Hi Olivier,
I solved this issue, but talking to some people in the maillist,
they adviced me to start a new project (already referenced in the wiki)
and latter think about include instance reduction in the sklearn.
https://github.com/dvro/scikit-protopy (name is not definite yet);
If you could take a look and give me some pointers, like:
- Is the use of 'fit' and 'reduce' ok? Or should I use 'transform'?
- Should I do the classifier setup in the __init__ (passing all
arguments of the KNN to in the InstanceReduction constructor)?
- Do you think I call it scikits.protopy (use: from scikits.protopy
import A) in order to be according to the scikits pattern?
If you have the time to take a look and answer any of those questions that
would be great.
Thanks,
On Wed, Dec 4, 2013 at 7:58 AM, Olivier Grisel <[email protected]>
wrote:
> Hi, indeed the generic exception catching / reraising of the test
> common stuff is not very helpful.
>
> You can add a test in you own test suite to check where it breaks in your
> code:
>
> import scipy.sparse as sp
>
> X_train_csr = sp.csr_matrix(X_train)
> X_test_csr = sp.csr_matrix(X_test)
> model = MyModel().fit(X_train_csr, y_train)
> model.score(X_test_csr, y_test)
>
> --
> Olivier
>
>
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--
*Dayvid Victor R. de Oliveira*
PhD Candidate in Computer Science at Federal University of Pernambuco (UFPE)
MSc in Computer Science at Federal University of Pernambuco (UFPE)
BSc in Computer Engineering - Federal University of Pernambuco (UFPE)
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