That won't work, as it is modifying the number of samples, which breaks
the scikit-learn pipeline.

Please add this usecase in the PR on the scikit-learn enhancement
proposal that discusses a possible modification to scikit-learn:
https://github.com/scikit-learn/enhancement_proposals/pull/2

Cheers,

Gaƫl

On Fri, Feb 19, 2016 at 11:36:29AM -0500, Sebastian Raschka wrote:
> Hi, Stelios,
> I am wondering, how did you implement this tweak? Just a thought, but instead 
> of adding extra functionality inside the GridSearch class, what about using a 
> random training data selector (transformer) as a pipeline object? Something 
> along the lines of

> class RandomRowSelector(object):
>     def __init__(self):
>         pass

>     def _some_random_sampling_function(self, X, y)

>     def transform(self, X, y):
>         sampled_rows = self.some_random_sampling_function(self, X, y)
>         return X[sampled_rows, :], y[sampled_rows, :]

>     def fit(self, X, y=None):
>         return self

> Best,
> Sebastian

> > On Feb 19, 2016, at 7:56 AM, Stylianos Kampakis 
> > <stylianos.kampa...@gmail.com> wrote:

> > Hi everyone,

> > I was thinking to implement a tweak where it is possible to sample randomly 
> > from a dataset when using grid search. This would particularly useful for 
> > big datasets. The sampling takes place during each round of grid search.

> > Does anyone think this would be worthy submitting to scikit-learn?

> > Best regards,
> > Stelios
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