On Sun, Jun 2, 2013 at 6:34 PM, Joel Nothman
<jnoth...@student.usyd.edu.au>wrote:
> On Sun, Jun 2, 2013 at 6:08 PM, Mathieu Blondel <math...@mblondel.org>wrote:
>
>>
>>
>> On Sun, Jun 2, 2013 at 4:26 PM, Joel Nothman <
>> jnoth...@student.usyd.edu.au> wrote:
>>
>>>
>>> That's only true if users know they are required to pass binarized input
>>> to cross-validation routines such as GridSearchCV and cross_val_score, or
>>> else they might land up with a 2d array of ints instead of a 1d array of
>>> objects.
>>>
>>
>> I hadn't thought of compatibility with grid search... We need to add
>> tests for this in test_multiclass.py.
>>
>> Yes, or at least cross_val_score. I haven't taken a look in detail at
> test_multiclass.py, but we at least should be testing for identical results
> given: sequence of sequences, binarized with neg_label=0, binarized with
> neg_label=-1. And we need to test it with a realistic dataset, and the edge
> case where all entries have the same number of labels (and hence np.asarray
> does something different). And if there are no fundamental failures in
> this, perhaps sequences of sequences aren't so harmful.
>
Now an issue: https://github.com/scikit-learn/scikit-learn/issues/2022
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