[scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

[Julio Antonio Soto de Vicente]

Joel is right.

In fact, you usually don't want to tune a lot the sample weights: you may leave them default, set them in order to balance classes, or fix them according to some business rule.

That said, you can always run a couple of grid searchs changing that sample weights and compare results afterwards.

--
Julio

> El 24 jun 2017, a las 15:51, Joel Nothman <joel.nothman at gmail.com> escribió:
> 
> yes, trying multiple sample weightings is not supported by grid search directly.
> 
>> On 23 Jun 2017 6:36 pm, "Manuel Castejón Limas" <manuel.castejon at gmail.com> wrote:
>> Dear Joel,
>> 
>> I tried and removed the square brackets and now it works as expected for a single sample_weight vector:
>> 
>> validator = GridSearchCV(my_Regressor,
>>                      param_grid={'number_of_hidden_neurons': range(4, 5),
>>                                  'epochs': [50],
>>                                 },
>>                      fit_params={'sample_weight':  my_sample_weights },
>>                      n_jobs=1,
>>                     )
>> validator.fit(x, y)
>> The problem now is that I want to try multiple trainings with multiple sample_weight parameters, in the following fashion:
>> 
>> validator = GridSearchCV(my_Regressor,
>>                      param_grid={'number_of_hidden_neurons': range(4, 5),
>>                                  'epochs': [50],
>>                                  'sample_weight':  [my_sample_weights, my_sample_weights**2] ,
>>                                 },
>>                      fit_params={},
>>                      n_jobs=1,
>>                     )
>> validator.fit(x, y)
>> But unfortunately it produces the same error again:
>> 
>> ValueError: Found a sample_weight array with shape (1000,) for an input with shape (666, 1). sample_weight cannot be broadcast.
>> 
>> I guess that the issue is that the sample__weight parameter was not thought to be changed during the tuning, was it?
>> 
>> 
>> Thank you all for your patience and support.
>> Best
>> Manolo
>> 
>> 
>> 
>> 
>> 2017-06-23 1:17 GMT+02:00 Manuel CASTEJÓN LIMAS <mcasl at unileon.es>:
>>> Dear Joel,
>>> I'm just passing an iterable as I would do with any other sequence of parameters to tune. In this case the list only has one element to use but in general I ought to be able to pass a collection of vectors.
>>> Anyway, I guess that that issue is not the cause of the problem.
>>> 
>>> El 23 jun. 2017 1:04 a. m., "Joel Nothman" <joel.nothman at gmail.com> escribió:
>>>> why are you passing [my_sample_weights] rather than just my_sample_weights?
>>>> 
>> 
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