Hi Jose.
That should work.
Can you provide the full traceback?
Also can you provide weights.shape?
Andy
On 06/08/2015 08:49 PM, José Guilherme Camargo de Souza wrote:
> Hi all,
>
> I am having a different issue when trying to use sample_weights with
> RandomizedSearchCV:
>
> weights = np.array(calculate_weighting(y_train))
> search = RandomizedSearchCV(estimator, param_dist, n_iter=n_iter,
> scoring="accuracy",
> n_jobs=-1, iid=True, cv=5,
> refit=True, verbose=1, random_state=seed,
> fit_params={"sample_weight":
> weights})
>
> search.fit(X_train, y_train)
>
> where weights has the same number of instances in X_train.
> I get the following error:
>
> ValueError: need more than 1 value to unpack
>
> I am using scikit-learn 0.16.1, therefore a more recent version than
> 0.15b. Was there some sort of change in the behavior of fit_params
> from 0.15b to 0.16.1?
>
> What is the current recommended way to pass the sample_weights vector
> to a *SearchCV object, if any?
>
> Thanks!
> José
>
>
> On Tue, Jul 8, 2014 at 9:33 AM, Hamed Zamani <hamedzam...@acm.org> wrote:
>> 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; 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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