(fwiw I think it's worse ;) : scalars are 1x1 matrices, i.e. 2d vectors in
matlab)
On Mon, May 4, 2015 at 2:03 PM, federico vaggi <vaggi.feder...@gmail.com>
wrote:
> 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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