[scikit-learn] Sparse predict_proba and Fenchel-Young losses

Andreas Mueller t3kcit at gmail.com
Thu Oct 25 12:26:15 EDT 2018


Awesome!

On 10/23/18 9:10 AM, Mathieu Blondel wrote:
> Hi,
>
> Most scikit-learn users who need predict_proba use the logistic 
> regression class. We've released a new package implementing more loss 
> functions useful for probabilistic classification.
>
> https://github.com/mblondel/fenchel-young-losses/
>
> This is based on our recently proposed family of loss functions called 
> "Fenchel-Young losses" [*].
>
> Two distinguishing features that should be of interest:
>
> 1) You can call fit(X, Y) where Y is a n_samples array of label 
> integers *or* Y is a n_samples x n_classes array containing *label 
> proportions*.
We've gotten that feature request for logistic regression a couple of 
times, not sure it's in the scope of scikit-learn.
Great to see that you've done it!

>
> 2) predict_proba(X) is able to output *sparse* probabilities for some 
> choices of loss functions (loss="sparsemax" or loss="tsallis"). This 
> means that some classes may get *exactly* zero probability.
>
> Both features are especially useful in a multi-label setting.
>
> We've also released drop-in replacements for PyTorch and Tensorflow 
> loss functions in the same package.
>
> Feedback welcome!
>
> Cheers,
> Mathieu
>
> [*] https://arxiv.org/abs/1805.09717
>
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