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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