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madrury / glmnet-python

Wrappers of Jerome Friedman's coordinate-descent Fortran implementation of lasso/elastic net regression from the R "glmnet" package.
GNU General Public License v2.0
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supporting n_obs x n_class target in logistic regression #4

Open afshinrahimi opened 8 years ago

afshinrahimi commented 8 years ago

Hi,

I was just using the code to do a regularized multinomial logistic regression where each instance's label is a probability distribution over all labels and the design matrix X is sparse. I found that it is not supported yet as _check_y throws an exception if y's dimension is not eq 1.

It would be great if it is implemented because currently no other library (that I know of) supports that and really need this feature. If you don't have enough time for that would you please guide me what is the best way to start implementing that in the code?

Thanks.

madrury commented 8 years ago

I saw your post on cross validated : )

On the face of it, I'm not sure what it would take. The library depends on the FORTRAN code of hastie et.al. I have no ability to modify that FORTAN code (it's a nightmare, and was seemingly written to make sure no one can understand it). The python-glmnet library simply hooks into the FORTAN code from python code. So, I would investigate two things:

1) Does the FORTRAN code support a multinomial distribution? 2) If so, can you call it with a discrete distribution as arguments?

You should be able to answer these questions by reading the comments in the FORTRAN. If the answer's are yes, then it should be possible to wrap the multinomial calls in python.

On Sun, May 15, 2016 at 5:50 PM, afshinrahimi notifications@github.com wrote:

Hi,

I was just using the code to do a regularized multinomial logistic regression where each instance's label is a probability distribution over all labels. I found that it is not supported yet as _check_y throws an exception if y's dimension is not eq 1.

It would be great if it is implemented because currently no other library (that I know of) supports that and really need this feature. If you don't have enough time for that would you please guide for me what is the best way to start implementing that in the code?

Thanks.

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afshinrahimi commented 8 years ago

Thank you very much for the prompt reply and the useful guides. I'll give it a try.