hi,

perhaps i'm completely missing the point here. but isn't the whole point of
the validation set that nobody touches it to do any model/feature selection?
all it should be used for is to test your model after it has been finalized.
and if it is used for model/feature selection, i do not see it as being any
different from current X.

cheers,

satra


On Fri, Oct 28, 2011 at 10:27 AM, Olivier Grisel
<[email protected]>wrote:

> 2011/10/28 Alexandre Gramfort <[email protected]>:
> > class SingleSplit(object):
> >
> >    def __init__(self, train_index, test_index):
> >        self.train_index = train_index
> >        self.test_index = test_index
> >
> >    def __iter__(self):
> >        yield self.train_index, self.test_index
> >
> > is this more complicated than this?
>
> It's just that in Andreas' case de datasets are already splitted. To
> use the GridSearchCV you have to concatenate them to able to call the
> fit API.
>
> X_train, y_train = load_svmlight_file("/path/to/training/set.dat")
> X_validation, y_validation =
> load_svmlight_file("/path/to/validation/set.dat")
>
> params_grid = { ... }
> cv =  SingleSplit(np.arange(X_train.shape[0]),
> np.arange(X_validation.shape[0]) + X_train.shape[0])
>
> X_total = np.vstack((X_train, X_validation))
> y_total = np.vstack((y_train, y_validation))
>
> GridSearchCV(params_grid, cv=cv).fit(X_total, y_total)
>
> This is a lot of complex boilerplate for the newcomer.
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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