I think Gael makes a very strong argument, but I think the error should be
as explicit and informative as possible (for new users).
On Fri, May 1, 2015 at 7:58 PM, 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
>
> >
>
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>
> --
> 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
>
>
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