Dear Joel,
Yes. After updating the version of Scikit-learn to 0.15b2 the problem was
solved.
Thanks,
Hamed
On Tue, Jul 8, 2014 at 2:51 PM, Joel Nothman <joel.noth...@gmail.com> wrote:
> This shouldn't be the case, though it's not altogether well-documented.
> According to
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_validation.py#L1225,
> if the fit_params value has the same length as the samples, it should be
> similarly indexed.
>
> So this would be a bug ... if it is found at master. I'm guessing, Hamed,
> that you are using scikit-learn version 0.14? Please check this works with
> the latest 0.15b.
>
> However, fit_params will not account for the weights in the scoring
> function. Noel has solved this
> <https://github.com/scikit-learn/scikit-learn/pull/1574>; pending some
> more tests, this should hopefully be merged, including support for
> RandomizedSearchCV(..., sample_weight=weights_array) soon. (The work seems
> to have stalled a little. If someone wants to see this feature included
> quickly, perhaps Noel would be willing for someone else to finish this PR
> for him.)
>
> - Joel
>
>
> On 8 July 2014 07:49, Kyle Kastner <kastnerk...@gmail.com> wrote:
>
>> It looks like fit_params are passed wholesale to the classifier being fit
>> - this means the sample weights will be a different size than the fold of
>> (X, y) fed to the classifier (since the weights aren't getting KFolded...).
>> Unfortunately I do not see a way to accomodate for this currently -
>> sample_weights may be a special case where we would need to introspect the
>> fit_params and modify them before passing to the underlying classifier...
>> can you file a bug report on github?
>>
>>
>> On Tue, Jul 8, 2014 at 1:27 PM, Hamed Zamani <hamedzam...@acm.org> wrote:
>>
>>> Dear all,
>>>
>>> I am using Scikit-Learn library and I want to weight all training
>>> samples (according to unbalanced data). According to the tutorial and what
>>> I found in the web, I should use this method:
>>>
>>> search = RandomizedSearchCV(estimator, param_distributions,
>>> n_iter=args.iterations, scoring=mae_scorer,n_jobs=1, refit=True,
>>> cv=KFold(X_train.shape[0],
>>> 10, shuffle=True, random_state=args.seed), verbose=1,
>>> random_state=args.seed, fit_params={'sample_weight': weights_array})
>>>
>>> search.fit(X_trains, y_train)
>>>
>>> where "wights_array" is an array containing the weight of each training
>>> sample. After running the code, I was stopped with the following exception:
>>>
>>> ValueError: operands could not be broadcast together with shapes (1118,)
>>> (1006,) (1118,)
>>>
>>> It is worth noting that the size of "X_trains", "y_train", and
>>> "weights_array" are equal to 1118.
>>>
>>> When I changed the number of folds from 10 to 2, the exception was
>>> changed to this one:
>>>
>>> ValueError: operands could not be broadcast together with shapes (1118,)
>>> (559,) (1118,)
>>>
>>> Do you know what is the problem? I guess the problem is with "KFold"
>>> method. Any idea is appreciated.
>>>
>>> Kind Regards,
>>> Hamed
>>>
>>>
>>>
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