yes, trying multiple sample weightings is not supported by grid search directly.
On 23 Jun 2017 6:36 pm, "Manuel Castejón Limas" <[email protected]> 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 <[email protected]>: > >> 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" <[email protected]> >> escribió: >> >>> why are you passing [my_sample_weights] rather than just >>> my_sample_weights? >>> >>> > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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