Ah, thanks! Much better solution to what I did :)
Regards,
Stelios
2016-02-22 16:51 GMT+00:00 Andreas Mueller <t3k...@gmail.com>:
> You can just do this via a CV object. For example, use
> StratifiedShuffleSplit(train_set=.1, test_set=.1, n_folds=5)
> and your training and test set will be randomly samples disjoint 10% of
> the data, repeated 5 times.
>
>
>
> On 02/19/2016 11:42 AM, Gael Varoquaux wrote:
> > 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,
> >
> > Gaël
> >
> > 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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