hi satra,

wouldn't a pipeline solve your pb?

see eg. :

http://scikit-learn.org/stable/auto_examples/feature_selection_pipeline.html#example-feature-selection-pipeline-py

Alex

On Wed, Nov 9, 2011 at 10:55 AM, Andreas Müller
<[email protected]> wrote:
> Hi Satra.
> Can you explain the use-case a bit more?
> I don't understand it from the code, sorry.
> Cheers,
> Andy
>
> On 11/09/2011 04:51 PM, Satrajit Ghosh wrote:
>
> i often find myself doing the following for cross-validation. i.e.
> estimating the transform from the training set. would this be useful as a
> parameter on cross_val_score, gridsearchcv, etc.,. if so i'll send a pr.
> ----
> class NoTransform():
>     def fit(self, X):
>         return self
>     def transform(self, X):
>         return X
> def doCV(clf, X, y, Tx, cvf):
>     result = []
>     for train, test in cvf:
>         if Tx is None:
>             T = NoTransform()
>         else:
>             T = Tx()
>         result.append((y1[test],
>                        clf.fit(T.fit(X[train]).transform(X[train]),
>                                y[train]).predict(T.transform(X[test]))
>                        ))
>     return result
> ----
> cheers,
>
> satra
>
>
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