Hi all,
I recently was in the position of wanting to use a OneVsRest SVC for ~50
classes.
It turned out that this was not easily possible with sklearn because the
GridSearchCV class queries the classifier's _pairwise property to see if
the data is a square matrix and should be sliced in both dimensions or
if it is a matrix that only needs to be sliced in the first dimension.
What works for me is adding these lines to the MetaEstimatorMixin in
./base.py:
class MetaEstimatorMixin(object):
"""Mixin class for all meta estimators in scikit-learn."""
@property
def _pairwise(self):
# Used by cross_val_score.
return getattr(self.estimator, "_pairwise", False)
OneVsRestClassifier uses this mixin which so far was just a flag.
Now it takes the property from the wrapped class.
Is there a good reason why this was not the case before?Any pitfalls I
don't see coming at the moment?
Thanks for your input
/Daan
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