ecommender returning 0 recommendations.
On Mon, Aug 12, 2013 at 9:47 AM, Matt Molek wrote:
> I'm using a custom PlusAnonUser recommender which is just a
> GenericBooleanPrefUserBasedRecommender with a PlusAnonymousUser DataModel
> wrapped around a GenericBoo
2013 at 4:13 PM, Ted Dunning wrote:
> On Fri, Aug 9, 2013 at 12:30 PM, Matt Molek wrote:
>
> > From some local IR precision/recall testing, I've found that user based
> > recommenders do better on my data, so I'd like to stick with user based
> if
> > I ca
input to the mostSimilarItems()
> method. That should give you the same results.
>
>
> On 09.08.2013 03:33, Matt Molek wrote:
> > Thanks, Sebastian.
> >
> > To get around this problem, I was just reading about the
> > PlusAnonymousUserDataModel. Would that be
save new interactions in a database and
> load them into memory from time to time.
>
> 2013/8/8 Matt Molek
>
> > Ok, having implemented a recommender that tried to call
> setPreference(...)
> > on a GenericBooleanPrefUserBasedRec
> > o
d new user-item associations to the model
though. Is this just no possible? That seems weird. I thought all of the
in-memory models supported having new data added on the fly. Am I missing
something?
Thanks for the help,
Matt
On Thu, Aug 8, 2013 at 12:31 PM, Matt Molek wrote:
>
I'm using a GenericBooleanPrefUserBasedRecommender with a
GenericBooleanPrefDataModel.
When I load the historical user/item associations from a file, they're just
in the format of userid, itemid, and as I understand it, the
GenericBooleanPrefDataModel does not store any 'rating' data.
I'd like to
Oops, I'm sorry. I had one too many zeros there, should be
'-Dmapred.max.split.size=10'
Just (input size)/(desired number of mappers)
One trick to getting more mappers on a job when running from the command
line is to pass a '-Dmapred.max.split.size=' argument. The is a
size in bytes. So if you have some hypothetical 10MB input set, but you
want to force ~100 mappers, use '-Dmapred.max.split.size=100'
On Wed, Jul 3
I'm losing a some documents when running seq2sparse. I think it's
because the documents are composed of common terms, and end up having
no terms at all once common words are pruned. I couldn't find
documentation that this is what's supposed to be happening though, so
I wanted to ask if this is expe
Instead of manually splitting your files, you should be able pass
-Dmapred.min.split.size= at the command line, or
otherwise set the mapred.min.split.size property to get the number of
mappers you want.
On Wed, Apr 17, 2013 at 7:55 PM, Ryan Compton wrote:
> Got it, thanks.
>
> For some reason I h
Hi Vivek,
Not sure if you're still trying to resolve this, but I got it working.
git clone http://git-wip-us.apache.org/repos/asf/mrunit.git
failed for me as well with error 405, "fatal: The remote end hung up
unexpectedly"
Just on a whim, I tried https://... instead and that worked.
On Fri,
oints file I
> see the following output:
>
> 1.0: [3887:3.000, 9441:1.000] is in 1205002
> 1.0: [6773:1.000] is in 1205002
> 1.0: [8987:2.000] is in 1205002
> 1.0: [2956:1.000] is in 1205002
>
>
> Thanks again,
> Colum
>
> On Tue, Mar 5, 2013 at 8:57 PM, Matt Mo
If you run kmeans with the "-cl" option (or set the runClustering option to
true if you're calling the driver from Java code), you'll get a sequence
file in the directory $KMEANS_OUT/clusteredPoints with an IntWritiable key
identifying the cluster, and a WeightedVectorWritable with a pdf weight
(al
n pdf value of 0.0200.
Do these pdf values say anything about the fit or quality of my cluster
results?
On Fri, Mar 1, 2013 at 2:56 AM, Ted Dunning wrote:
> How high is the dimension?
>
> How is your data generated?
>
>
>
> On Wed, Feb 27, 2013 at 1:38 PM, Matt Molek w
articular document was clustered or not.
>
> On 27 Feb 2013, at 03:01, Matt Molek wrote:
>
> > I think you have the right idea about the clusterClassificationThreshold,
> > but something just isn't working right in your case.
> >
> > I know this answer won't
I think you have the right idea about the clusterClassificationThreshold,
but something just isn't working right in your case.
I know this answer won't be particularly helpful since I don't have any
suggestions to fix your problem, but I did a test recently where I tried
clusterClassificationThres
te.
That's the sort of thing I want to be able to do with KMeans on multiple
separate datasets.
On Tue, Nov 20, 2012 at 11:58 AM, Matt Molek wrote:
> I've given up on the CLI and I'm trying to do this in java now, but it
> looks like I can't launch multiple KMeans driv
ot too
familiar with concurrency in java).
I'd really like to be able to launch multiple clustering runs at the same
time since launching them one at a time and waiting for each to finish is
killing my overall performance.
On Thu, Nov 8, 2012 at 1:48 PM, Matt Molek wrote:
> When do
Isn't this the same question you asked earlier today?
I responded to the initial one that "-D mapred.task.timeout=1800"
shouldn't have a space after the D. It should be
"-Dmapred.task.timeout=1800"
And IIRC, these Hadoop parameters need to go before all of your other
parameters.
On Mon,
ses: 1) do LSA ( in terms of SSVD, it means --pca
>>>> false and take U output for document topic space), or 2) perhaps do
>>>> sphere projection first and then do dimensionality reduction with
>>>> --pca true. the latter will at least preserve cosine distances as f
Did you have those spaces "-D mapred.task.timeout=1800"? That
won't be parsed correctly. It should be:
"-Dmapred.task.timeout=1800"
On Mon, Oct 22, 2012 at 1:08 PM, Amit Krishna Joshi wrote:
> Hi,
>
> I am running PFP on several datasets and it works well for smaller ones (<
> 5GB)
> Howe
I've done some more testing and submitted a JIRA:
https://issues.apache.org/jira/browse/MAHOUT-1103
On Sat, Oct 20, 2012 at 9:01 PM, Matt Molek wrote:
> Thanks for the quick response!
>
> I will do some testing tomorrow with various numbers of clusters and
> create a JIRA
e/MAHOUT.
> And if you are interested, this would be a good starting point to
> contribute to Mahout also.
>
> On Sun, Oct 21, 2012 at 1:14 AM, Matt Molek wrote:
>
>> First off, thank you everyone for your help so far. This mailing list
>> has been a great help getting m
Sorry for the basic question. I've been reading about this for a few hours,
but I'm still confused. I want to use ssvd to reduce the dimensionality of
some tfidf-vectors so I can perform clustering on the result.
Among many other things, I've read:
https://cwiki.apache.org/MAHOUT/dimensional-reduc
I'm using mahout 0.7 on a pseudo-distributed hadoop installation for
testing purposes.
A lot of what I'm doing is being guided by Mahout in Action, which I
know deals with 0.5, but as far as I can tell, nothing major has
changed with seq2sparse.
I'm having a problem with the tfidf vectors generat
25 matches
Mail list logo
