Yeah, especially coming from MATLAB, where there are so many weird special
cases (scalars being 1 dimensional vectors, etc) to make it easy to use.
On Mon, May 4, 2015 at 1:47 PM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> On Mon, May 04, 2015 at 01:32:02PM +0200, federico vaggi wrote:
> > I think Gael makes a very strong argument, but I think the error should
> be as
> > explicit and informative as possible (for new users).
>
> +1. Including suggesting the syntax X[:, np.newaxis], which is not
> trivial.
>
> G
>
> > 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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> >
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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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