Hi Martin.
This might be solved by issue 565
https://github.com/scikit-learn/scikit-learn/issues/565.
Maybe you should add there that keeping the estimators in memory also
prevents pickling.
Cheers,
Andy
On 03/13/2012 03:11 PM, Martin Fergie wrote:
> Good Afternoon,
>
> I'm trying to pickle a class that contains a reference to a
> GridSearchCV object and it is failing due to an instancemethod type. I
> was under the impression that scikit-learn objects should be
> picklable, is this correct? If so, would it be appropriate for me to
> raise an issue?
>
> Information about the Exception is included below.
>
> Thanks,
> Martin
>
> ---------------------------------------------------------------------------
> TypeError Traceback (most recent call last)
> /tmp/<ipython-input-7-687becba8c7c> in<module>()
> ----> 1 cPickle.dump(gs, te)
>
> /opt/epd-7.2/lib/python2.7/copy_reg.pyc in _reduce_ex(self, proto)
> 68 else:
> 69 if base is self.__class__:
> ---> 70 raise TypeError, "can't pickle %s objects" %
> base.__name__
> 71 state = base(self)
> 72 args = (self.__class__, base, state)
>
> TypeError: can't pickle instancemethod objects
>
>
> print GridSearchCV:
>
> GridSearchCV(cv=sklearn.cross_validation.StratifiedKFold(labels=[ 1.
> 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
> 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
> 0. 0. 0. 0. 0. 0.], k=10),
> estimator=LinearSVC(C=1.0, dual=True, fit_intercept=True,
> intercept_scaling=1,
> loss='l2', multi_class=False, penalty='l2', tol=0.0001),
> fit_params={}, iid=True, loss_func=None, n_jobs=4,
> param_grid={'C': [1, 10, 100, 1000, 10000, 100000]},
> pre_dispatch='2*n_jobs', refit=True, score_func=None, verbose=1)
>
>
> The method causing the problem is LinearSVC.predict:
>
>
>> /opt/epd-7.2/lib/python2.7/copy_reg.py(70)_reduce_ex()
>>
> 69 if base is self.__class__:
> ---> 70 raise TypeError, "can't pickle %s objects" %
> base.__name__
> 71 state = base(self)
>
> ipdb> self
> <bound method LinearSVC.predict of LinearSVC(C=1, dual=True,
> fit_intercept=True, intercept_scaling=1, loss='l2',
> multi_class=False, penalty='l2', tol=0.0001)>
>
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