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
> <[email protected]> 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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