I am in favour of raising a error. Arnaud
> On 01 May 2015, at 19:58, Gael Varoquaux <gael.varoqu...@normalesup.org> > wrote: > > I strongly advice raising an error. Very very very strongly. > > Being lax about ambiguous inputs makes prototyping and interactive usage > easier: less typing, and the systems gets it right most of the time. > However, it makes production use and debugging complex code much harder. > Indeed, errors, that might not be related to a simple user error but > might be generated by a complex framework, do not lead to exceptions, but > to problems down the line. > > We are not R. We require a bit more of typing, we don't have as many > shortcuts and magic syntax. But we can be used in production, on big > datasets. We can be used by people like Airbus to monitor failures of > part in planes [*], or by many others. > > Yes beginners want things to 'just work', but in the long run, they are > thankful for a well-thought and strict specification. > > Gaël > > > [*] > http://www.pyvideo.org/video/3519/scikit-learn-for-predictive-maintenance-at-airbus > > On Fri, May 01, 2015 at 06:51:00PM +0100, Luca Puggini wrote: >> I vote for 3. > >> On Fri, May 1, 2015 at 6:27 PM, Andreas Mueller <t3k...@gmail.com> wrote: > >> Hi all. >> A quick questions on future API. >> What should happen if a user passes an X with shape (N,), in other words >> X.ndim == 1? > >> This is unfortunately not really consistent in scikit-learn right now. >> Three things are possible: >> 1) Raise an error >> 2) N = n_features, that is X contains a single sample >> 3) N = n_samples, that is X has a single feature > >> I would think it should be N=n_samples. Gael thinks (iirc) we should raise >> an error. >> In the code, we currently take N=n_features in predict, decision_function, >> predict_proba and transform, basically everywhere. >> This is in part due to using ``check_array`` everywhere, which used the >> backward-compatible (but odd) behavior of np.atleast2d. > >> In ``fit``it looks like all estimators assume N=n_features, apart from >> DictionaryLearning, MinMaxScaler, StandardScaler, which assume >> N=n_samples. > >> See https://github.com/scikit-learn/scikit-learn/pull/4511 for more >> discussion > >> Obviously any change we make would mean a deprecation cycle, which will >> mean warning in 0.17 and 0.18 when someone gives a 1-dim X that we'll >> change something soon, and then actually change it in 0.19 (1.0?). > >> Andy > >> >> ------------------------------------------------------------------------------ >> One dashboard for servers and applications across Physical-Virtual-Cloud >> Widest out-of-the-box monitoring support with 50+ applications >> Performance metrics, stats and reports that give you Actionable Insights >> Deep dive visibility with transaction tracing using APM Insight. >> http://ad.doubleclick.net/ddm/clk/290420510;117567292;y >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > >> ------------------------------------------------------------------------------ >> One dashboard for servers and applications across Physical-Virtual-Cloud >> Widest out-of-the-box monitoring support with 50+ applications >> Performance metrics, stats and reports that give you Actionable Insights >> Deep dive visibility with transaction tracing using APM Insight. >> http://ad.doubleclick.net/ddm/clk/290420510;117567292;y > >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > -- > Gael Varoquaux > Researcher, INRIA Parietal > NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France > Phone: ++ 33-1-69-08-79-68 > http://gael-varoquaux.info http://twitter.com/GaelVaroquaux > > ------------------------------------------------------------------------------ > One dashboard for servers and applications across Physical-Virtual-Cloud > Widest out-of-the-box monitoring support with 50+ applications > Performance metrics, stats and reports that give you Actionable Insights > Deep dive visibility with transaction tracing using APM Insight. > http://ad.doubleclick.net/ddm/clk/290420510;117567292;y > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ One dashboard for servers and applications across Physical-Virtual-Cloud Widest out-of-the-box monitoring support with 50+ applications Performance metrics, stats and reports that give you Actionable Insights Deep dive visibility with transaction tracing using APM Insight. http://ad.doubleclick.net/ddm/clk/290420510;117567292;y _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
