Re: [Scikit-learn-general] Interested in participating in GSoC 2012

I will surely start using and working on the project. Thank you for your time, Harsh Mehta On Fri, Feb 3, 2012 at 6:26 PM, Andreas wrote: > Hi Harsh. > Thank you for your interest. > There was a thread on this theme on the list before: > > http://sourceforge.net/mailarchive/forum.php?thread_na

Re: [Scikit-learn-general] weighted clustering?

Andy- Thanks for the quick response. That is definitely a quick and easy hack - I'll try it out. The scenario is indeed online. We're collecting datapoints over time, and the underlying environment may change as the experiment runs. I'd like to be able to adapt to these changes more quic

Re: [Scikit-learn-general] optimization with constraints

On Sat, Feb 4, 2012 at 1:16 AM, Vlad Niculae wrote: > Sorry for being vague. NMF indeed tends to generally return sparse > representations. I meant sparse NMF as in an implementation of NMF that can > take sparse matrices as inputs (effectively keeping either the larger of W, H > or both as sp

Re: [Scikit-learn-general] weighted clustering?

On 02/03/2012 05:33 PM, Ben Clay wrote: Hi- I am using Mean Shift clustering with good results. Mean Shift was chosen because I don't know the number of clusters ahead of time, and the number of samples is very small (<100) so performance is a non-issue. Now I need to enforce an aging sche

[Scikit-learn-general] weighted clustering?

Hi- I am using Mean Shift clustering with good results. Mean Shift was chosen because I don't know the number of clusters ahead of time, and the number of samples is very small (<100) so performance is a non-issue. Now I need to enforce an aging scheme, so that older samples influence the

Re: [Scikit-learn-general] optimization with constraints

On Feb 3, 2012, at 18:07 , Mathieu Blondel wrote: > On Fri, Feb 3, 2012 at 11:55 PM, Vlad Niculae wrote: > >> The scipy NNLS is written in Fortran. I'd like to bench _nls_subproblem >> against it. >> Maybe we could have a cython projected sgd non-negative least square method >> with L1 constra

Re: [Scikit-learn-general] optimization with constraints

I had a need for a non-negative logistic classifier a while back, and wrote a light-weight function that does the optimization directly, along with an L2 regularizer. The code is on gist: https://gist.github.com/1730797 -Marc On Fri, Feb 3, 2012 at 9:55 AM, Vlad Niculae wrote: > A nice idea wo

Re: [Scikit-learn-general] optimization with constraints

A nice idea would be to extend the scipy NNLS in the ways needed to use it in scikit-learn's NMF instead of the _nls_subproblem code translated from C.J. Lin's code. The scipy NNLS is written in Fortran. I'd like to bench _nls_subproblem against it. Maybe we could have a cython projected sgd no

Re: [Scikit-learn-general] Interested in participating in GSoC 2012

2012/2/3 Andreas : > Hi Harsh. > Thank you for your interest. > There was a thread on this theme on the list before: > http://sourceforge.net/mailarchive/forum.php?thread_name=CAOKSrLzaX4COZmD-GGD7GeKE%2BgfpWw4z8%3DMXZkzAxvfjrA_B2Q%40mail.gmail.com&forum_name=scikit-learn-general > There is an entr

Re: [Scikit-learn-general] GridSearch

Hi, You can inject your fit params using the `fit_params` parameter in GridSearchCV. Gilles On 3 February 2012 13:59, Mathias Verbeke wrote: > Hi Andreas, > >> You would have to add it to the "fit" method of SVC, not GridSearchCV. > > > How can this be done in the digits example, since there's

Re: [Scikit-learn-general] GridSearch

Hi Andreas, You would have to add it to the "fit" method of SVC, not GridSearchCV. > How can this be done in the digits example, since there's only one fit there, namely the one of GridSearch? > > Does this mean class weighting isn't possible at all with GridSearch? > At the moment, yes. > > If

Re: [Scikit-learn-general] Interested in participating in GSoC 2012

Hi Harsh. Thank you for your interest. There was a thread on this theme on the list before: http://sourceforge.net/mailarchive/forum.php?thread_name=CAOKSrLzaX4COZmD-GGD7GeKE%2BgfpWw4z8%3DMXZkzAxvfjrA_B2Q%40mail.gmail.com&forum_name=scikit-learn-general There is an entry in the wiki but that is sti

Re: [Scikit-learn-general] GridSearch

On 02/03/2012 01:47 PM, Mathias Verbeke wrote: > Hi Andreas, > > Thanks for the answer. Hm, that's a pity. When I add it as a parameter > to fit, I get > > AssertionError: Invalid parameter class_weight for estimator GridSearchCV > You would have to add it to the "fit" method of SVC, not GridSearc

[Scikit-learn-general] Interested in participating in GSoC 2012

Dear friends, I am interested to participate in Scikit-learn project through GSoC. I wanted to learn about what are the things that i can work on before the real application. I also wanted to know the projects that can be undertaken in GSoC. Thank you for your time, Harsh Mehta Undergraduate

Re: [Scikit-learn-general] GridSearch

Hi Andreas, Thanks for the answer. Hm, that's a pity. When I add it as a parameter to fit, I get AssertionError: Invalid parameter class_weight for estimator GridSearchCV Does this mean class weighting isn't possible at all with GridSearch? Thanks, Mathias On Fri, Feb 3, 2012 at 1:30 PM, And

Re: [Scikit-learn-general] GridSearch

Hi Mathias. As far as I know the use of class weights in grid search is not possible in SVC at the moment. It can be used as a parameter to fit, but this prevents one from using it for grid searches. This is a known issue and the class_weight should be moved to the initialization of SVC. I am (som

Re: [Scikit-learn-general] GridSearch

Hi Olivier, That's something I tried already, but then I get: AssertionError: Invalid parameter class_weight for estimator SVC Any idea what can be wrong? Thanks, Mathias On Fri, Feb 3, 2012 at 12:19 PM, Olivier Grisel wrote: > 2012/2/3 Mathias Verbeke : > > Hi Adreas, > > > > Thanks a lot;

Re: [Scikit-learn-general] GridSearch

2012/2/3 Mathias Verbeke : > Hi Adreas, > > Thanks a lot; that answers my questions. Just a quick check to be sure I > understand it correctly: the results in the classification report for the > best classifier are the ones on the test set, right? It print the performance measured on the test set

Re: [Scikit-learn-general] GridSearch

Hi Adreas, Thanks a lot; that answers my questions. Just a quick check to be sure I understand it correctly: the results in the classification report for the best classifier are the ones on the test set, right? And another small question: could you tell me how/where I need to set the class_weight

Re: [Scikit-learn-general] GridSearch

Hi Mathias. First, please note that you are looking at an "old" version of the docs. We are in the process to include a warning. Please refer to http://scikit-learn.org/stable/auto_examples/grid_search_digits.html instead. For

[Scikit-learn-general] GridSearch

Hi all, I'm currently looking at the GridSearch example ( http://scikit-learn.org/0.9/auto_examples/grid_search_digits.html), and I don't completely get the point of using cross-validation twice. Why aren't the parameters and the classifier selected in on cross-validations step? Furthermore, I wa

Re: [Scikit-learn-general] Fw: Improving the accuracy of classifier

It extracted 155646 features. From: Olivier Grisel To: adnan rajper ; [email protected] Sent: Friday, February 3, 2012 1:34 PM Subject: Re: [Scikit-learn-general] Fw: Improving the accuracy of classifier 2012/2/3 adnan rajper : > > >

Re: [Scikit-learn-general] optimization with constraints

For non-negative least-squares, you can use this: http://docs.scipy.org/doc/scipy-0.7.x/reference/generated/scipy.optimize.nnls.html We could also add an estimator that implements fit and predict in scikit-learn (although the above function doesn't support sparse matrices :$) Mathieu --

Re: [Scikit-learn-general] Fw: Improving the accuracy of classifier

2012/2/3 adnan rajper : > > > Yes Peter, Indeed I am doing sentiment classification. > > > Your suggestion are highly appreciable. Sorry, but I am not able to > understand your question: "how many features do you have?". Would you care > to elaborate it? In scikit-learn parlance if you have a 2D d