ 
> v2).map(row => (row._1, svec(cardinality, row._2)))
> val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))
> // All element wise opperation will fail for those DRM with not 
> cardinality-consistent SparseVector
> val drm2 = drm + drm
> val drm3 = drm - drm
> val drm4 = drm * drm
> val drm5 = drm / drm
> {code}
> Notice that in the last map, the svec in above accepts one more parameter, so 
> the cardinality of those created SparseVector can be consistent.



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[jira] [Created] (MAHOUT-1833) One more svec function accepting cardinality as parameter

[Edmond Luo (JIRA)]

Edmond Luo created MAHOUT-1833:
--

 Summary: One more svec function accepting cardinality as parameter 
 Key: MAHOUT-1833
 URL: https://issues.apache.org/jira/browse/MAHOUT-1833
 Project: Mahout
  Issue Type: Improvement
Affects Versions: 0.12.0
 Environment: Mahout Spark Shell 0.12.0,
Spark 1.6.0 Cluster on Hadoop Yarn 2.7.1, 
Centos7 64bit
Reporter: Edmond Luo


It will be nice to add one more wrapper function like below to 
org.apache.mahout.math.scalabindings

{code}
/**
 * create a sparse vector out of list of tuple2's with specific 
cardinality(size),
 * throws IllegalArgumentException if cardinality is not bigger than required 
cardinality of sdata
 * @param cardinality sdata
 * @return
 */
def svec(cardinality: Int, sdata: TraversableOnce[(Int, AnyVal)]) = {
  val required = if (sdata.nonEmpty) sdata.map(_._1).max + 1 else 0
  if (cardinality < required) {
throw new IllegalArgumentException(s"Cardinality[%cardinality] must be 
bigger than required[%required]!")
  }

  val initialCapacity = sdata.size
  val sv = new RandomAccessSparseVector(cardinality, initialCapacity)
  sdata.foreach(t ⇒ sv.setQuick(t._1, t._2.asInstanceOf[Number].doubleValue()))
  sv
}
{code}

So user can specify the cardinality for the created sparse vector.

This is very useful and convenient if user wants to create a DRM with many 
sparse vectors and the vectors are not with the same actual size(but with the 
same logical size, e.g. rows of a sparse matrix).

Below code should demonstrate the case:
{code}
var cardinality = 20
val rdd = sc.textFile("/some/file.txt").map(_.split(",")).map(line => 
(line(0).toInt, Array((line(1).toInt,1.reduceByKey((v1, v2) => v1 ++ 
v2).map(row => (row._1, svec(cardinality, row._2)))

val drm = drmWrap(rdd.map(row => (row._1, row._2.asInstanceOf[Vector])))

// All element wise opperation will fail for those DRM with not 
cardinality-consistent SparseVector
val drm2 = drm + drm
val drm3 = drm - drm
val drm4 = drm * drm
val drm5 = drm / drm
{code}

Notice that in the last map, the svec in above accepts one more parameter, so 
the cardinality of those created SparseVector can be consistent.




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