Hi,
I'm hereby shamelessly advertising a blog post I've written up today
about setting up a recommender system with mahout and hadoop :)
http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/
--sebastian
OK that did work for mahout thanks!, but now hadoop cannot load the class, even
when
the jar containing it has been added to the hadoop classpath
hadoop@ubuntu:/home/camilo/mahout-distribution-0.4$ echo $HADOOP_CLASSPATH
/home/camilo/mahout-distribution-0.4/utils/target/dependency/lucene-core-3.
It is definitely a reasonable idea to convert data to hashed feature vectors
using map-reduce.
And yes, you can pick a vector length that is long enough so that you don't
have to worry about
collisions. You need to examine your data to decide how large that needs to
be, but it isn't hard
to do.
This code doesn't look right for category features.
Those features are usually described either as strings or as integers.
Either case can be handled as strings as
long as you don't have any surprises like leading 0's.
The best way to handle these features is to encode them using word encoders.
On Tue, Apr 19, 2011 at 11:02 PM, Stanley Xu wrote:
> What make me still a little confused is that, when training the model, I
> probably knew the errors, could we thought that the penalty I wanted was
> already counted in a loss function?
>
It could be, but usually isn't.
> And for weight the
The trainlogistic command is (as Stanley says) only a simple example.
You will need to write a program something like TrainNewsGroups for your
modelers to use.
I agree that the API oriented code in Mahout is not what those users need.
I was, however, what my users needed.
It would be great if y
Hi Ted,
I knew I have to change the encoder and parse it as a String(or byte array).
I am wondering even parse it as a byte array, it is still cost lot of time
in feature hashing, and both as you said and per hour test, the time spent
on feature hashing and parsing are normally dominate the SGD tr
Hi Ted,
I thought I got it but wanted to confirm once again for I am not a native
English speaker.
The add weight you mean here is re-define a train method, add a weight
parameter and adjust the learning rate of currentLearningRate() with this
param. Not the weight parameter already exist in the
On Thu, Apr 21, 2011 at 7:09 PM, Stanley Xu wrote:
>
> The add weight you mean here is re-define a train method, add a weight
> parameter and adjust the learning rate of currentLearningRate() with this
> param. Not the weight parameter already exist in the features. Am I correct?
>
Yes.
But you
On Thu, Apr 21, 2011 at 7:05 PM, Stanley Xu wrote:
> Hi Ted,
>
> I knew I have to change the encoder and parse it as a String(or byte
> array). I am wondering even parse it as a byte array, it is still cost lot
> of time in feature hashing, and both as you said and per hour test, the time
> spent
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