PEP8 violations are reaching a critical level causing a risk of code
style meltdown:
https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.7-numpy-1.6.2-scipy-0.10.1/violations/
We should be more careful in checking pep8 compliance prior to merging
PRs from now on :)
And remember kids:
Thanks for your responses.
@Kyle:
At the risk of sounding really naive, I'd like to make the following
comments. I'm referring to this paper that Sukru had posted,
http://www.stat.osu.edu/~dmsl/Sarwar_2001.pdf which is item based
collaborative filtering. I don't think there is really any need for
So X is the array of existing ratings, would y be a 2D array then? If not,
how do you map the ratings given back to a single user (since y is
typically, to my knowledge, 1D in sklearn)?
I am still a little confused, but your example helped. Can you could go
into a little more detail on X, x, and
Well y can be 2-D too, there are estimators like MultiTaskElasticNet
especially meant for multi-task y.
I was thinking something along these lines. Lets say
[ham, spam, ram, bam, tam] are the five items.
and if first user gives
ham - 2
spam - 3
the second user gives
ram - 1
bam - -3
tam - 4
I'm extremely sorry, that message got sent half way through. (I pressed
Ctrl + Enter by mistake)
X = [[ham, spam], [ram, bam, tam]], and y = [[2, 3], [1, -3, 4]]
and we do clf.fit(X, y)
Suppose we would like to predict, what we would recommend the user x who
has already rated ram as 1 and bam as
Hi scikit-learn editors,
Any documentation can have mistakes, but it's important to address them
quickly and efficiently. One plausible way is to contact the author of
an erroneous text to have him make proper changes. But, for all I know,
scikit-learn's documentation
lacks authors'
I agree that sparse matrices need to be supported as one of the main
properties inherent to the user/item rating matrix in recommender systems is
its sparsity. This sparsity is what has given rise to such a large scale of
research in the field. Hence this property would have to be taken
I would rather have this sorted out through the github issue tracker.
I don't think it's a good idea to encourage users to e-mail individual
developers. Someone else could have the expertise and do the change
confidently.
My 2c,
Vlad
On Thu Jan 16 18:12:05 2014, Issam wrote:
Hi scikit-learn
@Manoj
The predict stage taking 2 parameters is what I was talking about - are
there any other estimators that need anything more than a single matrix to
do a prediction? I do not recall any - this would be something particular
to CF. Maybe you could recast it as a matrix with alternating rows of
Yes indeed, getting two parameters for predict would be specific to CF.
That was the most obvious idea that came to my mind. I would like to hear
other's opinions also regarding the API, and the feasibility of such a
project.
On Thu, Jan 16, 2014 at 11:47 PM, Kyle Kastner
I agree with Vlad.
Further, if there is documentation or a module that none of the active
developers can touch (due to complexity or lack of expertise), the
preference has generally been to move to remove it from scikit-learn.
On 17 January 2014 05:12, Vlad Niculae zephy...@gmail.com wrote:
I
The other thing to keep mind an ideal solution would be compatible with
Pipeline() - it would be nice to be able to use it there, which is one of
the reasons a different signature for the predict() method is an issue.
Hopefully something can be figured out, as there is a lot interest in CF
`y` is by definition hidden at prediction time for supervised learning, so
I don't think your representation makes sense. But I see this as a
completion problem, not a supervised learning problem: the same data is
observed at training and predict time.
Isn't the following:
X = [[ham, spam], [ram,
2014/1/16 Joel Nothman joel.noth...@gmail.com:
There are still issues of whether this is in scikit-learn scope. For
example, does it make sense with sklearn's cross validation? Or will you
want to cross validate on both axes? Given that there is plenty of work to
be done that is well within
Hi all,
Jaques and I have recently been working on moving the dev
documentation build job out of Fabian's workstation to a server on the
public Rackspace Cloud.
The deployment of the documentation build server is now fully
automated thanks to this script and configuration:
Thanks everyone for your quick responses.
1. Could someone point me to a list of GSoC ideas this year?
2. Is it okay, if I take up projects related to ideas, that have not yet
been implemented. For example, a quick search tells me Improving GMM has
not been implemented.
Thanks.
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