On Oct 11, 2011, at 2:54 PM, Sean Owen wrote:
> NaN is added for all user item pairs that already exist in the input, to
> make them ineligible for recommendation. That's normal - could this be the
> case?
Trying to track down. I don't think it is the self case, but not 100% sure.
> On Oct 11, 2011 7:49 PM, "Grant Ingersoll" <gsing...@apache.org> wrote:
>
>>
>> On Oct 11, 2011, at 12:36 PM, Sean Owen wrote:
>>
>>> Where is the NaN coming up -- what has this value?
>>
>> simColumn seems to be the originator in the Aggregate step. For instance,
>> my current breakpoint shows:
>> {309682:0.9566912651062012,42938:0.9566912651062012,309672:NaN}
>>
>> I can also see some in the PartialMultiplyMapper via the
>> similarityMatrixColumn.
>>
>> Is that set by SimilarityMatrixRowWrapperMapper?
>> <code>
>> /* remove self similarity */
>> similarityMatrixRow.set(key.get(), Double.NaN);
>> </code>
>>
>>
>>
>>> It should be propagated in some cases but not others. I'm not aware of
>>> any changes here.
>>
>> yeah, me neither. This is all related to MAHOUT-798.
>>
>>>
>>> Generally small data sets will have this problem of not being able to
>>> compute much of anything useful, so NaN might be right here.
>>> But you say it was different recently, which seems to rule that out.
>>
>> I also _believe_ I'm seeing it in a much larger data set on Hadoop, it's
>> just that's a whole lot harder to debug.
>>
>>>
>>> On Tue, Oct 11, 2011 at 5:34 PM, Grant Ingersoll <gsing...@apache.org>
>> wrote:
>>>> I'm running trunk RecommenderJob (via build-asf-email.sh) and am not
>> getting any recommendations due to NaNs being calculated in the
>> AggregateAndRecommend step. I'm not quite sure what is going on as it seems
>> like this was working as little as two weeks ago (post Sebastian's big
>> change to RecJob), but I don't see a whole lot of changes in that part of
>> the code.
>>>>
>>>> The data is user id's mapping to email thread ids. My input data is
>> simply a triple of user id, thread id, 1 (meaning that user participated in
>> that thread) It seems like I will have a lot of good values in the inputs
>> to the AggregateAndRecommend step, except one id will be NaN and this then
>> seems to get added in and makes everything NaN (I realize this is a very
>> naive understanding). I sense that I should be looking upstream in the
>> process for a fix, but I am not sure where that is.
>>>>
>>>> Any ideas where I should be looking to eliminate these NaNs? If you
>> want to try this with a small data set, you can get it here:
>> http://www.lucidimagination.com/devzone/technical-articles/scaling-mahout(but
>> note the companion article is not published yet.)
>>>>
>>>> Thanks,
>>>> Grant
>>
>>
>>
--------------------------------------------
Grant Ingersoll
http://www.lucidimagination.com
Lucene Eurocon 2011: http://www.lucene-eurocon.com
