Shouldn't that be "score_samples"? Well, it is a conditional likelihood p(y|x), not p(x) or p(x, y). But it is the likelihood of some data given the model.
On 07/29/2015 02:58 AM, Jan Hendrik Metzen wrote: > Such a predict_proba_at() method would also make sense for Gaussian > process regression. Currently, computing probability densities for GPs > requires predicting mean and standard deviation (via "MSE") at X and > using scipy.stats.norm.pdf to compute probability densities for y for > the predicted mean and standard-deviation. I think it would be nice to > allow this directily via the API. Thus +1 for adding a method like > predict_proba_at(). > > Jan > > On 29.07.2015 06:42, Mathieu Blondel wrote: >> Regarding predictions, I don't really see what's the problem. Using >> GLMs as an example, you just need to do >> >> def predict(self, X): >> if self.loss == "poisson": >> return np.exp(np.dot(X, self.coef_)) >> else: >> return np.dot(X, self.coef_) >> >> A nice thing about Poisson regression is that we can query the >> probability p(y|x) for a specific integer y. >> https://en.wikipedia.org/wiki/Poisson_regression >> >> We need to decide an API for that (so far we have used predict_proba >> for classification so the output was always n_samples x n_classes). >> How about predict_proba(X, at_y=some_integer)? >> >> However, this is also mean that we can't use predict_proba to detect >> classifiers anymore... >> Another solution would be to introduce a new method >> predict_proba_at(X, y=some_integer)... >> >> Mathieu >> >> >> On Wed, Jul 29, 2015 at 4:19 AM, Andreas Mueller <t3k...@gmail.com >> <mailto:t3k...@gmail.com>> wrote: >> >> I was expecting there to be the actual poisson loss implemented in >> the class, not just a log transform. >> >> >> >> On 07/28/2015 02:03 PM, josef.p...@gmail.com >> <mailto:josef.p...@gmail.com> wrote: >>> Just a comment from the statistics sidelines >>> >>> taking log of target and fitting a linear or other model doesn't >>> make it into a Poisson model. >>> >>> But maybe "Poisson loss" in machine learning is unrelated to the >>> Poisson distribution or a Poisson model with E(y| x) = exp(x beta). ? >>> >>> Josef >>> >>> >>> On Tue, Jul 28, 2015 at 2:46 PM, Andreas Mueller >>> <t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote: >>> >>> I'd be happy with adding Poisson loss to more models, thought >>> I think it would be more natural to first add it to GLM >>> before GBM ;) >>> If the addition is straight-forward, I think it would be a >>> nice contribution nevertheless. >>> 1) for the user to do np.exp(gbmpoisson.predict(X)) is not >>> acceptable. This needs to be automatic. It would be best if >>> this could be done in a minimally intrusive way. >>> >>> 2) I'm not sure, maybe Peter can comment? >>> >>> 3) I would rather contribute sooner, but other might thing >>> differently. Silently ignoring sample weights is not an >>> option, but you can error if they are provided. >>> >>> Hth, >>> Andy >>> >>> >>> On 07/23/2015 08:52 PM, Peter Rickwood wrote: >>>> Hello sklearn developers, >>>> >>>> I'd like the GBM implementation in sklearn to support >>>> Poisson loss, and I'm comfortable in writing the code (I >>>> have modified my local sklearn source already and am using >>>> Poisson loss GBM's). >>>> >>>> The sklearn site says to get in touch via this list before >>>> making a contribution, so is it worth me to submitting >>>> something along these lines? >>>> >>>> If the answer is yes, some quick questions: >>>> >>>> 1) The simplest implementation of poisson loss GBMs is to >>>> work in log-space (i.e. the GBM predicts log(target) rather >>>> than target), and require the user to then take the >>>> exponential of those predictions. So, you would need to do >>>> something like: >>>> gbmpoisson = >>>> sklearn.ensemble.GradientBoostingRegressor(...) >>>> gbmpoisson.fit(X,y) >>>> preds = np.exp(predict(X)) >>>> I am comfortable making changes to the source for this to >>>> work, but I'm not comfortable changing any of the >>>> higher-level interface to deal automatically with the >>>> transform. In other words, other developers would need to >>>> either be OK with the GBM returning transformed predictions >>>> in the case where "poisson" loss is chosen, or would need to >>>> change code in the 'predict' function to automatically do >>>> the transformation is poisson loss was specified. Is this OK? >>>> 2) If I do contribute, can you advise what the best tests >>>> are to test/validate GBM loss functions before they are >>>> considered to 'work'? >>>> >>>> 3) Allowing for weighted samples is in theory easy enough to >>>> implement, but is not something I have implemented yet. Is >>>> it better to contribute code sooner that doesn't handle >>>> weighting (i.e. just ignores sample weights), or later that >>>> does? >>>> >>>> >>>> >>>> >>>> Cheers, and thanks for all your work on sklearn. Fantastic >>>> tool/library, >>>> >>>> >>>> >>>> Peter >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> ------------------------------------------------------------------------------ >>>> >>>> >>>> _______________________________________________ >>>> Scikit-learn-general mailing list >>>> Scikit-learn-general@lists.sourceforge.net >>>> <mailto:Scikit-learn-general@lists.sourceforge.net> >>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >>> ------------------------------------------------------------------------------ >>> >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> <mailto:Scikit-learn-general@lists.sourceforge.net> >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >>> >>> >>> >>> ------------------------------------------------------------------------------ >>> >>> >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> <mailto:Scikit-learn-general@lists.sourceforge.net> >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >> ------------------------------------------------------------------------------ >> >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> <mailto:Scikit-learn-general@lists.sourceforge.net> >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >> >> >> ------------------------------------------------------------------------------ >> >> >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
